Topic:"Demand Side Management" in M12186

2024 DSM Annual Progress Report FILED March 31, 2025

AI summary The 2024 DSM Annual Progress Report was filed on March 31, 2025, as part of a Nova Scotia regulatory proceeding. The document contains no further text beyond the heading and an image reference, suggesting it may be incomplete or require additional context from the image.

1. EXECUTIVE SUMMARY - EfficiencyOne ("E1") delivers demand side management ("DSM") programs and is the - administrator and operator of the Efficiency Nova Scotia ("ENS") franchise. The 2024 Annual - Progress Report ("APR") summarizes E1's...

AI summary EfficiencyOne (E1) administers the Efficiency Nova Scotia (ENS) franchise and reports on its 2024 demand side management (DSM) program results. The 2023-2025 DSM Plan, approved by the Nova Scotia Utility and Review Board (NSUARB) with a $173M investment, set four performance targets. In 2024, E1 achieved 74% progress toward the 412.7 GWh energy savings target and partial progress on other metrics.

2. 2024 PLAN AS APPROVED AND 2024 MID-COURSE TARGETS - 2024 Plan targets are 142.6 GWh of incremental annual net energy savings, 25.6 MW of annual - net peak demand savings, and 10.0 MW of available capacity, within an investment of $57.5...

AI summary The 2024 Plan targets 142.6 GWh energy savings, 25.6 MW peak demand savings, and 10.0 MW capacity with a $57.5M investment. E1 adjusted mid-course targets to 142.6 GWh, 25.7 MW, 7.2 MW, and $59.6M, aligning with market trends and evaluations.

3. 2024 PORTFOLIO RESULTS - In comparison to E1's 2024 mid-course targets, E1 achieved the following results: - 172.8 GWh of incremental annual net energy savings (121% of the mid-course adjusted target of 142.6 GWh); - 30.7 MW of annual n...

AI summary EfficiencyOne (E1) exceeded 2024 mid-course energy and demand savings targets by 121% for energy savings, 119% for peak demand, and 113% for demand response capacity. Achievements were driven by Residential and Business, Non-Profit, and Institutional sectors, with specific programs like the Mi'kmaw Home Energy Efficiency Project meeting 100% of their allocated targets.

Table 1: 2024 Results to 2024 Plan as Approved, 2024 Mid-Course Adjustments, and 2024 Year-End Forecast abic 1. 2024 Nest 1111 , Energy Efficiency (EE) Programs • Efficient Product Rebates 10.4 91.2 1.1 4.6 - 14.5 125.4 2.0 5.9 - 17.2 149....

AI summary Table 1 presents the 2024 results compared to the approved 2024 plan, mid-course adjustments, and year-end forecasts for energy efficiency programs including Efficient Product Rebates, Appliance Retirement, Instant Savings, and Existing Residential initiatives.

2024 Plan as Approved refers to the investment and savings targets as provided in the 2023-2025 DSM Resource Plan Compliance filing. See M10473, 2023-2025 DSM Resource Plan Compliance Filing, Appendix C, Schedule E, at page 41, October 4,...

AI summary The text references the 2023-2025 DSM Resource Plan Compliance filing and its approval by the NSUARB. It discusses program status, comparing 2024 results to forecasts and targets, with color-coded variances. The New Residential program ended in December 2023. Footnotes provide additional context on low-income participation and demand response verification.

1 Table 2: 2024 Participation Participation Results Unit 2023-2025 Plan as Approved 2024 Plan as Approved 2024 Mid-Course (MCA) 2024 Results % of 2024 Results to MCA % of 2024 Results to 2024 Plan as Approved % of 2024 Results to 2023-2025...

AI summary Table 2 outlines participation and results for various energy efficiency and demand-side management programs in 2024, comparing actual results to approved plans and mid-course targets. The data shows participation levels and performance metrics for initiatives such as efficient product rebates, appliance retirement, and residential and business programs.

3.3 2024 Expenditures - In 2024, E1's total expenditures were $65.7 million, $6.1 million, or 10%, above the mid-course - adjusted planned spending levels of $59.6 million, and $8.2 million, or 14%, above the 2024 Plan - as Approved spendi...

AI summary E1's 2024 expenditures of $65.7M exceeded planned spending by 10% and the 2024 Plan by 14%, driven by reinvestment of 2023's $7.8M underspend and increased spending on DSM initiatives, including a new five-year DSM Plan and a cost-effectiveness test mandated by the NSUARB. Incentive expenditures remained comparable to 2023 levels.

4. FORECAST FOR 2023-2025 DSM PLAN PERIOD E1's three-year Plan forecast provides additional insight on the DSM Plan implementation after the first two years. It includes E1's actual savings results and expenditures from 2023 and 2024, and...

AI summary E1's 2023-2025 DSM Plan forecasts $173 million in investment, aiming to meet 90% compliance on three performance targets (energy, demand savings, and affordable housing). However, challenges in the Demand Response program, including low participant engagement and paused initiatives, are expected to cause a shortfall in the 17.9 MW capacity target. E1 highlights ongoing learning and adaptation of the program as key to future improvements.

1 Table 4: 2023-2025 DSM Plan Period Forecast 2023-2025 DSM Plan Period Forecast Plan Year Energy Savings (GWh) Demand Savings (MW) Available Capacity (MW) Energy Savings applicable to Affordable Single-family Homes, Affordable Multi-famil...

AI summary Table 4 presents the forecast for the 2023-2025 DSM Plan Period, showing energy and demand savings, available capacity, and expenditures. The plan was approved by the NSUARB on November 8, 2022, with details provided in M10473.

1 4.1 2025 Forecast – Detailed program information - 2 In developing the 2025 forecast, which is the final year of 2023-2025 DSM Plan, a number of 3 assumptions were made and a variety of factors were considered, including: - 4 Following r...

AI summary E1's 2025 forecast for the final year of the 2023-2025 DSM Plan outlines expected reductions in energy savings due to the closure of the Canada Greener Homes Grant, changes in rebate structures for LED lighting, and the end of the Appliance Retirement program. Despite these challenges, E1 anticipates continued delivery of DSM programs and meeting low-income performance targets.

1 Table 5: 2025 Plan as Approved and 2025 Forecast 2025 Plan as Approved 2025 Forecast First-Year Energy Savings (GWh) Peak EE Demand Savings (MW) Investment ($ million) Available Capacity (MW) First-Year Energy Savings (GWh) Peak EE Deman...

AI summary Table 5 outlines the 2025 Plan as Approved and 2025 Forecast for energy efficiency and demand response programs in Nova Scotia. It details energy savings, investment, and capacity for various initiatives, including Efficient Product Rebates, Appliance Retirement, and Demand Response programs.

1 5. 2024 PROGRAM RESULTS - 2 This section provides an overview of 2024 results and activities for E1's Residential and BNI sector - 3 programs including: - 4 evaluation activities; - 5 program results and highlights; - 6 discussion of pro...

AI summary This section outlines E1's 2024 program results for Residential and BNI sectors, covering evaluation activities, program outcomes, variance explanations for programs deviating by +/-25% from mid-course targets, savings in low-income and underserved communities, and Enabling Strategies. Program rate class results are detailed in Attachment 1.

13 5.1 2024 Evaluation Activities 14 Evaluation activities are conducted annually by E1's independent third-party evaluation 15 consultant to ensure accurate determination of net electrical energy, net system-peak demand 16 savings, and av...

AI summary E1 conducts annual evaluations of its DSM programs via a third-party consultant, assessing energy savings and demand reductions. The 2024 reports include audit reviews for each program component except Residential Behaviour, which uses random sampling. Impact evaluations are either condensed or comprehensive, using prior year data where applicable.

5.2 Residential Sector Results - The Residential sector consists of the following programs: - Efficient Product Rebates; and - Existing Residential. - (Note: The New Residential program, featuring the New Home Construction program componen...

AI summary The Residential sector in Nova Scotia achieved 77.9 GWh energy savings and 17.0 MW peak demand savings in 2024, exceeding mid-course adjusted targets. Programs include Efficient Product Rebates and Existing Residential, with the New Residential program ending in 2023. E1 provided variance explanations for programs deviating by +/-25% from targets, referencing its 2023-2025 DSM Plan.

Instant Savings Highlights - Instant Savings' two major campaigns (spring and fall) saw high uptake and very strong results in terms of unit sales and energy and demands savings achieved, compared to previous years. This was driven primari...

AI summary Instant Savings' spring and fall campaigns in 2024 achieved high uptake and strong energy/demand savings due to 70-80% rebates and targeted marketing for LED products. Digital campaigns also contributed to consistent savings. The Evaluator's survey indicated lower freeridership than previous years.

3 Table 7: 2024 Existing Residential EXISTING RESIDENTIAL (2024) Existing Residential Energy Savings (GWh) Demand Savings (MW) Expenditure ($ million) 2024 Results 53.3 14.2 23.1 2024 MCA Target 46.5 11.5 19.7 • Existing Residential exceed...

AI summary The 2024 Existing Residential program exceeded mid-course adjusted energy (53.3 GWh vs. 46.5 GWh) and demand (14.2 MW vs. 11.5 MW) savings targets, driven by increased participation in the Canada Greener Homes Grant (co-delivered by E1 and Natural Resources Canada). Expenditure of $23.1M exceeded the $19.7M target due to higher Affordable Single-family Homes participation.

Program Components - The Affordable Multi-family Housing and Non-Profit Organizations program component assists nonprofit organizations (e.g., shelters, transition houses, community hubs etc.) and affordable housing owners with financial i...

AI summary The Program Components section outlines Nova Scotia's energy efficiency initiatives, including financial incentives for retrofits, free installations, and assessments targeting multi-family housing, single-family homes, and Mi'kmaw communities. Programs like HomeWarming and Green Heat aim to reduce electricity consumption through upgrades and rebates, while E1 and ENS manage implementations with community partners.

Efficient Product Installation Highlights - Energy and demand savings in Efficient Product Installation in 2024 were consistent with 2023, but fell short of mid-course adjusted targets due to increasing measure saturation. With fewer measu...

AI summary Energy and demand savings from Efficient Product Installation in 2024 remained consistent with 2023 but missed mid-course adjusted targets due to measure saturation. Lower installation rates for smart thermostats and LED lamps, along with reduced average savings per household, contributed to this shortfall. Electrician-installed measures and new marketing campaigns are expected to improve outcomes in 2025.

Home Energy Assessment Highlights - Energy and demand savings remained very strong in 2024, as Canada Greener Homes Grant (which E1 co-delivers with Natural Resources Canada and provides a top up to DSM incentives, through the Home Energy...

AI summary Energy and demand savings remained strong in 2024 despite the Canada Greener Homes Grant closing in February. Savings were impacted by a billing analysis adjusting HOT2000 modelling software overestimation ratios. Post-grant closure, demand for Home Energy Assessments declined, though a marketing campaign was launched in H2 2024.

1 5.3 Business, Non-Profit and Institutional (BNI) Sector Results - 2 The BNI sector is comprised of the following programs: - 3 Efficient Product Rebates; - 4 Custom Incentives; and - 5 Direct Installation. 6 10 7 In 2024, the BNI sector...

AI summary The BNI sector achieved 94.8 GWh energy savings and 13.7 MW peak demand savings in 2024, exceeding mid-course adjusted targets. E1's Energy Manager initiative supported large organizations, with 33 energy manager positions, six partially funded by DSM. Variance explanations were provided for Custom Incentives programs deviating by +/-25% from targets.

Business Energy Rebates Highlights - Business Energy Rebates, including both the Instant Rebates and Application Rebates services, fell slightly short of its 2024 mid-course adjusted energy savings target and achieved its mid-course adjust...

AI summary Business Energy Rebates under Efficiency Nova Scotia (ENS) missed 2024 mid-course adjusted energy savings targets but met demand targets. Instant Rebates saw lower 2024 participation due to reduced LED lamp sales, though rebate increases in Q2 and promotional campaigns were implemented. 2024 savings aligned with 2023 results.

1 5.3.2 Custom Incentives - 2 The Custom Incentives program consists of the following two program components: - 3 Custom; and - 4 Strategic Energy Management. 5

AI summary The Custom Incentives program comprises two components: 'Custom' and 'Strategic Energy Management.' This section outlines the structure of the program under Efficiency Nova Scotia (ENS) within the Nova Scotia Utility and Review Board (NSUARB) proceeding.

6 Table 9: 2024 Custom Incentives CUSTOM INCENTIVES (2024) Custom Incentives Energy Savings (GWh) Demand Savings (MW) Expenditure ($ million) 2024 Results 44.4 5.8 9.2 2024 MCA Target 31.3 4.6 8.6 • Custom Incentives exceeded its mid-cours...

AI summary Custom Incentives exceeded 2024 mid-course adjusted energy and demand savings targets, driven by Retrofit and New Construction services. Compressed air leak audits in Retrofit contributed significantly to savings.

Program Components / Offerings - The Custom program component provides large business, non-profit and institutional participants with technical assistance and financial incentives to help reduce electricity consumption and demand and inclu...

AI summary The Custom program component offers technical assistance and financial incentives to large BNI participants through services like Retrofit and Pay-for-Performance. Strategic Energy Management focuses on operational changes to reduce energy use, with E1 merging Energy Management Information Systems into this program as per the 2023-2025 DSM Plan. The goal is to enhance long-term energy performance through business practice changes.

Custom Highlights - Custom exceeded its 2024 mid-course adjusted energy and demand savings targets, as the Retrofit, New Construction, and Building Optimization services all achieved higher savings in 2024 than 2023. Several partial saving...

AI summary Custom exceeded its 2024 energy and demand savings targets through higher savings from Retrofit, New Construction, and Building Optimization services, and partial savings claims. Despite lower Retrofit participation, average savings per project increased. E1 focused on compressed air opportunities in Retrofit.

Strategic Energy Management (SEM) - Strategic Energy Management supported 12 participants in 2024; nine continuing participants and three new participants. Seven of the 12 participants achieved savings in 2024.[19](#page-35-2) - Performanc...

AI summary Strategic Energy Management (SEM), part of Demand Side Management (DSM) programs, supported 12 participants in 2024, with seven achieving energy savings. Performance-based incentives of $0.04/kWh were offered, resulting in seven customers receiving rewards. E1's evaluation reports detail these outcomes.

CUSTOM INCENTIVES (2024) - Strategic Energy Management customers enrolled in the Large Industrial initiative[20](#page-36-2) received an additional $0.02/kWh, added on to the performance-based incentive base rate of $0.04/kWh, for a total...

AI summary In 2024, Strategic Energy Management customers in the Large Industrial initiative received an additional incentive of $0.02/kWh, increasing their total performance-based incentive to $0.06/kWh. Procurement for a new Strategic Energy Management service provider agreement began in Q4 2024 and is expected to be finalized by Q2 2025.

2 5.4 Demand Response Program Results - 3 E1 launched its Demand Response program in 2023, an initiative outlined in the 2023-2025 Plan - 4 with a Performance Target of 17.9 MW of available capacity. It is delivered through the - 5 Residen...

AI summary E1 launched a Demand Response program in 2023 with a 17.9 MW capacity target, delivered through Residential and BNI sectors. The 2023/2024 season included Commercial & Industrial Aggregator results and the Eco Shift pilot (smart thermostats, EV telematics, and chargers). Table 11 highlights 2024 program results.

1 Table 11: 2024 Demand Response DEMAND RESPONSE (2024) Demand Response Available Demand Response Capacity (MW) Expenditure ($ million) 2024 Results 8.1 3.3 2024 MCA Target 7.2 3.8 • Results reflect available capacity achieved during the p...

AI summary Table 11 shows 2024 Demand Response results, with E1 achieving 8.1 MW available capacity (exceeding its 7.2 MW mid-course target) and spending $3.3 million (below the $3.8 million target). The operational period was December 1, 2023, to February 29, 2024.

Demand Response Residential Highlights - The Residential Demand Response program component achieved 0.1 MW of available capacity for the 2023/2024 season, through the Eco Shift pilot. Two eligible devices (smart thermostats, EV telematics...

AI summary The Residential Demand Response program achieved 0.1 MW of available capacity in 2023/2024, below the 0.2 MW mid-course adjustment. Key factors include preheating implementation issues and E1 finalizing its capacity methodology. The pilot involved 353 participants with 1,257 smart thermostats and 50 EV devices, with 10 demand response events triggered by NS Power.

DEMAND RESPONSE (2024) - o Not all registered devices participated in each event as registration was ongoing throughout the season, participants could opt out of any event, and device connectivity issues meant that not all enrolled partici...

AI summary The 2023/2024 Demand Response season faced challenges with device participation due to ongoing registration, opt-outs, and connectivity issues. Post-season efforts focused on recruiting for the 2024/2025 season, including expanding eligible devices, installing smart thermostats, and resuming domestic hot water controller installations after a 2023 pause due to quality concerns. 803 controllers were installed in 2024.

Demand Response BNI Highlights - The BNI Demand Response achieved 8.0 MW of available capacity for the 2023/2024 season through the Commercial and Industrial Aggregator, higher than the 7.0 MW mid-course adjusted target. There were 76 part...

AI summary The BNI Demand Response program achieved 8.0 MW of available capacity in 2023/2024, exceeding the 7.0 MW target, with 76 participants and 93 NS Power accounts. Capacity per participant dropped from 263 kW to 106 kW due to increased participation. Focus shifted to recruiting smaller participants and testing commercial-scale batteries for peak load reduction in the 2024/2025 season.

5.5.1 Performance Target In the 2023-2025 approved Plan, the NSUARB established a Performance Target of 15.8 GWh for cumulative annual energy savings applicable to Affordable Single-family Homes, Affordable Multi-family Housing, and Mi'kma...

AI summary The NSUARB set a 15.8 GWh energy savings target for E1's 2023-2025 DSM Plan. E1 met the 2024 mid-course target (5.3 GWh) through Affordable Single-family Homes, though Multi-family and Mi'kmaw projects fell short due to lower applications and software overestimation. E1 aims to achieve 90% of the overall target in the final year.

6 202 4 Plan as Ap proved 20 2024 Mid-Course Adjustment 2024 Forec ast (Year-Er nd) 2024 Results Program Components First-Year Energy Savings (GWh) Lifetime Energy Savings (GWh) Peak Demand Savings (MW) Investment ($ million) Participation...

AI summary The table presents energy savings and investment data for various energy efficiency programs in Nova Scotia, including Affordable Multi-family Housing, Affordable Single-family Homes, and the Mi'kmaw Home Energy Efficiency Project. The data includes first-year and lifetime energy savings, peak demand savings, investment amounts, and participation numbers for the 2024 Plan as Approved.

1 Table 13: 2023 Results Applicable to Low-Income and Underserved Communities, Non-Targeted Programs 2024 Plan Performance Indicators 2024 Mid-Course Adjustments 2024 Forecast (Year-End) 2024 Results First-Year Energy Savings (GWh) Lifetim...

AI summary Table 13 presents 2023 results for low-income and underserved communities under non-targeted programs, including energy savings, participation numbers, and investment figures. It outlines performance indicators for 2024, mid-course adjustments, forecasts, and actual results, with data on efficient product rebates and appliance retirement initiatives.

1 5.6 Enabling Strategies - 2 The Enabling Strategies component in the 2023-2025 DSM Plan focuses on the following three - 3 categories: - 4 Education and Outreach; - 5 Development and Research; and - 6 Other Enabling Strategies. 7 8 [Tabl...

AI summary The Enabling Strategies component of the 2023-2025 DSM Plan focuses on three categories: Education and Outreach, Development and Research, and Other Enabling Strategies. Table 14 highlights E1's 2024 Enabling Strategy activities.

ENABLING STRATEGIES 2024 ACTIVITY HIGHLIGHTS

AI summary This section from a Nova Scotia regulatory proceeding highlights Enabling Strategies 2024 activities, focusing on energy efficiency programs and collaborations involving Efficiency Nova Scotia, EfficiencyOne, and the Mi'kmaw Home Energy Efficiency Project, under the oversight of the Nova Scotia Utility and Review Board.

Development and Research Development and Research activities in 2024 included: - quarterly tracking of quality assurance, participant satisfaction, and other attitudinal metrics among Nova Scotia households to help E1 respond to changing m...

AI summary In 2024, Development and Research activities focused on tracking household metrics for E1, conducting studies to enhance program offerings, and completing surveys including Demand Response Eco Shift participants and Efficiency Preferred Partner Network members. Projects included finalizing a Small Business Energy Solutions portal prototype and evaluating events and training programs.

Regulatory Affairs E1 filed the following reports with the NSUARB: - 2023 DSM Evaluation Reports and 2023 Annual Progress Report, and a summary report on DSMAG consideration of these reports; - Behavioural Program Update, responses to Info...

AI summary E1 submitted multiple reports to the NSUARB, including DSM evaluations, financial statements, and quarterly DSM updates. Engagement activities involved NS Power-led technical sessions and stakeholder interactions. The filings cover 2023-2024 DSM program performance and compliance.

Other regulatory activities included: - ongoing development of the 2026-2030 DSM Plan (in February 2025 the provincial government introduced legislation to extend E1's current DSM Plan by one year to include 2026. Assuming the legislation...

AI summary Regulatory activities include development of the 2026-2030 DSM Plan, pending legislation to extend E1's DSM Plan, 2024 DSM evaluation, NS Power project participation, and information requests on load forecasts. Key entities involve E1, provincial government, and NS Power.

5 5.8 Incentive Reporting - 6 In the NSUARB's Decision on the 2023-2025 DSM Plan, E1 was directed to "identify any instances - 7 where E1 has adjusted the per unit incentive amount for a measure by more than 10% from the - 8 amount include...

AI summary The NSUARB required E1 to report on incentive adjustments exceeding 10% from the 2023-2025 DSM Plan. E1 identified a variance in the Efficient Product Rebates program's Instant Savings component during Q4.

14 Residential Sector Programs

AI summary The section outlines residential sector programs under Nova Scotia's regulatory proceeding, referencing key acronyms like DSM, E1, ENS, and NSUARB. It highlights initiatives such as the Mi'kmaw Home Energy Efficiency Project (MHEEP) and mentions regulatory oversight by the NSUARB.

5.9 Avoided CO2e emissions - On a proactive basis, E1 provides the annual net avoided CO2e emissions from DSM measures - installed during each Plan year. Using the most recent NS Power emissions intensity data, E1's - Evaluation Consultant...

AI summary E1 reports annual net avoided CO2e emissions from DSM measures, with 81,327 tonnes estimated for 2024 using NS Power's emissions intensity data. The Evaluation Consultant identified this figure based on recent data.

6. CONCLUSION - In 2024, E1 achieved 172.8 GWh of incremental annual net energy savings (121% of the mid- - course adjusted target of 142.6 GWh), 30.7 MW of annual net peak demand savings (119% of the - mid-course adjusted target of 25.7 M...

AI summary E1 exceeded 2024 DSM targets for energy and demand savings but expects to miss the available capacity target. They plan to spend $173 million under their 2023-2025 plan and will file a quarterly report in May 2025.

ATTACHMENT 1: 2024 RATE CLASS RESULTS

AI summary Attachment 1 presents the 2024 rate class results from a Nova Scotia regulatory proceeding, involving entities such as Efficiency Nova Scotia (ENS), EfficiencyOne (E1), and the Nova Scotia Utility and Review Board (NSUARB). Key programs include the Mi'kmaw Home Energy Efficiency Project (MHEEP) and Demand Side Management (DSM).

Rate Class Expenditures - E1 reports on planned and actual DSM expenditures by rate class to aid in cost recovery - allocations.[32](#page-51-0) - In an effort to provide insight into how E1 tracks and calculates its rate class results, th...

AI summary E1 reports on planned and actual Demand Side Management (DSM) expenditures by rate class to support cost recovery allocations. The document outlines E1's methodology for tracking rate class results, historical investment data, 2024 results, and 2025 forecasts.

1. RATE CLASS SPENDING ALLOCATION METHODOLOGY

AI summary The document outlines the methodology for allocating spending across different rate classes within a regulatory proceeding. It involves considerations of demand-side management, efficiency programs, and regulatory oversight by Nova Scotia's utility board.

1.1 Rate class allocation for 2023-2025 DSM Plan - Rate class investment allocations for the 2023-2025 DSM Plan were the sum of rate class - allocations calculated by program component. For each program component, the spending by - rate cl...

AI summary E1 allocates DSM Plan funds by rate class using historical spending data, with exceptions for Custom. The methodology ties to NS Power's DCR Rider applications and references a 2015 NSUARB order and the Public Utilities Act. E1 commits to refining estimates for future DSM Plans.

2. HISTORICAL RATE CLASS SPENDING - [Table 1 p](#page-53-0)rovides Plan as Approved, and Actual spending by rate class from 2016-2024. - Historically, actual spending has been closely aligned with the Plan as Approved. In recent years, - E...

AI summary Historical spending by rate class from 2016-2024 shows actual spending closely aligning with the Plan as Approved. E1 reports growth in BNI programs, particularly medium industrial participation in Demand Response. E1 commits to improving spending reporting and ensuring program equity and access for all Nova Scotians.

5 3. 2024 RESULTS BY RATE CLASS 6 2024 annual expenditures were higher than the 2024 Plan as Approved expenditures, asspending 7 increased overall. The increased investment was spread fairly evenly amongst most rate classes 8 and was fairl...

AI summary 2024 expenditures exceeded the approved plan, with increased investment across most rate classes except large general and medium industrial. Medium industrial spending rose due to higher BNI Demand Response participation (76 participants in 2024 vs. 9 in 2023), with over 50% of incentives allocated to medium industrials. Variability in spending is influenced by BNI sector project timing and participation.

5 Table 2: 2024 Planned and Actual DSM Expenditures by Rate Class 2024 Expenditures by Rate Class Rate Class 2024 Plan as Approved ($ million) 2024 Mid-Course Planned Expenditures ($ million) Actual 2024 Expenditures ($million) 2024 Expend...

AI summary Table 2 presents the 2024 planned and actual Demand Side Management (DSM) expenditures by rate class, showing that actual expenditures exceeded planned amounts in most categories, with residential and general rate classes showing the highest percentages of achievement. The data is based on the 2023-2025 DSM Resource Plan Compliance filing approved by the NSUARB in November 2022.

9 Table 4: 2024 Existing Residential Rate Class Results Existing Residential (2024) First-Year Energy Savings (GWh) Lifetime Energy Savings (GWh) Peak Demand Savings (MW) Expenditures ($ million) Housing Units / Products (#) Residential/Ch...

AI summary Table 4 presents 2024 residential rate class results, showing energy and demand savings across various categories, along with expenditures and the number of housing units or products. Savings are calculated net of free-ridership and spillover, and expenditures are unaudited.

13 Existing Residential includes the following program components: Affordable Multi-family Housing and Non-Profit Organizations, Efficient Product 14 Installation, Green Heat, Home Energy Assessment, Affordable Single-family Homes, Mi'kmaw...

AI summary The Existing Residential program includes several components such as Affordable Multi-family Housing, Efficient Product Installation, Green Heat, and the Mi'kmaw Home Energy Efficiency Project. The Home Energy Assessment component primarily targets homes heated by electricity, with some non-electric homes also participating. The Appliance Retirement program collects eligible appliances from BNI customers and residential participants.

1 Table 5: 2024 BNI Efficient Product Rebates Rate Class Results BNI Efficient Product Rebates (2024) First-Year Energy Savings (GWh) Lifetime Energy Savings (GWh) Peak Demand Savings (MW) Expenditures ($ million) Units Rebated (#) Residen...

AI summary Table 5 presents the 2024 BNI Efficient Product Rebates rate class results, showing energy and demand savings, expenditures, and the number of rebated units across various categories, including residential, industrial, and municipal. The data highlights the impact of the rebate program on energy efficiency and cost management.

13 Custom Incentives includes the Custom program component and the Strategic Energy Management program component. Strategic Energy 14 Management participants are each counted once, but their participation is ongoing throughout the year. 15...

AI summary The text describes the Custom Incentives program, which includes both the Custom program component and the Strategic Energy Management program component. Strategic Energy Management participants are counted once but remain active throughout the year, and the Custom Incentives program includes certain residential customers such as farms and rental buildings.

1 Table 7: 2024 Direct Installation Rate Class Results Direct Installation (2024) First-Year Energy Savings (GWh) Lifetime Energy Savings (GWh) Peak Demand Savings (MW) Expenditures ($ million) Products (#) Residential/Charitable (2,3,4) 1...

AI summary Table 7 outlines the 2024 Direct Installation Rate Class Results, showing energy and demand savings across various rate classes, along with expenditures and the number of products installed. Notably, some rate classes show no savings or expenditures, and the data includes footnotes about rounding and unaudited figures.

- 5 Direct Installation includes the Small Business Energy Solutions program component. - 7 [Table 8](#page-57-0) provides a breakdown of available capacity, expenditures, and participation achieved, by - 8 rate class within the Demand Res...

AI summary The text mentions that Direct Installation includes the Small Business Energy Solutions program component and references a table that provides a breakdown of available capacity, expenditures, and participation within the Demand Response program by rate class.

1 4. 2025 FORECAST AND 2023-2025 DSM PLAN RESULTS & FORECAST BY RATE CLASS 2 The 2025 forecast rate class allocation has been developed as described in Sectio[n](#page-52-0) 1.2, using 2022-2024 rate class spending data. 4 [Table 9 p](#pag...

AI summary The document outlines the 2025 forecast for rate class allocation using 2022-2024 spending data. Table 9 details 2023-2025 DSM plan expenditures, actuals, and variances, incorporating 2023-2024 results and 2025 forecasts. It emphasizes the Plan outlook based on historical and projected data.

8 Table 9: 2023-2025 DSM Plan Period Results and Forecast by Rate Class 2023 2024 2025 2023-2025 Plan Rate Class Plan 2023 Actual 2023 Variance (Plan to Actual 2023) Plan 2024 Actual 2024 Variance (Plan to Actual 2024) Plan 2025 Forecast 2...

AI summary Table 9 presents the 2023-2025 DSM Plan Period Results and Forecast by Rate Class, showing variances between planned and actual/forecast expenditures. The NSUARB approved the 2023-2025 DSM Plan on November 8, 2022, as outlined in M10473, referencing the 2023-2025 DSM Resource Plan Compliance Filing.

Table 1 Update on Implementation of 2018-2022 Evaluation Recommendations Year Evaluation/ Verification Recommendation Text Source Status Comments Expected Period of Completion 2022 Evaluation For any future Retrofit indoor horticultural li...

AI summary In 2022, an evaluation recommended that EfficiencyOne (E1) use the IPMVP Option C approach for M&V in future indoor horticultural lighting projects. E1 agrees and is currently exploring methods, including a trial project informed by the Efficiency Maine 2024 Technical Reference Manual. M&V efforts are expected to continue through 2025.

2024 DSM PROGRAMS EVALUATION REPORTS Final DSM Reports March 27, 2025 In Collaboration with:

AI summary The document announces the submission of final 2024 DSM Programs Evaluation Reports on March 27, 2025, with collaboration indicated through visual logos (image placeholders). No detailed content or findings from the evaluations are provided in the text.

2024 DSM PROGRAMS EVALUATION Final Report

AI summary Final evaluation report assessing the effectiveness of 2024 Demand Side Management (DSM) programs, focusing on program performance, cost recovery, and alignment with regulatory objectives.

DEFINITIONS Accuracy Reflects the proximity of measurements to the true value. Adjustment ratio The ratio of evaluated results to tracked results. This ratio expresses the adjustment made to tracked savings or other tracked values such as...

AI summary This section defines key terms used in the regulatory proceeding, including accuracy, adjustment ratio, available demand response capacity, and baseline. These definitions are critical for understanding how savings and performance metrics are measured and evaluated in energy efficiency programs.

EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (DSM) for Nova...

AI summary EfficiencyOne (E1) is an independent non-profit that delivers energy efficiency and demand response programs in Nova Scotia through the Efficiency Nova Scotia franchise. An evaluation of E1's 2024 DSM program portfolio confirmed significant energy and GHG savings, with 172.760 GWh in net electrical energy savings and 81,577 tonnes of CO2 eq in avoided GHG emissions.

1 Evaluation Scopes and Objectives The 2024 Portfolio Evaluation Plan was based on the Evaluation Schedule outlined in the Overall Strategic Evaluation Plan[4](#page-10-1) that provides the framework and approach to guide evaluation planni...

AI summary The 2024 Portfolio Evaluation Plan outlines the approach for evaluating demand-side management (DSM) activities from 2023-2025. It emphasizes prioritizing evaluations based on program savings, uncertainty, changes in design, regulatory requirements, and timely feedback. The plan includes impact, process, and market evaluations, with a focus on comprehensive or condensed impact evaluations.

Table 1: 2024 Portfolio Evaluation Plan PY2024 Program Components Impact Process Market Residential DSM Program Components Appliance Retirement Condensed Instant Savings Comprehensive Affordable Multifamily Housing Comprehensive X Affordab...

AI summary Table 1 outlines the 2024 Portfolio Evaluation Plan for various program components under residential and BNI DSM programs. It details the impact, process, and market aspects of each program, including comprehensive and condensed evaluations for initiatives such as Appliance Retirement, Instant Savings, and Business Energy Rebates.

1.1 Impact Evaluation Objectives and Scope The impact evaluation activities were aimed at determining: - › Gross electrical energy and peak demand savings at the meter and at the generator - › Available demand response (DR) capacity for de...

AI summary The impact evaluation objectives include assessing energy and peak demand savings, demand response capacity, net-to-gross ratios (NTGRs), effective useful life (EUL) of measures, and GHG emissions. Two evaluation types are outlined: comprehensive (reviewing baseline definitions, methodologies, parameters, and NTGRs) and condensed (reusing prior-year parameters). Evaluations occur every three years for most programs, with factors like program maturity and calculation complexity influencing the approach.

Demand-side Management Measure Assessment Document The impact evaluation scope for 2024 also included an update of the Demand-side Management Measure Assessment (DSM MA) document. This update covered all prescriptive measures and their par...

AI summary The 2024 update of the Demand-side Management Measure Assessment (DSM MA) includes prescriptive and semi-prescriptive measures, reviews of parameters like unitary energy savings and peak demand-to-energy ratios, and annual updates for LED lighting and appliance retirements, using new data sources and methodologies.

Net-to-gross Ratio Review The evaluation scope included a NTGR review that served to update the NTGR questionnaires and algorithms for the DSM portfolio so that they were consistent across programs and in line with the latest best practice...

AI summary The NTGR review updated algorithms and questionnaires for E1's DSM programs to align with best practices, focusing on self-report methods. It covered rule alignment, weighting, and survey formulation, applicable to 2024 programs and the 2023-2025 DSM Plan period, excluding Instant Savings due to prior data collection.

2 Evaluation Methodology This section presents the methodologies used and the activities carried out to evaluate E1 DSM program components and services through impact, process, and market evaluations.

AI summary This section outlines the methodologies and activities used to evaluate E1 DSM program components and services through impact, process, and market evaluations, focusing on assessing program effectiveness and outcomes.

Table 3: 2024 Interviews Completed Program Component Program Manager/1 E1 Staff/Business Development Manager Service Providers/ Distributors/ Contractors Participants Non participants Dropped-out or Overdue Participants Residential Applian...

AI summary Table 3 presents the number of interviews conducted in 2024 across various program components, including residential and business energy programs. It includes data on participants, non-participants, and dropped-out or overdue participants, with the exception of Appliance Retirement, which had only one round of interviews.

Site Visits and Project Reviews with Follow-up Site Visits or Interviews The Evaluator performed a total of 146 project reviews during the summer and fall of 2024, 70 of which were complemented through site visits and 14 were complemented...

AI summary The Evaluator conducted 146 project reviews in 2024, including site visits and phone interviews, to validate installations and gather information on free-ridership and spillover effects for various programs such as Efficient Product Installation, Affordable Multifamily Housing, and Strategic Energy Management.

Table 4: 2024 Site Visits and Project Reviews with Follow-up Site Visits or Interviews Program Component Project Reviews Followed by Site Visits Project Reviews Followed by Phone Interviews Project Reviews Without Site Visits or Phone Inte...

AI summary Table 4 outlines the 2024 project reviews and site visits conducted under various programs, including Affordable Multifamily Housing, Efficient Product Installation, and BNI. It details the distribution of project reviews followed by site visits, phone interviews, or neither, with a note on the exclusion of a large-scale compressed air leak audit from adjustment ratio calculations.

Unitary Savings Review As part of the update to the 2024-2025 DSM MA that was reviewed in its entirety in 2024, for program components with unitary savings values - namely Appliance Retirement, Instant Savings, Home Energy Assessment, Gree...

AI summary The 2024-2025 DSM MA update reviewed unitary savings values for programs like Appliance Retirement, Home Energy Assessments, and Business Energy Rebates. Parameters were validated and updated, with new measures added, including advanced thermostats and solar PV systems. Review methods included literature reviews, metering studies, and energy models.

Billing Analyses In 2024, three billing analyses were performed for Green Heat, Home Energy Assessment, and Residential Behaviour. The Green Heat billing analysis was conducted to obtain measured electrical energy savings generated through...

AI summary In 2024, three billing analyses were conducted for Green Heat, Home Energy Assessment, and Residential Behaviour. The analyses aimed to measure energy savings from MSHP installations, establish adjustment ratios for HOT2000 estimates, and calculate net savings using a difference-in-difference approach. Participants who modified their homes during participation were excluded to ensure accurate savings calculations.

2.1.4 Effective Useful Life Review As part of the DSM MA update in 2024, the Evaluator reviewed the EUL values for existing and new measures to ensure they were valid and revised them where appropriate. The EUL update was based on a litera...

AI summary The 2024 DSM MA update reviewed and revised EUL values for energy measures using technical references from other jurisdictions. Adjusted EUL calculations considered baseline evolution and lifetime savings, applied to programs like Affordable Single-family Homes and Mi'kmaw Home Energy Efficiency Project, with updated metrics for multifamily housing heat pumps.

Table 5: Comparison of 2024 Evaluated and Tracked Energy Savings at the Generator a Tracked Results Evaluated Results Program Component Annual Gross Savings (GWh) Annual Net Savings (GWh) Annual Gross Savings (GWh) NTGR b Annual Net Saving...

AI summary Table 5 compares the 2024 evaluated and tracked energy savings for various programs in Nova Scotia. It highlights discrepancies between gross and net savings, as well as differences in net savings and net realization rates for each program component, including Appliance Retirement, Instant Savings, Affordable Multifamily Housing, and others.

Table 6: Comparison of 2024 Evaluated and Tracked Peak Demand Savings at the Generator a Program Component Tracked Results Evaluated Results Difference DSM Program Annual Gross Savings (MW) Annual Net Savings (MW) Available Capacity (MW) A...

AI summary Table 6 compares the evaluated and tracked peak demand savings at the generator for various programs in 2024. It shows results for residential, BNI, and demand response programs, highlighting differences in net savings and available capacity, along with net realization rates for each component.

3.1.1 Residential DSM Programs

AI summary The section outlines residential demand-side management (DSM) programs aimed at enhancing energy efficiency in residential sectors through initiatives like appliance retirement, home energy assessments, and incentives for efficient technologies such as heat pumps and LED lighting.

Efficient Product Installation - › Compared to 2023 levels, participation in 2024 increased by 2% while average electrical energy savings per participant decreased by 6.3%. This reduction in savings per participant was mainly due to the up...

AI summary Efficient Product Installation (EPI) participation rose 2% in 2024 compared to 2023, but average electrical energy savings per participant fell 6.3% due to 2024-2025 DSM MA updates, particularly reduced smart thermostat and LED lamp savings. Evaluated net electrical energy savings were 6% lower than tracked, while peak demand savings increased 1% due to DSM MA updates and NTGR adjustments.

Home Energy Assessment - › With 5,367 projects, the 2024 participation level was the highest observed since the program component was launched. The average gross electrical energy savings per home slightly decreased compared to 2023 levels...

AI summary The 2024 Home Energy Assessment program achieved record participation with 5,367 projects, driven by the Canada Greener Homes Grant. Energy savings slightly decreased from 2023 but remained high. Updated adjustment ratios from a 2024 billing analysis revealed higher savings than E1's tracked results due to DSM MA updates.

Residential Behaviour › E1 did not track savings for Residential Behaviour, which relies on a random selection of treatment group of participants among all residential customers in 2024. Therefore, the program component does not have a tra...

AI summary E1 did not track savings for the Residential Behaviour program due to a random selection of participants in 2024, resulting in no tracking sheet or realization rate for the program component.

3.1.2 Business, Non-profit, and Institutional DSM Programs

AI summary The section outlines Demand-Side Management (DSM) programs targeting businesses, non-profits, and institutions in Nova Scotia, including initiatives like Business Energy Rebates (BER) and Efficiency Nova Scotia (ENS) programs.

Custom Incentives In 2024, Custom Incentives achieved 44.429 GWh in net electrical energy savings and 5.793 MW in net peak demand savings at the generator through its two components, namely Custom and Strategic Energy Management. Custom co...

AI summary In 2024, the Custom Incentives program achieved 44.429 GWh in net electrical energy savings and 5.793 MW in peak demand savings through its Custom and Strategic Energy Management components. Custom includes Retrofit, Pay-for-Performance, New Construction, and Building Optimization services.

Custom - › Compared to 2023, Custom participation decreased in 2024. While Retrofit and Building Optimization participation levels decreased, New Construction participation significantly increased in 2024. - › Following the project reviews...

AI summary Custom program participation decreased in 2024, with Retrofit and Building Optimization participation declining while New Construction increased. The Evaluator adjusted energy and peak demand savings estimates, noting reduced free-ridership (15% for Retrofit, 28% for New Construction) and no spillover. Evaluated savings were 8% and 5% higher than E1's tracked figures.

Strategic Energy Management - › The SEM participation level has remained stable over the past five years. Electrical energy savings per participant were similar to those in 2023. - › The measurement and verification (M&V) methodologies app...

AI summary Strategic Energy Management (SEM) participation has remained stable over five years, with energy savings per participant consistent with 2023 levels. Measurement and verification (M&V) methods were deemed accurate, and peak demand savings estimates improved significantly. Evaluated net energy and peak demand savings aligned closely with initial E1 tracking.

3.1.3 Demand Response DSM Programs

AI summary The section outlines Nova Scotia's Demand Response Demand-Side Management (DR DSM) programs, focusing on initiatives to manage energy demand through incentives, rebates, and efficiency measures. Key stakeholders include Efficiency Nova Scotia (ENS) and Nova Scotia Power (NSP), with emphasis on programs like Business Energy Rebates (BER) and the Oil to Heat Pump Program (OHPA).

Demand Response Demand Response is comprised of two program components, Residential Demand Response, and Business, Non-profit, and Institutional Demand Response. In 2024, Demand Response achieved 8.091 MW in available demand response capac...

AI summary Demand Response includes residential and business/non-profit/institutional components, achieving 8.091 MW in available capacity in 2024. Program results are detailed in the text.

Residential Demand Response › Tracked results were not fully compiled, as multiple methodologies for establishing the available DR capacity of the measures included in 2024 were being considered. The Evaluator decided on the final methodol...

AI summary The 2024 evaluation of residential demand response (DR) programs faced delays due to unresolved methodologies for measuring DR capacity. The Evaluator finalized the methodology as part of the 2024 evaluation, resulting in the absence of tracked results or realization rates for the program component.

The Evaluator reviewed the EUL values of all measures offered by E1 and their associated lifetime electrical energy savings. The Evaluator found that the DSM portfolio generated 2,424.024 GWh in lifetime net electrical energy savings. [14]...

AI summary The Evaluator analyzed the Effective Useful Life (EUL) values and lifetime electrical energy savings of measures in the E1 DSM portfolio, finding that insulation-related measures contribute significantly to long-term savings, while lighting measures have shorter EULs and lower lifetime savings relative to their annual contributions.

Table 8: 2024 Evaluated Net Lifetime Electrical Energy Savings at the Generator DSM Program Program Component Annual Net Energy Savings (GWh) Lifetime Net Energy Savings (GWh) Weighted Average EUL (years) Share of Annual Net Energy Savings...

AI summary Table 8 outlines the 2024 evaluated net lifetime electrical energy savings by DSM program and component, highlighting the significant contributions from residential and BNI programs. The Home Energy Assessment program contributes the highest share of both annual and lifetime savings, while the Demand Response program is excluded due to no electrical energy savings.

3.4 Greenhouse Gas Emission Reductions The Evaluator established the reduced GHG emissions due to the DSM portfolio at 81,327 tonnes of CO2 eq in terms of annually avoided net GHG emissions. [Table](#page-33-1) 9 below presents the GHG emi...

AI summary The Evaluator calculated that the DSM portfolio reduced GHG emissions by 81,327 tonnes of CO2 eq annually. Table 9 provides details on the GHG emission reductions for each program component and the overall portfolio in 2024.

Table 9: 2024 Evaluated GHG Emission Reductions DSM Program Program Component Gross Annual Avoided GHG Emissions in CO2 eq Tonnes Net Annual Avoided GHG Emissions in CO2 eq Tonnes Residential Efficient Appliance Retirement 1,981 1,117 Prod...

AI summary Table 9 presents evaluated greenhouse gas (GHG) emission reductions for 2024 under various demand-side management (DSM) programs in Nova Scotia. The table includes both gross and net annual avoided emissions in CO2 equivalent tonnes for different program components, such as appliance retirement, product rebates, and energy assessments.

4 DSM Portfolio Performance This section presents a comparison of evaluated savings with E1 planned savings at the program and component levels. It also presents satisfaction results, annual savings performance, as well as the historical p...

AI summary This section compares evaluated savings with E1 planned savings at program and component levels, presenting satisfaction results, annual savings performance, and historical contributions of individual program components to overall portfolio savings.

4.1 Participant and Partner Satisfaction The 2024 evaluation revealed high participant satisfaction with E1 and its programs. Affordable Multifamily Housing, Affordable Single-family Homes, Efficient Product Installation, Custom (New Const...

AI summary The 2024 evaluation showed high participant satisfaction with E1 and its programs, with scores of 7.9 or higher for several initiatives. Program partners were generally satisfied but less so than participants. A table presents the average satisfaction scores for program components on a 0-to-10 scale.

Table 10: 2024 Satisfaction Results DSM Program Program Component Participant Satisfaction Partner Satisfaction Residential Existing Residential Affordable Multifamily Housing 9.7 - Affordable Single-family Home 9.4 (Heat Pump) 8.9 (Modell...

AI summary Table 10 presents 2024 satisfaction results for various DSM programs, including residential and BNI components, with participant and partner satisfaction scores for different program elements such as Affordable Multifamily Housing, Efficient Product Installation, and Business Energy Rebate – Instant Rebates.

[Table](#page-35-0) 11 below presents E1 planned savings,[16](#page-34-4) evaluated results, and the variances between them. 16 E1's 2023 Q1 Demand-side Management (DSM) Report (M11148), page 3, Table 1: 2023 Approved Plan, Mid-Course Adju...

AI summary The text references Table 11, which presents E1's planned savings, evaluated results, and variances. It cites E1's 2023 Q1 DSM Report (M11148), which includes data on the 2023 Approved Plan, Mid-Course Adjustments, and Q1 Results, filed on May 25, 2023.

Table 11: 2024 Planned Net Savings and Evaluated Results Planned Savings Evaluated Results Variance Program Component and DSM Program Net Energy Savings (GWh) Net Peak Demand Savings (MW) Available DR Capacity (MW) Net Energy Savings (GWh)...

AI summary Table 11 outlines the 2024 planned net savings and evaluated results for various energy efficiency and demand response programs in Nova Scotia. It shows the performance of residential and BNI programs, highlighting variances between planned and evaluated outcomes, such as energy savings and demand reductions.

4.3 Historical Portfolio Analysis This subsection presents year-over-year program performance (GWh energy savings) and the contribution of individual program components to portfolio savings (% energy savings). [Table](#page-37-0) 12, [Tabl...

AI summary This subsection provides a historical overview of program performance, focusing on year-over-year energy savings (in GWh) and the contribution of individual program components to overall savings (% energy savings) from 2020 to 2024.

Table 12: Evaluated Net Electrical Energy Savings at the Generator, 2020-2024 Energy Savings (GWh) Energy Savings (%) DSM Program Program Component 2020 2021 2022 2023 2024 2020 2021 2022 2023 2024 Residential Residential Appliance Retirem...

AI summary Table 12 presents evaluated net electrical energy savings from various DSM programs in Nova Scotia from 2020 to 2024, highlighting contributions from residential, BNI, and overall portfolio programs, with significant energy savings percentages and GWh values reported for each category and year.

d Strategic Energy Management includes Energy Management Information System since 2022 when the two program components were merged into a single one. b Residential Behaviour was introduced in 2024. c Custom includes four services: Retrofit...

AI summary The text discusses the integration of Strategic Energy Management with Energy Management Information Systems since 2022, the introduction of Residential Behaviour in 2024, and the components of Custom services, including Retrofit, Pay-for-Performance, New Construction, and Building Optimization.

Table 13: Evaluated Net Peak Demand Savings at the Generator, 2020-2024 Peak Demand Savings (MW) Peak Demand Savings (%) DSM Program a Program Component 2020 2021 2022 2023 2024 2020 2021 2022 2023 2024 Residential Residential Efficient Ap...

AI summary Table 13 presents evaluated net peak demand savings from various Demand Side Management (DSM) programs in Nova Scotia from 2020 to 2024. It details savings by program type, including residential and BNI programs, and provides both megawatt (MW) and percentage savings for each year.

b Affordable Single-family Homes was introduced in 2024. c Custom includes four services: Retrofit, Pay-for-Performance, New Construction, and Building Optimization. d Strategic Energy Management includes Energy Management Information Syst...

AI summary The document discusses the introduction of Affordable Single-family Homes in 2024 and mentions the integration of Strategic Energy Management with Energy Management Information Systems in 2023. It also outlines the components of the Custom program, which includes Retrofit, Pay-for-Performance, New Construction, and Building Optimization.

Table 14: Evaluated Net Available DR Capacity, 2023-2024 DSM Program Program Component Available DR Capacity (MW) Available DR Capacity (%) 2023 2024 2023 2024 Demand Response (DR) Demand Response Residential Demand Response 0.058 0.057 2%...

AI summary Table 14 shows the evaluated net available demand response (DR) capacity for 2023 and 2024, with significant contributions from BNI Demand Response. In 2024, E1 achieved notable increases in net electrical energy and peak demand savings compared to 2023, driven by both residential and BNI programs.

› Record keeping/reporting is time consuming. Having to use two platforms for filling out the required record keeping/reporting forms (CIS) and uploading documentation (SharePoint) is said to lead to repetitive data entry and unclear requi...

AI summary The use of two platforms (CIS and SharePoint) for record keeping/reporting creates inefficiencies through repetitive data entry and unclear requirements for DAs and heat pump contractors. While SharePoint is functional, CIS lacks effective project management. Uncommunicated changes to processes since training have caused ongoing issues with E1.

CONCLUSIONS AND RECOMMENDATIONS Overall, 2024 evaluated net electrical energy savings and peak demand savings achieved in the E1 DSM portfolio were respectively 6.630 GWh and 3.536 MW above the values tracked by E1, while available DR capa...

AI summary The 2024 evaluation of the E1 DSM portfolio showed net electrical energy savings and peak demand savings exceeding E1's tracked values, while available DR capacity was slightly below. The Evaluator made no cross-cutting recommendations, with individual program component recommendations detailed in reports and tables.

Table 15: 2024 Recommendations on Residential Program Components No. Recommendation ASFH – R5 Ensure that DAs/contractors provide a leave-behind so that participants know who to contact in case of questions or difficulties or to better und...

AI summary The table outlines 2024 recommendations for residential program components, focusing on improving communication, administrative efficiency, and product satisfaction. Key recommendations include providing leave-behind materials, streamlining record-keeping, updating training guides, improving smart thermostat retention, and removing low-impact products from offers.

Program Components Bibliographic References CADMUS, prepared for CenterPoint Energy, 2023 Demand Response Impact Evaluation FINAL REPORT, March 2024. Retrieved from: https://midwest.centerpointenergy.com/assets/downloads/planning/irp/IRP v...

AI summary The text provides references to various demand response program evaluations conducted by different organizations, including CADMUS, Demand Side Analytics, Navigant, and EPRI, for utility companies across North America. These evaluations focus on the impact of demand response initiatives and load control programs.

Calculation of the Margin of Error The margin of error on the adjustment ratio of the EPI LED lighting free-ridership level was established by using the following formula that is the general equation linking the standard error to the margi...

AI summary The margin of error for EPI LED lighting free-ridership was calculated using a formula involving standard error (0.0193), a t-value (1.667 for 90% confidence), and a sample size (n=70 from N=5,342 participants). The result was 3.2%, applied to EPI, Instant Savings, BER Instant Rebates, Custom programs, EPI installation rates, and AMH adjustment ratios in 2024.

EfficiencyOne

AI summary The document introduces EfficiencyOne (E1), a program under Nova Scotia's energy efficiency initiatives, and lists related acronyms and terms. It outlines programs like Demand-Side Management (DSM), Affordable Multifamily Housing (AMH), and Business Energy Rebates (BER), highlighting their roles in energy conservation and affordability.

RESIDENTIAL EFFICIENT PRODUCT REBATES PROGRAM Final Report 2024 DSM EVALUATION March 25, 2025 In collaboration with:

AI summary The document presents the Final Report of the 2024 DSM Evaluation for Nova Scotia's Residential Efficient Product Rebates Program, dated March 25, 2025. It highlights collaboration with unnamed entities, though specific details about program outcomes, stakeholder input, or evaluation findings are not provided in the text snippet.

EXECUTIVE SUMMARY This report presents the 2024 demand-side management (DSM) evaluation results of the Residential Efficient Product Rebates program administered by EfficiencyOne (E1). This program is comprised of two components: (1) Appli...

AI summary This report evaluates the 2024 Residential Efficient Product Rebates program by EfficiencyOne (E1), comprising Appliance Retirement (ARet) and Instant Savings, assessing their effectiveness in demand-side management.

Participa ation Level Gross Savings NTGR Net S avings Value Unit Value Unit Value Value Unit ARet Energy Savings 5,941 Appliances 4.195 GWh 0.56 2.365 GWh Lifetime Energy Savings 16.729 GWh 0.56 9.433 GWh Peak Demand Savings 0.598 MW 0.56...

AI summary The Residential Efficient Product Rebates program exceeded its 2024 energy savings and peak demand savings targets by 70% and 41%, respectively, with Instant Savings being the primary contributor. The program achieved significant reductions in GHG emissions and energy consumption.

Program Tracked and Evaluated Savings [Table](#page-85-0) 3 below compares E1 tracked energy and peak demand savings to Evaluated savings at the generator. The realization rate and NTGR are also presented and correspond to the rounded aver...

AI summary The table compares E1 tracked energy and peak demand savings to evaluated savings at the generator, showing realization rates and NTGR values, which are calculated as the ratio of net savings to gross savings.

Table 3: Comparison of 2024 Tracked and Evaluated Savings at the Generator Gross Savings Net Savings Realization Unit NTGR Value Unit Rate Energy Savings Tracked Savings by E1 4.212 GWh 0.56 2.374 GWh 100% Evaluation Results 4.195 GWh 0.56...

AI summary Table 3 compares tracked and evaluated energy and peak demand savings for 2024, showing data from EfficiencyOne (E1) and evaluation results. Gross and net savings are presented in gigawatt-hours (GWh) and megawatts (MW), with realization rates and NTGR values provided for each category.

INTRODUCTION EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (...

AI summary EfficiencyOne (E1), a non-profit organization, manages demand-side management (DSM) programs in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. E1's 2024 DSM program portfolio includes the Residential Efficient Product Rebates program with components like Appliance Retirement and Instant Savings. Econoler was commissioned to evaluate these programs, focusing on baseline definitions, savings calculations, and net-to-gross ratios.

Table 4: Types of Evaluation Conducted for Each Program Component, 2024 2024 Program Program Component Process Market Impact Residential Efficient Product Rebates ARet Condensed Instant Savings Comprehensive For each program, the Evaluator...

AI summary Table 4 outlines the types of evaluation conducted for each program component in 2024, specifically for the Residential Efficient Product Rebates program. The Evaluator prepared a DSM evaluation report detailing findings, energy savings, peak demand savings, and GHG emissions avoidance.

1.1 ARet Description Through ARet, E1 promotes the retirement of old, inefficient household appliances such as refrigerators, freezers, room air conditioners, and dehumidifiers. ARet educates Nova Scotians about the cost of maintaining old...

AI summary ARet, managed by E1, retires inefficient household appliances in Nova Scotia, offering rebates and free pick-up services. ARCA Canada Inc. handles collection and recycling. Eligibility requires appliances to be 10+ years old, with specific size and rebate criteria. The program aims for 1.937 GWh energy savings and 0.287 MW peak demand reduction in 2024.

Unitary Savings Review As part of a major update to the 2024-2025 Demand-side Management Measure Assessment (DSM MA)[,](#page-90-2) 4 a unitary savings review was conducted for all measures. The unitary savings review entailed a literature...

AI summary A unitary savings review was conducted for the 2024-2025 Demand-side Management Measure Assessment (DSM MA), involving a literature review of technical manuals, metering studies, and program evaluations to refine methodologies for calculating energy and peak demand savings. The review specifically examined refrigerators and freezers, considering changes in manufacturing year-classes and unit sizes.

Calculations Using Evaluation Results The Evaluator calculated the first-year and lifetime energy and peak demand savings as per the calculation methodology presented in Section [3](#page-92-0) below. 4 The 2024-2025 DSM MA is a reference...

AI summary The Evaluator uses the 2024-2025 DSM MA to calculate first-year and lifetime energy and peak demand savings for E1's DSM program, referencing the document's parameters and effective useful life values for measures.

3.2 Gross Savings For ARet, gross savings correspond to the change in energy consumption resulting from the retirement of energy inefficient appliances in participants' homes regardless of both why they participated and what they would hav...

AI summary Gross savings for ARet are calculated based on energy consumption changes from retiring inefficient appliances, regardless of participation motives. The 2024-2025 DSM MA update revised unitary savings values for freezers, refrigerators, and air conditioners using updated data, while dehumidifier values retained 2022 figures. Detailed calculations are outlined in the DSM MA document.

3.2.2 Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is between 5 p.m. and 7...

AI summary Peak demand savings in Nova Scotia occur during 5-7 p.m. on non-holiday weekdays from December to February. Table 7 shows 2024 unitary peak demand savings for retired appliances via ARet, with variations due to updated energy savings values in the 2024-2025 DSM MA. Refrigerators and freezers show 1% and -4% changes respectively, while small appliances remained stable.

3.2.3 Interactive Effects Interactive effects occur when an implemented energy efficiency measure has an impact on the energy consumption of other elements such as heating and cooling equipment. For ARet, retiring old appliances causes an...

AI summary Interactive effects from retiring inefficient appliances (ARet) in Nova Scotia may increase winter heating loads and reduce summer cooling loads. However, factors like electricity use for heating, appliance placement, and air conditioning adoption mitigate these effects. The Evaluator concluded interactive effects are negligible, setting the factor at 0%.

3.2.4 Effective Useful Life Effective useful life (EUL) values are used in the calculations of electrical energy savings that are expected to persist over time. For ARet, the lifetime energy savings and equivalent EUL values are highly inf...

AI summary The document discusses the use of Effective Useful Life (EUL) values in calculating electrical energy savings for appliance retirement programs. It references the 2024-2025 DSM Measure Assessment and notes that the weighted average EUL value is 4.0 years.

3.4 Realization Rate [Table](#page-101-2) 14 below compares ARet total tracked and evaluated savings. It also includes the realization rate, representing the ratio of evaluated net savings to tracked net savings, for both energy savings an...

AI summary This section discusses the realization rate, which is the ratio of evaluated net savings to tracked net savings, for both energy and peak demand savings. A table is referenced that compares total tracked and evaluated savings, providing insight into the effectiveness of savings realization.

Unitary Savings Review A unitary savings review was conducted for all measures as part of a major update to the 2024-2025 DSM Measure Assessment (DSM MA). [15](#page-109-0) The unitary savings review consisted of a literature review of tec...

AI summary A unitary savings review was conducted for all measures as part of updating the 2024-2025 DSM Measure Assessment. The review analyzed technical references, metering studies, and program evaluations to refine methodologies for calculating energy and peak demand savings, focusing on baseline assumptions, efficiency levels, and interactive effects factors.

EUL Update For 2024, the Evaluator conducted a review of all EUL values as part of the 2024-2025 DSM MA update to determine if there were more appropriate EUL estimates.

AI summary The Evaluator reviewed EUL values in 2024 as part of the DSM MA update to assess more accurate estimates.

Note on Margin of Error For evaluation activities that yield quantitative results based on a sample, the Evaluator aimed to achieve a maximum margin of error of 10% at a confidence level of 90%. This means that, if measurements were conduc...

AI summary The Evaluator aimed for a 10% margin of error at 90% confidence in quantitative evaluations, focusing on free-ridership levels in the Instant Savings program. Confidence levels exclude non-sampling errors. The 2024-2025 DSM MA document provides parameters for calculating energy savings and effective useful life values for E1's DSM program measures.

7.2.1 Installation Rates The installation rates of products sold under Instant Savings are assumed to be 100% except for smart power controllers for audiovisual equipment (smart power strips) for which the installation rate was updated to...

AI summary Installation rates for products under Instant Savings are assumed at 100%, except for smart power strips at 86%, as updated in the 2024-2025 DSM MA. Details are referenced to the DSM MA document.

7.2.2 Unitary Energy Savings [Table](#page-111-1) 19 below summarizes the tracked and evaluated energy savings values for the product categories rebated through Instant Savings, which were revised as part of the 2024-2025 DSM MA update. De...

AI summary This section discusses unitary energy savings for product categories rebated through Instant Savings, as revised in the 2024-2025 DSM MA update. The table summarizes tracked and evaluated energy savings, with detailed information provided in the DSM MA document.

7.2.3 Unitary Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is defined as t...

AI summary The document outlines the definition of unitary peak demand savings, focusing on the coldest days between December and February. It notes that the Evaluator updated unitary energy savings for products rebated through Instant Savings as part of the 2024-2025 DSM MA update, while other measures remain unchanged.

Table 20: 2024 Instant Savings Tracked and Evaluated Unitary Peak Demand Savings Product Tracked Savings [W/year] Evaluated Savings [W/year] ENERGY STAR Certified LED Non-A-type Lamps (R, BR, and Decorative) 7.97 7.00 ENERGY STAR Certified...

AI summary Table 20 presents the tracked and evaluated unitary peak demand savings for various energy-efficient products in 2024. The data includes products like LED lamps, dimmer switches, and heat pump water heaters, showing differences between tracked and evaluated savings. The section also references interactive effects related to these savings.

Lighting Products and Controls The weighted interactive effects factors for lighting products were established as part of the 2024-2025 DSM MA update and included the various types of space heating and cooling found in Nova Scotia homes. [...

AI summary The weighted interactive effects factors for lighting products were established as part of the 2024-2025 DSM MA update, and Table 21 presents the interactive effects factors for LED products.

Table 21: 2024 Instant Savings Interactive Effects Factors for Lighting Products Product Category - LED Lamps, Fixtures, and Other Indoor Devices Weighted Energy Interactive Effects Factor Weighted Peak Demand Interactive Effects Factor In...

AI summary Table 21 outlines the 2024 Instant Savings Interactive Effects Factors for various lighting products, including LED lamps, fixtures, and motion sensors. The table provides weighted energy and peak demand interactive effects factors, as well as indoor and outdoor percentages for each product category.

7.2.5 Effective Useful Life The Evaluator validated the EUL values based on the 2024-2025 DSM MA. The EUL values are used in the calculation of electrical energy savings that are expected to persist over time. The baseline for LED lamps re...

AI summary The Evaluator validated Effective Useful Life (EUL) values based on the 2024-2025 DSM MA, noting that LED lamps will become the baseline starting in 2025. The EUL values are used to calculate electrical energy savings over time, with revised values for product categories rebated through Instant Savings, while other measures remain unchanged. The weighted average gross and net EUL values are 3.8 and 4.4 years, respectively.

[Figure](#page-118-0) 8 and [Figure](#page-119-0) 9 below compare tracked unitary electrical energy and peak demand savings to evaluated gross savings. Both sets of savings values include adjustments to account for interactive effects. As...

AI summary The text compares tracked unitary electrical energy and peak demand savings to evaluated gross savings, highlighting adjustments for interactive effects, particularly for LED fixtures, solar fixtures, and room air purifiers. GHG emission reductions are calculated using a Nova Scotia-specific factor applied to Instant Savings gross savings.

Table 28: 2024 Instant Savings Free-Ridership for LED Lamps and Fixtures Product Retailer Evaluation Free-ridership Number of Units Sold ited Average e-ridership Category Period Level Margin of Error in 2024 Level Margin of Error LEDiama C...

AI summary Table 28 presents free-ridership levels for LED lamps and fixtures sold through Instant Savings in 2024. Free-ridership refers to the percentage of savings that are not captured by the program. The data shows varying levels of free-ridership depending on the product and retailer. Table 29 summarizes free-ridership for all product categories, indicating that all other products had zero free-ridership.

Table 32: Evaluated 2024 Instant Savings Net Energy and Peak Demand Savings LED Non-A-ty /pe Lamps LED Recessed LED ENI ERGY STAR Fixture es . Dimmer Indoor Outdoor Motion Sensors Product Category R, BR, and Decorative Others Downlight Fix...

AI summary Table 32 presents the evaluated 2024 Instant Savings Net Energy and Peak Demand Savings for various product categories. It includes gross and net energy savings at the meter and generator, effective useful life, and peak demand savings, providing data on energy efficiency measures.

Evaluated 2024 Instant Savings Net Energy and Peak Demand Savings (Continued) Product Category Efficient Combined Washers/Dryers Room Air Purifiers Dehumidifiers Pool Pumps Heat Pump Water Heaters High Efficiency Dishwashers Bathroom & Uti...

AI summary The 2024 Instant Savings program exceeded its energy and peak demand savings targets by 77% and 45%, respectively. The table provides detailed energy and peak demand savings across various product categories, including gross and net savings at the meter and generator, as well as effective useful life and lifetime energy savings.

7.4 Realization Rate [Table](#page-127-2) 33 below compares the energy and peak demand savings established through this evaluation to those tracked by E1. It also includes the realization rate, representing the ratio of evaluated net savin...

AI summary This section introduces the concept of realization rate, comparing energy and peak demand savings evaluated by the process to those tracked by E1. The realization rate is defined as the ratio of evaluated net savings to tracked net savings for both energy and peak demand.

Gross Savings NTGR Net Savings Realization Rate Value Unit Value Value Unit Value Energy Savings Tracked Savings by E1 29.151 GWh 0.66 19.129 GWh 116% Evaluation Results 26.884 GWh 0.83 22.252 GWh Peak Demand Savings Tracked Savings by E1...

AI summary The table presents energy and peak demand savings tracked by E1 and evaluation results, including gross savings, net-to-gross ratio (NTGR), net savings, and realization rates. The data highlights the performance of energy efficiency measures in Nova Scotia.

Table 1: Overview of Data Collection Activity Descriptor This Instrument Instrument Type Participant Intercept Survey with Online Option Estimated Time to Complete 5 min Target Audience Instant Savings participants who have purchased LED n...

AI summary This document outlines the data collection activities for a survey targeting participants of the Instant Savings program who purchased LED bulbs and fixtures during the fall 2024 campaign. The survey aims to assess free-ridership, cross-influence, and participant perspectives on program awareness.

D. Free-Ridership - LED Bulbs - D1. Efficiency Nova Scotia offered a discount on non-pear-shaped LED bulbs. Before paying at the cash register, were you aware that a discount was offered on the purchase of LEDs? [ALLOW ONLY ONE CODE] - 1....

AI summary This section of the proceeding explores customer awareness and behavior related to discounts on LED bulbs offered by Efficiency Nova Scotia. It includes questions about whether respondents knew about the discount, whether they delayed purchases, and what they would have bought if the discount was not available.

- 96. Other, SPECIFY: ______________________ Incandescent Bulb Halogen Bulb CFLs Bulb G7. How likely would you have been to buy the LED fixtures that you purchased if you had to pay the full price? That is, how likely would have you have b...

AI summary The text includes a question about the likelihood of purchasing LED fixtures without a rebate and another about the timing of purchase without the discount. These questions are part of a survey or proceeding related to energy efficiency and customer behavior.

H. Cross-Influence ROTATE [(H1+](#page-151-1) [H2-](#page-151-2)[H3)](#page-151-3) AND [(H4](#page-151-4) + [H5-](#page-151-5)[H6)](#page-151-6) SEQUENCES; SHOW [H1](#page-151-1) TO [H3](#page-151-3) ON SAME SCREEN AND H4 TO [H6](#page-151...

AI summary The section includes survey questions assessing how prior participation in Efficiency Nova Scotia programs and exposure to promotional materials influenced customers' decisions to purchase LED lighting products.

B1. How many non-pear-shaped LED bulbs did you purchase? 2024 2023 2022 2021 Sample Size 41 81 49 67 1-2 15% 21% 10% 39% 3-4 15% 48% 0% 24% 5-7 17% 15% 16% 25% 8-10 34% 10% 43% 7% 11 or more 20% 6% 31% 4% MEAN 6.9 4.5 9.1 4.3 Base: Respond...

AI summary The text presents data on the number of non-pear-shaped LED bulbs purchased by respondents in different years, with percentages and mean values provided for various purchase ranges. The data is based on a sample size and excludes those who did not answer or refused to participate.

D6. If the discount on LEDs had NOT been offered, what would you have bought? Would you have… 2024 2023 2022 2021 Sample Size 30 40 33 35 Bought LEDs anyway 40% 60% 45% 89% Bought another type of bulb 17% 23% 6% 3% Not bought any bulbs 43%...

AI summary The data shows that a significant percentage of respondents would have purchased LED bulbs even without the discount, with the percentage decreasing over time. A smaller portion would have bought other types of bulbs, while the majority would have not purchased any bulbs.

\ Responses do not sum to 100% due to rounding - E6. For which of the following reasons did you buy this LED fixture?

AI summary The text presents a question regarding the reasons for purchasing an LED fixture, indicating a focus on customer behavior and product adoption.

G6. If you were going to purchase a fixture without LED lighting, what type of bulbs would you have installed? 2024 2023 2022 2021 Sample Size 11 (#) 4 (#) 4 (#) 4 (#) Incandescent 6 2 3 - CFL 4 1 1 1 Halogen 2 - - 3 Others - 1 - - Base: R...

AI summary The table shows the types of bulbs respondents would have installed in fixtures without LED lighting, based on survey data from 2021 to 2024. Incandescent, CFL, and halogen bulbs are the primary options considered, with varying responses across years.

I1. What type of residence do you live in? 2024 2023 2022 2021 Sample Size 200 122 177 120 Detached single-family house 78% 86% 84% 91% Semi-detached house 8% 7% 5% 3% Townhouse or duplex which share adjacent walls 5% 2% 3% 3% Apartment or...

AI summary The text presents survey data on residence types, ownership, electricity bill payment, income, household size, and gender across multiple years. It includes tables with percentages and sample sizes for each category.

Table 2: ME Algorithm - LED bulbs Total Non-A Type Sales G5. If the discount on LED fixtures had NOT been offered, what would you IF 1: G5 = 100% have bought today? IF 2: G5 = Use G5 1. Bought LED fixtures anyway IF 3: G5 = 0% 2. Bought an...

AI summary This table presents the ME Algorithm for LED bulbs, focusing on customer purchasing behavior in the absence of discounts. It includes questions about alternative purchases, purchase timing, and quantity purchased, with conditional logic for responses based on prior answers.

2024 DSM EVALUATION March 26, 2025 In collaboration with:

AI summary The 2024 DSM Evaluation document, dated March 26, 2025, indicates collaboration with unspecified entities, though no detailed analysis or arguments are present in the provided text. The content is limited to a header and image placeholders.

Table 2: Overall 2024 Existing Residential Participation and Evaluated Savings Participation Level Gross Savings NTGR Net Savings Value Unit Value Unit Value Value Unit AMH Energy Savings 83 Projects 1.159 GWh 1.00 1.159 GWh Lifetime Energ...

AI summary Table 2 presents the 2024 participation levels and evaluated savings for various residential programs, including energy savings, peak demand savings, GHG emission reductions, and energy efficiency measures. The data includes metrics like gross and net savings, as well as program-specific participation numbers.

ts (64 prescriptive and 19 comprehensive) generated electrical savings in 2024. Together, AMH paths generated 21% and 23% fewer electrical energy and peak demand savings respectively compared to 2023. 2024 AMH-Finding: Following project re...

AI summary In 2024, AMH programs achieved 21% and 23% lower electrical energy and peak demand savings compared to 2023. The Evaluator adjusted energy savings upward and peak demand savings downward, with discrepancies between evaluated and E1-tracked savings attributed to these adjustments.

Ensure that DAs/contractors provide a leave-behind so that participants know who to contact in case of questions or difficulties or to better understand how to operate and maintain their heat pump. 2024 ASFH-Finding: Having to use two plat...

AI summary The 2024 ASFH program achieved significant energy savings but faces administrative challenges due to fragmented reporting platforms and outdated training materials. Recommendations include streamlining processes and updating DA/contractor training. Participation increased 5x YoY, though average savings per participant declined 48% due to adjustment ratio changes.

EPI Findings and Recommendations This section provides the Evaluator's key findings and recommendations in relation to the above objectives. 2024 EPI-Finding: Participants are highly satisfied with the overall program component. 2024 EPI-F...

AI summary The 2024 EPI program achieved high participant satisfaction but fell 15% and 22% short of energy and peak demand savings targets. Savings per participant declined due to DSM MA updates reducing smart thermostat and LED savings assumptions. Installation rates for smart thermostats with MSHPs dropped 16%, prompting recommendations for improved participant education and follow-up. NTGR stability masked product-specific fluctuations, while jurisdictional scans identified new potential EPI measures.

Green Heat Findings and Recommendations 2024 Green Heat - Finding: Green Heat fell short of its net savings targets, achieving 24% and 55% of its electrical energy and peak demand targets respectively. 2024 Green Heat-Finding: Green Heat p...

AI summary The 2024 Green Heat program underperformed, achieving only 24% and 55% of its energy and peak demand targets. Participation dropped due to the CGH Grant's launch, with low savings rates (35% and 71%) for MSHPs and biomass measures. Nil electrical savings were found for some households, and a recommendation to remove wood/pellet fireplace inserts is proposed.

MHEEP Findings and Recommendations This subsection presents the key findings from the MHEEP evaluation. The Evaluator has no specific recommendation for MHEEP. 2024 MHEEP-Finding: MHEEP net electrical energy savings fell short of targets b...

AI summary The MHEEP program underperformed in 2024, achieving 35% less net energy savings than targets but exceeding peak demand savings by 189%. Participation increased by 19%, yet energy savings per participant fell by 41%. Discrepancies with E1's data stemmed from revised adjustment ratios and heat pump peak demand metrics.

Residential Behaviour Findings and Recommendations This subsection presents the key findings from the 2024 Residential Behaviour evaluation. The Evaluator has no specific recommendation for Residential Behaviour. 2024 Residential Behaviour...

AI summary The 2024 Residential Behaviour program achieved 6.270 GWh in energy savings, below its 8.000 GWh target, but shows promise as it scales. Attrition rates reached 5.2-7.0% among participants, and treatment group customers showed higher engagement in other programs compared to controls.

Program Tracked and Evaluated Savings [Table](#page-3-0) 3 below compares E1 tracked electrical energy and peak demand savings to evaluated savings at the generator. It also includes the realization rate, representing the ratio of evaluate...

AI summary The table compares tracked and evaluated savings from E1 programs, including realization rates and NTGRs. Residential Behaviour lacks a tracking sheet due to random participant selection. NTGR values differ from other sections as they are rounded averages of net savings divided by gross savings.

EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (DSM) for Nova...

AI summary EfficiencyOne (E1), a non-profit organization, manages energy efficiency and demand-side management (DSM) programs in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. E1's 2024 DSM program portfolio includes residential and BNI programs, and an evaluation report was commissioned by E1 to assess these programs. The evaluation focuses on impact assessments, including baseline definitions, savings calculation methodologies, and parameter values.

Table 4: Types of Evaluations Conducted for Each Program Component, 2024 2024 Program Program Component Process Market Impact Existing Residential AMH X Comprehensive ASFH X Condensed EPI X Comprehensive Green Heat Comprehensive HEA Compre...

AI summary Table 4 outlines the types of evaluations conducted for each program component in 2024, including details on market and impact assessments. The Evaluator prepared a DSM evaluation report that includes findings on energy savings, peak demand savings, and GHG emissions for each component.

EUL Update Using the effective useful life (EUL) values for common measures presented in the 2024-2025 Demand-side Management Measure Assessmen[t](#page-11-0) 3 (DSM MA) and the proportions of savings generated by individual measures imple...

AI summary The Evaluator calculated an average Effective Useful Life (EUL) for comprehensive projects using EUL values from the 2024-2025 DSM MA and savings proportions from AMH in 2024. Individual measure EULs were applied to prescriptive projects based on DSM MA data.

Table 6: 2024 Elements of Dissatisfaction and Justifications Element of Dissatisfaction (Score of <8) Reasons for Dissatisfaction (#) (P = Prescriptive participant) n=1 Selection of upgrades available › Would like to see more options for p...

AI summary The table outlines elements of dissatisfaction from participants in the 2024 program, including limited upgrade options, delays in program completion, and delays in rebate processing. These issues were reported by prescriptive participants.

Table 7: 2024 Elements of Dissatisfaction and Justifications Among EAs Element of Dissatisfaction (Score of <8) Reasons for Dissatisfaction (#) n=4 Initial audit reporting template › Template is not user-friendly/awkward/lengthy to fill ou...

AI summary The table highlights dissatisfaction among Energy Auditors (EAs) with various aspects of program delivery, including audit reporting templates, simulator software, information provided at the start of projects, and communication processes. Key issues include non-user-friendly templates, limited flexibility, and challenges with software tools like R2000 and RETScreen.

Service, Relationship Building, and Flexibility E1 staff are of the opinion that the AMH prescriptive path offers quick and easy turnaround and a handson service to participants. They see the comprehensive path as complementing the prescri...

AI summary E1 staff advocate for the AMH prescriptive path's efficiency and hands-on service, while the comprehensive path offers long-term, customized upgrades and relationship-building opportunities. EAs emphasize the comprehensive path's flexibility for multi-fuel and system improvements over time.

Managing Projects and Finding Contractors Over and above the E1 service provided throughout AMH participation, some EAs receive requests from participants for additional project management assistance . EAs mention that participants are som...

AI summary Energy Auditors (EAs) report that Affordable Multifamily Housing (AMH) participants often lack project management skills and struggle to secure contractors, causing delays and affecting rebate calculations. E1 staff recommend enhanced project management support, while supply chain cost increases have influenced incentive adjustments.

Prescriptive Path Project Desk Review Findings Energy savings for AMH prescriptive path projects are calculated using the 2024-2025 DSM MA equations. Among the reviewed prescriptive path projects, four were lighting projects and six were h...

AI summary The Prescriptive Path Project Desk Review evaluates energy savings calculations for AMH projects, noting that lighting projects required no adjustment ratios while heat pump projects saw 6% increased savings due to HSPF2 adjustments. One project's energy savings decreased by 10% due to a regional HSPF2 mismatch. The overall adjustment ratio was 1.016 (margin of error 2.8%), below the 10% threshold, leading to its use only in 2024 evaluations.

4.2.2 Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is defined as the colde...

AI summary Peak demand savings are defined as reductions in electricity demand during the coldest days (−15°C) between 5 p.m. and 7 p.m. in December to February. Project reviews adjust these savings similarly to energy savings.

Comprehensive Project Review Findings For AMH comprehensive projects, peak demand savings are obtained by multiplying the energy savings by a peak demand-to-energy ratio of 0.283 W/kWh as indicated in the 2024-2025 DSM MA. This resulted in...

AI summary For AMH comprehensive projects, peak demand savings are calculated using a 0.283 W/kWh ratio from the 2024-2025 DSM MA. Due to high margin of error, no adjustment ratio was applied to nonreviewed projects, aligning with energy savings adjustment ratios.

Prescriptive Project Review Findings Similarly to energy savings, peak demand savings for AMH prescriptive projects are calculated using the 2024-2025 DSM MA equations. For lighting projects, project reviews revealed that E1 used a peak co...

AI summary The review found that peak demand savings for AMH projects used 2024-2025 DSM MA equations. Lighting projects had a 32% reduction in peak savings after adjusting coincidence factors, but margin of error limited extrapolation. Heat pump projects had no adjustments, leading to equal evaluated and tracked savings. No adjustment ratios were extrapolated for non-reviewed projects.

Adjustment Ratio Summary The above changes resulted in three different adjustment ratios for peak demand savings. [Table](#page-32-1) 9 below summarizes the peak demand savings adjustment ratios and margins of error obtained for the differ...

AI summary The text presents three adjustment ratios for peak demand savings in AMH projects, with a table detailing ratios, margins of error, and one-time adjustments for different project categories. Comprehensive, Prescriptive Lighting, and Prescriptive Heat Pump projects have distinct ratios, with the latter having a 0% margin of error.

4.2.4 Effective Useful Life Using the EUL values presented in the 2024-2025 DSM MA and the proportion of savings for measures implemented through AMH in 2024, the Evaluator revised the EUL values for comprehensive projects. For prescriptiv...

AI summary The Evaluator revised Effective Useful Life (EUL) values for comprehensive projects using data from the 2024-2025 DSM MA and savings proportions from AMH in 2024. The weighted average EUL for comprehensive projects increased from 17.7 years in 2023 to 20.5 years in 2024.

4.4 Realization Rate [Table](#page-36-0) 13 below compares the energy and peak demand savings established through this evaluation to those calculated in the 2024 tracking sheet. It also includes the realization rate, representing the ratio...

AI summary The section discusses the realization rate, comparing energy and peak demand savings from the current evaluation to those in the 2024 tracking sheet, with the realization rate representing the ratio of evaluated net savings to tracked net savings.

2024 AMH-Finding: AMH net electrical energy savings and peak demand savings fell short of targets. In 2024, AMH achieved 1.159 GWh in net electrical energy savings and 0.570 in net peak demand savings at the generator, falling short of pla...

AI summary In 2024, the Affordable Multifamily Housing (AMH) program achieved 1.159 GWh in net electrical energy savings and 0.570 in net peak demand savings, falling short of targets by 39% and 64%, respectively.

2024 AMH-Finding: Following project reviews, the Evaluator made an overall upward adjustment to electrical energy savings and a downward adjustment to peak demand savings. The 2024 AMH project reviews led to two adjustments for electrical...

AI summary The 2024 AMH project reviews resulted in an upward adjustment to electrical energy savings via a 1.016 AR for prescriptive heat pump projects due to HSPF2 factor changes, and a downward adjustment to peak demand savings from revised peak coincidence factors and comprehensive project modelling errors. Adjustments largely offset each other, yielding negligible net differences.

2024 AMH-Finding: The evaluated net electrical energy savings and peak demand savings were nearly the same as the savings tracked by E1. The net evaluated electrical energy savings were nearly the same as the energy savings tracked by E1 a...

AI summary The evaluated net electrical energy savings and peak demand savings for the 2024 AMH program were nearly identical to E1's tracked savings, with a 2% lower peak demand difference attributed to evaluator adjustments following project reviews.

6.1 ASFH Description ASFH provides energy efficient retrofits and heat pump installations to income-qualified Nova Scotian owners of both electrically heated and non-electrically heated homes at no cost to participants. The program compone...

AI summary The Affordable Single-family Homes (ASFH) program offers no-cost energy-efficient retrofits and heat pump installations to income-qualified Nova Scotian homeowners. It includes building envelope upgrades, moisture management, and appliance replacements via the E1 Appliance Retirement program. E1 manages HomeWarming, supported by delivery agents and contractors, with energy assessments conducted by EAs and retrofits approved by E1. Heat pump installations are handled separately by contractors.

Figure 15: ASFH Participation Process Summary Interested participants can apply to E1 directly or be referred by Housing Nova Scotia. - DAs, Housing Nova Scotia, or staff from other E1 programs identify potential participants. - Following...

AI summary The ASFH program enables eligible participants to access energy efficiency retrofits through E1 and DAs, with eligibility for EPI and OHPA programs. Process steps include application, assessment, retrofit scheduling, and cost approval. Energy savings targets for 2024 are 2.825 GWh and 1.046 MW.

DAs A total of four DAs were interviewed for this process evaluation, three of whom began their involvement in January 2023 and one of whom already had experience delivering HomeWarming for the province. Overall, seven DAs manage the model...

AI summary Four Delivery Agents (DAs) were interviewed, with three new to the role and one experienced in HomeWarming. Seven DAs manage participants through program components, leveraging their broad experience in energy efficiency initiatives.

Receiving Participant Information from E1 Mixed opinions were offered regarding the process for receiving information about participants interested in ASFH. DAs say they receive list updates directly from E1 every two weeks. While informat...

AI summary Mixed feedback exists about E1's process for sharing ASFH participant data with DAs. While SharePoint updates are accurate, inconsistencies in lead volume, incorrect details (e.g., outdated addresses), and lack of home system information create challenges for DAs in staffing, logistics, and program delivery.

Installation of Upgrades Once a project is approved, the DA informs the modelled participant and organizes and completes the approved upgrade installations. This step mostly occurs without any major hitches. As illustrated in [Figure](#pag...

AI summary Installation of upgrades generally proceeds smoothly with high participant satisfaction, but challenges include disruptions during large projects, rescheduling delays, cost-sharing frustrations, and occasional participant dissatisfaction leading to discontinuation. DAs report good collaboration with E1 but note challenges in managing expectations and administrative issues.

8.1.3 Participant Questions and Concerns A majority of modelled participants (63%) does not have questions about energy efficiency or the technologies installed through ASFH. For the minority that does, questions tend to revolve around how...

AI summary 63% of ASFH participants have no questions about energy efficiency, but those who do seek guidance on operating heat pumps, maintenance, and costs. DAs note challenges in explaining systems to elderly participants and report a preference for phone-based support. Contractors provide manuals, but understanding remains an issue.

Receiving Participant Information from E1 At this step, heat pump contractors are most concerned with the time required to regularly monitor SharePoint for new leads. They say that this can delay the program delivery process.

AI summary Heat pump contractors express concern that the time required to monitor SharePoint for new leads delays program delivery processes, highlighting inefficiencies in lead management within E1's operations.

Initial Visit with Participant The interviewed heat pump contractors, similar to the interviewed DAs, believe that the initial visit to complete the energy assessment or size a heat pump is generally a simple and easy process to complete....

AI summary Heat pump contractors and Delivery Agents (DAs) report that initial visits for energy assessments or heat pump sizing are generally straightforward. However, homeowners with limited knowledge about Affordable Single-family Homes (ASFH) face challenges during this process.

Where: - › and correspond to the modelled energy consumption levels obtained in HOT2000 during the pre-retrofit (D) and post-retrofit (E) assessments respectively. Since modelled energy consumption levels were not directly available in the...

AI summary The text outlines a methodology for calculating energy savings using EnerGuide ratings, adjustment ratios (ARs) derived from 2024 billing analyses, and prescriptive measures in ASFH. It references HOT2000 modelled consumption, HEA evaluations, and DA assessments for space heating estimates.

Non-modelled Energy Savings Smart thermostats and replaced appliances are not modelled in HOT2000. Instead, energy savings are calculated based on unitary energy savings values from the 2024-2025 DSM MA, which provides a detailed descripti...

AI summary Non-modelled energy savings from smart thermostats and replaced appliances are calculated using unitary energy savings values from the 2024-2025 DSM MA. Heat pump energy savings are calculated using the Green Heat approach. Table 17 summarizes updated unitary energy savings values for measures changed in 2024.

Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is between 5 p.m. and 7 p.m. in the months of Dec...

AI summary The text discusses how peak demand savings are calculated for Affordable Single-Family Housing (ASFH) in Nova Scotia, using a peak demand-to-energy ratio established by Navigant in the 2016-2018 DSM Plan. Special considerations are made for heat pump measures, where peak demand savings are calculated separately using the HEA and Green Heat approaches.

participants that installed a heat pump and had no modelled energy savings, peak demand savings were calculated using the Green Heat calculation approach (see Subsection [19.2.3](#page-135-0) below). Smart thermostats and replaced applianc...

AI summary The document discusses the calculation of peak demand savings for heat pumps and other measures under the Affordable Single-Family Housing (ASFH) program. It references the Green Heat calculation approach and notes that smart thermostats and replaced appliances are not modelled in HOT2000, with unitary peak demand savings values reviewed and updated in the 2024-2025 DSM MA.

9.2.4 Effective Useful Life As part of the 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculation of electrical energy savings that are expected to persist over time; the EUL values were not revised for...

AI summary The Evaluator reviewed Effective Useful Life (EUL) values for electrical energy savings in the 2024-2025 DSM MA activities. EUL values were not revised for most measure categories except modelled measures, such as building envelope upgrades and modelled space heating equipment. Table 19 shows the EUL values changed in 2024.

Table 20: Evaluated 2024 ASFH Modelled Measure Gross Energy and Peak Demand Savings Total Energy Savings Gross Energy Savings Without Adjustment Ratio (AR) – at the Meter (GWh) 4.484 Gross Energy Savings with AR – at the Meter (GWh) 3.111...

AI summary Table 20 and Table 21 present energy and peak demand savings from the 2024 Affordable Single-Family Housing (ASFH) program. The tables include modeled and non-modeled measures, with energy savings calculated using line loss factors provided by NS Power. The data highlights the impact of various efficiency measures on energy and peak demand, including heat pumps and appliance replacements.

2024 ASFH-Finding: ASFH exceeded its electrical energy and peak demand savings targets. ASFH achieved 3.719 GWh in net electrical energy savings and 2.089 MW in net peak demand savings at the generator in 2024, thus exceeding the planned t...

AI summary In 2024, ASFH achieved 3.719 GWh in net electrical energy savings and 2.089 MW in net peak demand savings, exceeding targets of 2.825 GWh and 1.046 MW by 32% and 100%, respectively.

11.1 EPI Description EPI provides participants with free-of-charge direct installations of energy efficient products. EPI has played a pivotal role in transforming the residential lighting market by making energy-efficient products more af...

AI summary EPI provides free direct installations of energy-efficient products, transforming the residential lighting market by making energy-efficient products more affordable. As LEDs become the baseline, EPI will phase out lighting measures and focus on electrician-installed products, which are key for Residential Demand Response activities. E1 contracts service providers to deliver EPI across the province, available to both homeowners and renters.

11.3 Participation History As presented in [Figure](#page-78-0) 36 below, EPI had 9,993 DSM participants, which represents a 2.4% increase in participation compared to 2023.[28](#page-77-2) In 2024, 150,722 efficient products were installe...

AI summary EPI (Efficient Product Installation) reported 9,993 DSM participants in 2024, a 2.4% increase from 2023. However, efficient product installations decreased by 2%, with LED lamps remaining the top product type (74% of installs). Average savings per participant fell 6.3%, and gross energy savings in 2024 were slightly lower than 2023 despite higher participation.

Unitary Savings Review The Evaluator did a complete review and update of unitary savings values and produced the 2024-2025 Demand-side Management Measure Assessment (DSM MA).[31](#page-80-0) The unitary savings review entailed a literature...

AI summary The Evaluator conducted a comprehensive review and update of unitary savings values for the 2024-2025 Demand-side Management Measure Assessment (DSM MA), evaluating technical references and methodologies to refine calculations for energy and peak demand savings, including baseline assumptions and efficiency parameters.

EUL Update The Evaluator updated the EUL values of lighting products given the rapid evolution of the lighting market. The 2024-2025 DSM MA was updated accordingly.

AI summary The Evaluator updated Effective Useful Life (EUL) values for lighting products due to rapid market changes, leading to revisions in the 2024-2025 Demand-side Management Measure Assessment (DSM MA).

GHG Emission Reduction Calculations To obtain net avoided GHG emissions in CO2 eq for EPI, the Evaluator multiplied net energy savings by the latest Nova Scotia-specific factor for GHG emissions generated by electricity production. This fa...

AI summary The Evaluator calculates net avoided GHG emissions for EPI by multiplying energy savings with Nova Scotia Power (NSP) data. The 2024-2025 DSM MA document guides energy savings calculations and effective useful life values. The Evaluator assesses study appropriateness based on factors like measure similarity, climate conditions, and methodology quality.

14.2 Gross Savings For EPI, gross savings correspond to the change in energy consumption resulting from installing energy efficient products in participant homes regardless of why they participated.[35](#page-83-2) The Evaluator relied on...

AI summary Gross savings for EPI are calculated based on energy consumption changes from efficient product installations, using the 2024-2025 DSM MA and a 2019 adjustment ratio. EUL values for lighting products were updated, with other product EUL values sourced from the same DSM MA.

14.2.1 Installation Rates Installation rates represent the proportions of products recorded in the tracking sheet that remain installed in participant homes. During the 2024 evaluation, the Evaluator conducted 60 on-site visits to update i...

AI summary The 2024 evaluation of installation rates for EPI measures involved 60 on-site visits, with some products having unreliable rates. Thermostatic shower valves retained an 89% rate due to proximity to prior data, while smart thermostats for MSHPs had 84% due to participant disconnections. Margins of error influenced these decisions.

For EPI, E1 establishes separate unitary savings values for single-family homes and apartments. For the 2024 evaluation, the Evaluator used the unitary savings values from the 2024-2025 DSM MA. That document provides a detailed description...

AI summary The text discusses the unitary energy savings values for products installed through the Efficient Product Installation (EPI) program, noting that they were updated in the 2024-2025 Demand-Side Management Measure Assessment (DSM MA) document. Table 28 summarizes the changes to these savings values.

14.2.3 Unitary Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity demand peak period in Nova Scotia is between 5 p...

AI summary Peak demand savings refer to reductions in electricity demand during peak periods, specifically between 5 p.m. and 7 p.m. from December to February on non-holiday weekdays in Nova Scotia. The Evaluator used the 2024-2025 DSM MA to establish unitary peak demand savings for each product, with differences arising from updates to unitary energy savings.

14.2.6 Effective Useful Life As part of the 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculation of electrical energy savings that are expected to persist over time. Apart from lighting products, the E...

AI summary The document discusses the review and update of Effective Useful Life (EUL) values for energy efficiency measures, particularly lighting products, due to changes in the LED market. The EUL values are used to calculate electrical energy savings over time, and updated values are applied in the 2024-2025 DSM MA.

Thermostatic Low-flow Showerheads Product Category Faucet Aerators Shower Valves 2.5 gpm 0.5 gpm Reduction 0.75 gpm Reduction 1.0 gpm Reduction Number of Units Number of Units 3,892 329 325 183 1,995 Installation Rate (%) 92% 89% 95% 95% 9...

AI summary The table presents data on energy and peak demand savings from the installation of thermostatic faucet aerators and low-flow showerheads. It includes metrics such as the number of units installed, energy savings, effective useful life, and peak demand savings across various product categories and flow rate reductions.

Product Category Pipe Insulation (per foot) Hot Water Tank Wraps Smart Thermostats for Electric Baseboards Smart Thermostats for MSHPs Advanced Learning Thermostats for Central Electric Heating without a Heat Pump Advanced Learning Thermos...

AI summary The table presents energy savings and installation data for various energy efficiency products, including pipe insulation, hot water tank wraps, and smart thermostats, across different categories and technologies, such as MSHPs and central electric heating systems.

Table 32: Evaluated 2024 EPI Gross Electrical Energy and Peak Demand Savings per Measure – Apartments LED Lamps Product Category 9 W Replacing 25 W 29 W 40 W 43 W 60 W 72 W 100 W 150 W Number of Units Number of Units 39 13 493 16 2,799 2 5...

AI summary Table 32 evaluates the 2024 EPI Gross Electrical Energy and Peak Demand Savings per Measure in apartments, focusing on LED lamps replacing various wattage bulbs. It includes metrics like energy savings, interactive effects factors, and peak demand savings, with data on installation rates and effective useful life.

Thermostatic Low-flow Showerheads Product Category Faucet Aerators Shower Valves 2.5 gpm 0.5 gpm Reduction 0.75 gpm Reduction 1.0 gpm Reduction Number of Units Number of Units 205 3 24 11 78 Installation Rate (%) 92% 89% 95% 95% 95% Number...

AI summary The table presents data on energy and peak demand savings from the installation of thermostatic faucets, aerators, and low-flow showerheads in various categories. It includes metrics such as the number of units installed, energy savings, effective useful life, and peak demand savings, with adjustments for line loss and interactive effects.

[Figure](#page-110-0) 39 below compares the tracked and evaluated gross electrical energy savings at the generator, while [Figure](#page-111-0) 40 further below compares the tracked and evaluated gross peak demand savings at the generator....

AI summary The text discusses the comparison of tracked and evaluated gross electrical energy savings and gross peak demand savings at the generator, as illustrated in Figures 39 and 40. It also mentions the calculation of GHG emission reductions using a Nova Scotia-specific factor applied to EPI gross savings results, as presented in Table 34.

14.3.1 Free-ridership For EPI, free-ridership occurs when participants would have installed the same energy efficient products in the absence of the program component. For low-income participants, the free-ridership level is assumed to be...

AI summary The document discusses free-ridership in EPI, noting that non-low-income participants were surveyed to assess program impact, while literature reviews informed air sealing product free-ridership levels. A 12% rate was selected for air sealing based on Massachusetts data, reflecting lower adoption likelihood without the program.

Evaluated 2024 EPI Net Electrical Energy and Peak Demand Savings per Measure (Continued) LED Lamps Gross Energy Savings – at the Meter (GWh) 0.447 0.037 0.066 0.055 0.782 NTGR 0.99 0.99 0.99 0.99 0.99 Net Energy Savings – at the Meter (GWh...

AI summary The document provides a continuation of the evaluated 2024 EPI net electrical energy and peak demand savings per measure, including data on various efficiency measures such as LED lamps, pipe insulation, and smart thermostats, along with metrics like gross and net energy savings, NTGR, and line loss factors.

Evaluated 2024 EPI Net Energy and Peak Demand Savings per Measure (Continued) Air Sealing Products – Heat Pump Heating Product Category Foam Gaskets Door Sweeps Window Air Sealing Window Film Kits Door Weather Stripping Grand Total Energy...

AI summary The document presents a continuation of the evaluated 2024 EPI net energy and peak demand savings per measure, showing results for various air sealing products. EPI fell short of its electrical energy and peak demand savings targets by 15% and 22%, respectively.

14.4 Realization Rate [Table](#page-121-1) 39 below compares the energy and peak demand savings established through the 2024 evaluation to those tracked in the 2024 tracking sheet. It also includes the realization rate, representing the ra...

AI summary Table 39 compares energy and peak demand savings from the 2024 evaluation with those tracked in the 2024 tracking sheet, including the realization rate, which is the ratio of evaluated net savings to tracked net savings for both energy and peak demand.

Table 39: Comparison of 2024 EPI Tracked and Evaluated Savings at the Generator Gross Savings NTGR Net Savings Realization Rate Value Value Unit Value Value Unit Energy Savings Tracked Savings by E1 9.516 GWh 1.00 9.519 GWh 94% Evaluation...

AI summary Table 39 compares tracked and evaluated energy and peak demand savings from the 2024 Efficient Product Installation (EPI) program. Tracked savings by EfficiencyOne (E1) are compared with evaluation results, showing slight differences in gross and net savings, as well as realization rates for energy and peak demand.

15.1 Jurisdictional Scan This section outlines the findings of the jurisdictional scan conducted to identify opportunities for electricianinstalled measures. In 2023, E1 introduced some electrician-installed measures to the EPI offering an...

AI summary A jurisdictional scan by E1 identified few new electrician-installed measures for EPI beyond existing offerings. Reviews of Canadian and US direct install and instant rebate programs found most measures either already included in EPI or outside electrician scope (e.g., insulation, appliances). Key findings highlight limited opportunities for expansion.

16 EPI Key Findings and Recommendations As mentioned previously, the main objectives of the 2024 EPI evaluation were as follows: - › Collect information on participant perspectives - › Calculate gross and net EPI results, namely electrical...

AI summary The 2024 EPI evaluation aimed to collect participant perspectives, calculate energy and GHG savings, and analyze market opportunities. This section outlines key findings and recommendations related to these objectives.

2024 EPI-Finding: Participation slightly increased in 2024, although not enough to increase savings compared to 2023. While participation increased by 2% in 2024, average electrical energy savings per participant decreased by 6.3%. This re...

AI summary Participation in the 2024 EPI program increased by 2%, but average electrical energy savings per participant fell by 6.3%. This decline was driven by updates to the 2024-2025 DSM MA, including reduced savings estimates for smart thermostats (28%) and LED lamps (39%) in EPI gross savings.

Billing Analysis After inconclusive results in 2023, a billing analysis was again conducted for Green Heat as part of the 2024 DSM evaluation to obtain measured electrical energy savings generated through the installation of MSHPs and wood...

AI summary A 2024 billing analysis for Green Heat, part of the DSM evaluation, updated savings values for MSHPs and wood/pellet stoves. This followed inconclusive 2023 results and updated prior 2017 data for MSHPs, with first-time analysis for stoves since 2013 modeling. Appendix XXIV details the methodology.

19.2 Gross Savings For Green Heat, gross savings correspond to the change in energy consumption resulting from actions taken by participants regardless of their reasons for participating.[50](#page-133-2) For the 2024 evaluation, the Evalu...

AI summary Gross savings for Green Heat are defined as energy consumption changes from participant actions, regardless of motivation. The 2024 evaluation used the 2024-2025 DSM MA values for calculations, as noted in footnote 50.

19.2.2 Unitary Energy Savings To establish Green Heat unitary savings, the Evaluator relied on a combination of billing analyses, energy models, engineering algorithms, and literature reviews. The 2024-2025 DSM MA provides a detailed descr...

AI summary The Evaluator used billing analyses, energy models, engineering algorithms, and literature reviews to establish Green Heat unitary savings. The 2024-2025 DSM MA details inputs and calculations for measure categories, with subsections focusing on MSHP measures, wood/pellet stoves, and literature-based updates to unitary savings.

Summary of Updates to Unitary Energy Savings [Table](#page-134-0) 44 below summarizes the tracked and evaluated unitary energy savings values of each measure installed through Green Heat and for which there was an update in the 2024-2025 D...

AI summary The document summarizes updates to unitary energy savings values for measures installed through Green Heat, based on the 2024-2025 DSM MA. The MSHP and wood/pellet stove and fireplace insert savings were updated, while other measures retained their original values.

19.2.3 Unitary Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is defined as...

AI summary The text explains how unitary peak demand savings are calculated for heating systems in Nova Scotia, noting that MSHPs showed no savings due to nil energy results, while wood/pellet stoves had adjusted savings based on billing analysis. Adjustments were made using energy savings ratios, referencing the 2024-2025 DSM MA and Econoler's 2014 report.

Table 46: Evaluated 2024 Green Heat Gross Electrical Energy and Peak Demand Savings Measure MSH IPs Fully Electrical Mainly Electrical CASHPs Air-to-water Heat Pumps Number of Units 793 162 16 1 Energy Savings Unitary Energy Savings (kWh)...

AI summary Table 46 evaluates the 2024 Green Heat program's electrical energy and peak demand savings, comparing different measures such as mini-split heat pumps, wood stoves, and pellet stoves. It provides data on energy savings, unitary savings, gross energy savings, and effective useful life for each measure.

Table 49: 2024 Green Heat NTGRs Measure Free-ridership NTGR MSHPs 48% 0.52 Biomass and Solar Measures 47% 0.53 Demand Reduction Measures 9% 0.91 CASHPs and AWHPs 33% 0.67 19.3.3 Evaluated Net Savings

AI summary Table 49 presents the 2024 Green Heat NTGRs for various measures, including MSHPs, Biomass and Solar Measures, Demand Reduction Measures, and CASHPs and AWHPs. The table shows free-ridership percentages and corresponding NTGR values for each measure. Section 19.3.3 discusses the evaluated net savings related to these measures.

Table 50: Evaluated 2024 Green Heat Net Electrical Energy and Peak Demand Savings MSHPs Measure Fully Electrical Mainly Electrical CASHPs AWHPs Energy Savings Gross Energy Savings – at the Meter (GWh) 1.174 - 0.056 0.007 NTGR 0.52 0.52 0.6...

AI summary Table 50 evaluates the 2024 Green Heat Net Electrical Energy and Peak Demand Savings, comparing various heating measures such as MSHPs, CASHPs, AWHPs, and wood and pellet stoves. The table includes gross and net energy savings at the meter and generator, as well as peak demand savings and line loss factors for each measure.

19.4 Realization Rate [Table](#page-147-1) 51 below compares the energy and peak demand savings established through the 2024 evaluation to those calculated in the 2024 tracking sheet. It also includes the realization rate, representing the...

AI summary This section discusses the realization rate, comparing energy and peak demand savings from the 2024 evaluation to those in the 2024 tracking sheet, and includes the ratio of evaluated net savings to tracked net savings.

2024 Green Heat - Finding: Green Heat participation decreased for the third consecutive year. Green Heat participation levels decreased by 21% compared to 2023 levels, particularly for MSHPs (19% reduction) and demand reduction measures (3...

AI summary Green Heat participation fell 21% in 2024, the third consecutive year of decline, attributed to federal CGH Grant competition. Evaluation showed 35% net energy savings realization, with MSHPs and biomass measures underperforming. Recommends removing wood/pellet inserts due to negligible savings.

Expired Participant Survey To reassess unconverted D assessment savings parameters, Narrative Research conducted a telephone survey with a total of 76 expired participants, i.e. participants who did not complete a final home energy assessm...

AI summary Narrative Research conducted a telephone survey of 76 expired participants in E1's DSM program who did not complete final home energy assessments. The survey, part of the 2024-2025 DSM MA evaluation process, aimed to reassess unconverted D assessment savings parameters and inform energy savings calculations for prescriptive measures.

23.2.1 Installation Rates Installation rates represent the proportion of products recorded in the tracking sheet and that remain installed in participant homes. Based on the 2024-2025 DSM MA, installation rates for all HEA measures were es...

AI summary Installation rates track the proportion of products installed in participant homes, with 100% rates for HEA measures confirmed by EAs during assessments under the 2024-2025 DSM MA. The definition draws from NREL's Uniform Methods Project Chapter 23.

Measures Modelled in HOT2000 After the D and E home energy assessments, EAs model the house in the HOT2000 energy simulation software to obtain the initial and final EnerGuide ratings of the given house. Energy consumption levels obtained...

AI summary The document outlines the methodology used in HOT2000 for modeling energy savings from home energy assessments. It describes the use of adjustment ratios derived from billing analyses to improve the accuracy of savings calculations, ensuring that only savings attributable to the program are captured. The analysis excludes participants who installed non-modelled measures like wood burning equipment or solar PV.

For some measures installed under HEA, instead of using a performance approach based on energy simulation results, energy savings are calculated using a prescriptive approach based on unitary energy savings values. This is the case for: -...

AI summary The document explains how energy savings for certain measures installed under HEA are calculated using a prescriptive approach based on unitary energy savings values rather than performance-based simulations. This applies to wood burning equipment, smart thermostats, solar PV systems, and HPWHs. The approach for HPWHs is adjusted to avoid double counting and facilitate future savings accounting.

Table 55: 2024 HEA Tracked and Evaluated Unitary Energy Savings for Prescriptive Measures Measure Tracked Savings [kWh/year] Evaluated Savings [kWh/year] Wood Stoves or Fireplace Inserts with Electric Resistance Baseline 10,443 1,461 Wood...

AI summary Table 55 presents evaluated energy savings for wood and pellet stoves and fireplace inserts under the Home Energy Assessment (HEA) program. Due to limitations in tracking separate savings for these measures, a unitary energy savings value was applied. The Evaluator recommends removing fireplace inserts from future offers due to low savings.

Reporting Requirements HEA incentives originate from three sources of funding: Nova Scotia Power ratepayers for DSM, the Province of Nova Scotia, and the Government of Canada (CGH Grant). The incorporation of the CGH Grant, as a cofunder o...

AI summary HEA incentives are funded by Nova Scotia Power ratepayers, the Province of Nova Scotia, and the Canadian government (CGH Grant). Savings are reported to NSUARB and the Province via separate evaluations, focusing on electrical savings (DSM) and participation/GHG reductions (government). Equations prevent double-counting, and solar PV savings are included in DSM reports regardless of heating source.

Measures Modelled in HOT2000 For measures modelled in HOT2000, peak demand savings are calculated by applying a peak demand-toenergy ratio of 0.283 MW/GWh after having removed the energy savings associated with mini-split heat pumps. The e...

AI summary This section discusses the calculation of peak demand savings for measures modelled in HOT2000, specifically focusing on mini-split heat pumps. The calculation involves a peak demand-to-energy ratio and energy savings values derived from the Home Energy Assessment (HEA) and Green Heat. An equation is provided to calculate peak demand savings based on various parameters.

Table 56: Unitary Peak Demand Savings Values for Mini-split Heat Pumps Variable Symbol Value Rated heating capacity of the new heat pump at outdoor air temperature of -15 °C [kBTU/h] 𝐻𝐶𝑚𝑖𝑛 Specification data for each installed system Coeff...

AI summary Table 56 outlines unitary peak demand savings values for mini-split heat pumps, including parameters such as heating capacity, coefficient of performance, and conversion factors. The peak demand-to-energy ratio of 0.283 MW/GWh is referenced from the 2024-2025 DSM MA.

Prescriptive Measures For prescriptive measures, specific unitary peak demand savings values are used. The detailed calculations are presented in the 2024-2025 DSM MA, and the values of each measure for which there was an update are presen...

AI summary Prescriptive measures use specific unitary peak demand savings values. The 2024-2025 DSM MA provides detailed calculations, with updates presented in Table 57. For stoves and fireplace inserts, the unitary peak demand savings value for stoves from Green Heat is applied as HEA does not track these items separately.

Table 57: 2024 HEA Tracked and Evaluated Unitary Peak Demand Savings for Prescriptive Measures Measure Tracked Savings [W] Evaluated Savings [W] Wood or pellet stoves or fireplace inserts with electric resistance or heat pump baseline 4,00...

AI summary Table 57 presents tracked and evaluated unitary peak demand savings for prescriptive measures under the 2024 Home Energy Assessment (HEA). The table includes data for wood or pellet stoves and fireplace inserts with electric resistance or heat pump baselines, showing tracked savings of 4,000 W and evaluated savings of 828 W.

23.2.6 Effective Useful Life As part of 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculation of electrical energy savings that are expected to persist over time. For the electrical energy savings that...

AI summary The Evaluator reviewed Effective Useful Life (EUL) values for electrical energy savings in the 2024-2025 DSM MA, calculating a weighted average for building envelope and heating measures, and directly applying EUL values for prescriptive measures.

Table 60: Evaluated 2024 HEA Gross Energy and Peak Demand Savings Measure Category HOT2000 Modelled Measures Wood Burning Equipment HPWHs Electrical Thermal Storage Energy Efficiency Measure Subtotal Solar PV Measures Total Number of Parti...

AI summary Table 60 presents evaluated 2024 Home Energy Assessment (HEA) gross energy and peak demand savings, including data on the number of participants, installed capacity, energy savings with and without adjustment ratios, and peak demand savings across various measures and technologies.

Table 67: 2024 Savings Overlap to Deduct from HEA Overlap Net Energy Savings at Generator (GWh) Net Peak Demand Savings at Generator (MW) Overlap with Green Heat (0.013) (0.007) Overlap with EPI (0.051) (0.009) Total (0.064) (0.017) 23.3.6...

AI summary Table 67 shows the overlap of energy savings from various programs, including Green Heat and EPI, which are being deducted from the Home Energy Assessment (HEA). The net energy savings and peak demand savings are negative, indicating overlaps that need to be accounted for in the evaluation of net savings.

23.4 Realization Rate [Table](#page-169-1) 69 below compares the electrical energy and peak demand savings established through this evaluation to those calculated in the 2024 tracking sheet. It also includes the realization rate, represent...

AI summary This section compares electrical energy and peak demand savings from the current evaluation with those in the 2024 tracking sheet, including the realization rate, which is the ratio of evaluated net savings to tracked net savings for both energy and peak demand.

2024 HEA-Finding: Parameters used to calculate unconverted D assessment spillover were reassessed and remain relatively stable. As part of the 2024 evaluation, the Evaluator compiled the results of 19 site visits completed by EAs among sur...

AI summary The 2024 HEA evaluation reassessed parameters for unconverted D assessment spillover using 19 site visits by Energy Auditors (EAs) on expired participants. While falling short of the 30-visit target, results were deemed consistent and sufficient. E1 may seek additional visits in 2025 for higher precision.

26 MHEEP Evaluation Approach The 2024 MHEEP evaluation comprised a condensed impact evaluation. The main objectives of the 2024 MHEEP evaluation were as follows: › Calculate gross and net MHEEP results, namely electrical first-year and lif...

AI summary The 2024 MHEEP evaluation focused on calculating gross and net results, including energy savings, peak demand savings, and avoided GHG emissions. The evaluation objectives are outlined in a table and mapped to research questions and methods.

Where: - › and correspond to the modelled energy consumption levels respectively obtained in HOT2000 during the pre-retrofit (D) and post-retrofit (E) assessments. When modelled energy consumption levels were not available, the participant...

AI summary The text outlines methods for calculating energy savings using HOT2000 and EnerGuide ratings, unitary savings values for HPWHs and DWHRs, adjustment ratios (ARs) derived from billing analyses, and prescriptive measure calculations from MHEEP. ARs vary by heating system scenarios and are based on 2024 HEA evaluations.

Non-modelled Measures Replaced appliances as well as programmable and smart thermostats are not modelled in HOT2000. Instead, resulting energy savings are calculated based on unitary energy savings values from the 2024-2025 DSM MA. That do...

AI summary Non-modelled measures such as replaced appliances and thermostats are not included in HOT2000. Energy savings for these measures are calculated using unitary values from the 2024-2025 DSM MA. For HPWHs and DWHRs, savings are adjusted using an AR before being added as prescriptive measures. This approach accounts for low installation rates and supports future savings tracking.

27.2.4 Effective Useful Life As part of the 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculation of electrical energy savings that are expected to persist over time. The EUL values for building envelop...

AI summary The Evaluator reviewed Effective Useful Life (EUL) values for building envelope upgrades and space heating equipment as part of the 2024-2025 DSM MA update. EUL values were not revised for most measure categories, except for heat pump water heaters and modelled measures, as reflected in Table 74.

The modelled and non-modelled measure annual gross savings at the meter are presented in [Table](#page-181-1) 75 and [Table](#page-182-0) 76 below respectively. [Table](#page-183-0) 77 further below combines the savings of all MHEEP measur...

AI summary The text discusses the presentation of annual gross savings for modelled and non-modelled measures under the MHEEP program, with specific tables referenced for detailed data on energy and peak demand savings.

Number of Participants 192 Energy Savings Gross Energy Savings Without Adjustment Ratio (AR) – at the Meter (GWh) Gross Energy Savings with AR – at the Meter (GWh) 0.632 0.361 Effective Useful Life (years) 20.5 Gross Lifetime Energy Saving...

AI summary The document presents energy savings data from the MHEEP Non-modelled Measure for 2024, including gross energy and peak demand savings from appliance replacements and drain water heat recovery systems. The data includes metrics such as effective useful life, unitary energy savings, and gross lifetime energy savings.

Table 77: Evaluated 2024 MHEEP Gross Energy and Peak Demand Savings Measure Category Modelled Non-modelled Total Energy Savings Gross Energy Savings – at the Meter (GWh) 0.361 0.005 0.366 Line Loss Factor 1.0947 1.0947 - Gross Energy Savin...

AI summary Table 77 evaluates the 2024 MHEEP Gross Energy and Peak Demand Savings, showing modelled and non-modelled energy and peak demand savings at both the meter and generator levels. The table includes metrics such as Effective Useful Life and Line Loss Factor, with footnotes explaining the calculation of non-modelled measure lifetimes.

27.3.1 Evaluated Net Savings Net savings are defined as the energy savings specifically attributable to MHEEP. Since spillover and freeridership effects were considered nil, the net MHEEP impacts are equal to the gross savings generated by...

AI summary The MHEEP program achieved net energy savings of 0.401 GWh and peak demand savings of 0.481 MW, with no spillover or free-rider effects. It missed its energy target by 35% but exceeded peak demand savings by 189% as shown in Figure 55.

27.4 Realization Rate A comparison of the gross and net energy and peak demand savings values established through this evaluation and those tracked by E1 is presented in [Table](#page-184-1) 79 below. The table also includes the realizatio...

AI summary This section compares gross and net energy and peak demand savings values from the evaluation with those tracked by E1, and presents the realization rate, which is the ratio of evaluated net savings to tracked net savings for both energy and peak demand savings.

Gross Savings NTGR Net Savings Realization Rate Value Unit Value Unit Energy Savings Tracked Savings by E1 0.578 GWh 1.00 0.578 GWh Evaluation Results 0.401 GWh 1.00 0.401 GWh 69% Peak Demand Savings Tracked Savings by E1 0.420 MW 1.00 0.4...

AI summary This table compares tracked and evaluated energy and peak demand savings from the Mi'kmaw Home Energy Efficiency Project (MHEEP) in 2024. Tracked savings by E1 are compared to evaluation results, showing a realization rate of 69% for energy savings and 115% for peak demand savings.

28 MHEEP Key Findings and Recommendations As mentioned previously, the main objectives of the 2024 MHEEP evaluation were as follows: › Calculate gross and net MHEEP results, namely electrical first-year and lifetime energy savings, peak de...

AI summary The 2024 MHEEP evaluation found that net electrical energy savings fell short of targets (65% achieved vs. 95% in 2023), while peak demand savings exceeded targets. Higher participation (19% increase) was offset by a 41% drop in average savings per participant due to updated adjustment ratios. Evaluated savings were 31% lower and 15% higher than E1's initial tracking for energy and peak demand, respectively.

29 Residential Behaviour Overview This section describes the Residential Behaviour program component, follows up on past evaluation recommendations, and provides an overview of participation history.

AI summary This section outlines the Residential Behaviour program, its follow-up on past evaluations, and provides an overview of participation history within the program.

29.1 Description The Residential Behaviour program component, publicly branded as Efficiency Insights, is designed to help Nova Scotia Power (NS Power) residential customers reduce their electricity consumption. The component provides a su...

AI summary The Residential Behaviour program, branded as Efficiency Insights, helps NS Power customers reduce electricity use via personalized Home Energy Reports (HERs) and behavior tips. E1, using Bidgely's algorithms, integrates HERs into NS Power's MyEnergy Insights platform. Energy Solutions Advisors (ESAs) assist customers, and savings are evaluated annually through billing analysis. Funded under E1's 2023-2025 DSM Plan, the program launched in May 2024 with four reports issued in 2024.

29.3 Participation History Residential Behaviour is an opt-out program component, which means that E1 enrolls customers, and customers remain in the program until they opt out online or by contacting E1. The targeted population for Residen...

AI summary Residential Behaviour is an opt-out program component managed by E1, targeting NS Power residential customers with specific rate codes. Participants are enrolled by default and can opt out. The program population was divided into control and treatment groups, with three waves based on electricity usage levels. Data on participant numbers is provided in a table.

Table 80: 2024 Treatment and Control Group Sizes Wave Population Size (N) Treatment Group Size Control Group Size Group Sizes at the Time of Selection 1 – High users 119,132 101,273 17,859 2 – Medium users 103,752 93,233 10,519 3 – Low use...

AI summary The table presents the treatment and control group sizes for residential behavior programs in 2024, showing attrition due to account closures, participant movement, and customers switching to solar rate codes. These customers are removed from the study as they no longer provide valid billing analysis comparisons.

30 Residential Behaviour Evaluation Approach The 2024 Residential Behaviour evaluation consisted of a comprehensive impact evaluation using a billing analysis. The main objectives of the 2024 Residential Behaviour evaluation were as follow...

AI summary The 2024 Residential Behaviour evaluation aimed to calculate electrical first-year energy savings and avoided GHG emissions through a billing analysis. Research questions were identified to achieve these objectives, with methods outlined in Table 81.

Table 81: 2024 Residential Behaviour Evaluation Approach Evaluation Objectives Research Questions Methodology Calculate net results › What are the evaluated first-year net electrical energy savings? › Are the participation levels in other...

AI summary Table 81 outlines the 2024 Residential Behaviour Evaluation Approach, focusing on calculating net results through billing analysis, participation analysis in other E1 programs, and GHG emission reduction calculations.

31 Residential Behaviour Impact Evaluation The objectives of the 2024 Residential Behaviour impact evaluation were to determine net electrical energy savings. The savings calculation methodology for Residential Behaviour is based on evalua...

AI summary The 2024 Residential Behaviour Impact Evaluation aimed to quantify net electrical energy savings using a randomized controlled trial (RCT) methodology, aligning with industry best practices and evaluation protocols to ensure unbiased savings estimates through comparison of treatment and control groups.

31.1 Tracking Sheet Audit Considering Residential Behaviour relies on a random selection of treatment group participants among all residential customers, the program component does not have a tracking sheet. Therefore, no tracking sheet au...

AI summary The Residential Behaviour program component does not require a tracking sheet audit because it uses random selection of participants from all residential customers, eliminating the need for targeted tracking sheets.

31.2 Net Savings For Residential Behaviour, savings correspond to the change in electricity consumption resulting from behaviours adopted by treatment group participants compared with the change in electricity consumption observed among co...

AI summary Net savings for residential behavior programs are calculated by comparing electricity consumption changes between treatment and control groups, ensuring savings directly attribute to the program. The Uniform Methods Project (UMP) defines net savings as the difference in energy consumption with and without the program, excluding control group changes. No additional freeridership adjustments are needed, but overlap with other ENS programs must be considered.

31.2.1 Treatment and Control Group Selection and Equivalency Check To yield accurate and unbiased results when calculating savings under a RCT approach, the treatment and control groups must be selected properly so that the two groups are...

AI summary The document discusses the selection and equivalency check of treatment and control groups in a randomized controlled trial (RCT) approach for residential behavior programs. The Evaluator ensured groups were randomly selected and statistically equivalent by analyzing energy consumption data and geographical locations before program launch.

Table 82: Average Daily Electricity Consumption Values for the Pre-program Period Wave Group Average Daily Consumption (kWh) Variation Between Control and Treatment Groups Control 46.2070 1 – High users Treatment 46.2437 0.08% 2 – Medium u...

AI summary Table 82 shows minimal differences in average daily electricity consumption between control and treatment groups across different user categories. This suggests that the Evaluator effectively matched households based on consumption data, ensuring similar consumption patterns in both groups.

31.2.2 Installation Rates Considering a billing analysis is used to calculate Residential Behaviour savings, the savings already take into account installation rates. Therefore, no additional installation rates needed to be applied.

AI summary The text states that installation rates are already factored into Residential Behaviour savings calculations via billing analysis, eliminating the need for additional installation rate applications.

Table 84: 2024 Evaluated Cumulative Electrical Energy Savings Parameters and Results Cumulative Savings Wave 1 – High users Initial Number of Active Participants (Treatment Group) 97,368 Attrition Rate (%) 5.2% Total Number of Treatment Da...

AI summary Table 84 presents the 2024 evaluated cumulative electrical energy savings from three waves of participants in a demand-side management program. It shows the number of participants, attrition rates, average daily consumption and savings, and total savings in gigawatt-hours for high, medium, and low users.

31.2.4 Peak Demand Savings No peak demand savings targets were set for Residential Behaviour and no peak demand savings were calculated as part of the 2024 evaluation.

AI summary No peak demand savings targets were established for residential behavior programs, and no corresponding savings were quantified in the 2024 evaluation, indicating a gap in residential demand management efforts within the regulatory framework.

31.2.6 Effective Useful Life For Residential Behaviour, energy savings are assessed annually through a billing analysis that serves to calculate the change in electricity consumption between the program evaluation year (post-program period...

AI summary The text explains that residential behavior programs in Nova Scotia use an Effective Useful Life (EUL) of one year for energy savings calculations, based on annual billing analysis comparing pre- and post-program periods. This approach aligns with practices in other jurisdictions like Massachusetts and Illinois.

Table 85: Other 2024 Program Participation Levels Program Treatment Participation Level Control Participation Level Difference (%) Is the Difference Statistically Significant? Wave 1 – High users HEA 1.6% 1.7% -0.1% No Green Heat 0.4% 0.3%...

AI summary Table 85 shows participation levels for various energy efficiency programs in 2024, comparing treatment and control groups across different user categories. The data indicates that some programs show statistically significant differences in participation, while others do not.

Net electrical energy savings are defined as the energy use reductions that are specifically attributable to Residential Behaviour. Program component net electrical energy savings were estimated using the following equation: Net Savings =...

AI summary The document defines net electrical energy savings as reductions specifically attributable to residential behavior and calculates the resulting GHG emission reductions using a Nova Scotia-specific factor. Net savings amounted to 2,961 tonnes of CO2 eq annually.

Table 86: Evaluated 2024 Residential Behaviour Net Electrical Energy Savings Cohort Wave 1 Wave 2 Wave 3 Total Energy Savings Energy Savings (GWh) 3.051 2.837 0.781 6.669 Savings Deductions (GWh) 0.354 0.044 0.000 0.399 Net Energy Savings...

AI summary Table 86 presents the evaluated 2024 residential behaviour net electrical energy savings, showing energy savings across three waves and a total. The data for the Nova Scotia-specific factor was sourced from Nova Scotia Power and Emera Inc. As of the time of writing, 2024 data were not yet available.

31.3 Realization Rate No tracked savings were calculated for Residential Behaviour; therefore, there is no realization rate.

AI summary No tracked savings were calculated for Residential Behaviour, resulting in the absence of a realization rate. This indicates that energy efficiency initiatives targeting residential behavior did not generate measurable savings for evaluation purposes.

32 Residential Behaviour Key Findings and Recommendations The main objectives of the 2024 Residential Behaviour evaluation were as follows: › Calculate net results, namely electrical first-year energy savings, as well as avoided GHG emissi...

AI summary The 2024 Residential Behaviour evaluation aimed to calculate electrical first-year energy savings and avoided GHG emissions. The Evaluator provided key findings but stated no recommendations for Residential Behaviour programs.

2024 Residential Behaviour-Finding: Residential Behaviour fell short of its net electrical energy savings targets. Residential Behaviour achieved 6.270 GWh in net electrical energy savings in 2024, thus falling short of the planned net ele...

AI summary Residential Behaviour program underperformed in 2024, achieving 6.270 GWh vs target 8.000 GWh, but shows potential as it scales. Control and treatment groups were similar, and treatment group had higher participation in other programs.

Table 87: Overall 2024 Existing Residential Participation and Evaluated Savings Particip ation Level Gross Savings NTGR Net S Savings Value Unit Value Unit Value Value Unit AMH • Energy Savings 83 Projects 1.159 GWh 1.00 1.159 GWh Lifetime...

AI summary Table 87 presents data on residential participation and evaluated savings for 2024, including energy savings, GHG emission reductions, and EUL for various programs like AMH, ASFH, EPI, Green Heat, HEA, MHEEP, and Residential Behaviour. The table also includes net savings ratios (NTGR) for each program component.

Residential Behaviour Appendix XXXVI Residential Behaviour: Monthly Savings Approach Appendix XXXVII: Residential Behaviour 2024 Recommendations PROJECT NO. 6562 2475, Laurier boul., Suite 250 Quebec City, QC G1T 1C4 Canada Tel.: 418-692-2...

AI summary The document includes appendices on residential behavior programs, focusing on monthly savings approaches and 2024 recommendations, as part of Project No. 6562.

Table 1: 2024 AMH Corrected Tracked Savings Value Tracked by E1 Corrected Tracked Value Relative Difference Program Component Results Value Unit Value Unit Value AMH Gross Electrical Energy Savings at the Generator 1.132 GWh 1.158 GWh 2.30...

AI summary Table 1 shows the 2024 AMH Corrected Tracked Savings, highlighting differences between tracked and corrected values due to an incorrect line loss factor used by E1 for residential participants. The relative difference in gross electrical energy savings is 2.30%, while peak demand savings remain unchanged.

APPENDIX II AMH Energy Auditor Interview Guide

AI summary This document is an interview guide for energy auditors in the Affordable Multifamily Housing (AMH) program, part of a Nova Scotia regulatory proceeding. It outlines procedures for assessing energy efficiency in multifamily housing, aligning with broader demand-side management (DSM) and energy efficiency initiatives.

D. Challenges/Barriers to Completion - D1. In your experience, what reasons would lead someone who completed an energy audit of their building to choose not to go ahead with the recommended upgrades and discontinue their participation in t...

AI summary The section outlines open-ended questions about barriers to program completion, including reasons participants might discontinue upgrades post-audit, challenges faced, requested support (e.g., better communication), and strategies for Efficiency Nova Scotia to improve participation rates.

[RECORD FROM DATABASE: MEASURE TYPE] - 1. [IF MEASURE CATEGORY = HP-ONLY] Heat pump participants [CHECK QUOTA AND CONTINUE] - 2. [IF MEASURE CATEGORY = MODELLED] Modelled participants (participant had a home energy assessment) [CHECK QUOTA...

AI summary The document outlines conditional processing rules for measure types in a regulatory proceeding, distinguishing between heat pump participants and modelled participants (those with home energy assessments). It includes a database check for the Oil to Heat Pump Affordability (OHPA) program, though no specific arguments or entities are explicitly discussed.

APPENDIX X ASFH Delivery Agent Interview Guide

AI summary This document outlines an interview guide for Delivery Agents (DAs) involved in the Affordable Single-family Homes (ASFH) program, focusing on their role in implementing energy efficiency initiatives and assessing program effectiveness.

Table 1: 2024 ASFH Corrected Tracked Savings Program Component Result Value Tracked by E1 Corrected Tracked Value Relative Difference Value Unit Value Unit Value Modelled Measures Gross Energy Savings at the Generator 4.758 GWh 4.635 GWh -...

AI summary Table 1 presents corrected tracked savings for the 2024 Affordable Single-family Homes (ASFH) program, showing a decrease in both modelled and non-modelled measures due to adjustments in calculation methods and overestimation corrections. The differences between tracked and corrected values are attributed to changes in the calculation approach by EfficiencyOne (E1), particularly for non-modelled heat pumps.

This appendix summarizes all the recommendations made by the Evaluator as part of the 2024 ASFH evaluation. Section Recommendations Executive Summary Recommendation #1: To assess progress with participant wait times, E1 could track wait ti...

AI summary The appendix outlines seven recommendations from the Evaluator for improving the 2024 ASFH evaluation, focusing on participant experience, program clarity, communication, and administrative efficiency for delivery agents and contractors.

A. Verification - A1. I am going to read you a list of energy-efficient products that, according to our records, were installed in your home by the Efficient Product Installation Service. For each one, can you please confirm that this prod...

AI summary This section outlines a verification process to confirm installation of energy-efficient products (e.g., LED bulbs, insulation, smart thermostats) via the Efficient Product Installation Service. Respondents are asked to validate each item, with options to confirm, deny, or indicate uncertainty. The process includes instructions for data recording and termination if all responses are negative.

C. Satisfaction C1. Using a scale from 1 to 10 where 1 is "not at all satisfied" and 10 is "completely satisfied," how satisfied are you with the Efficient Product Installation Service overall? [DO NOT ACCEPT A RANGE] 1-10 SCALE WITH END P...

AI summary The section evaluates customer satisfaction with the Efficient Product Installation Service (EPI) through 1-10 and 1-4 scales, exploring reasons for satisfaction or dissatisfaction, focusing on energy bill savings, service quality, and product effectiveness.

[ASK [E4,](#page-106-1) [E5,](#page-107-0) [E6](#page-107-1) SEQUENCE IN ORDER/DO NOT RANDOMIZE; REPEAT SCALE IF NECESSARY] - E4. Without the Efficient Product Installation Service, how likely would you have been to take the initiative to...

AI summary The text presents survey questions assessing the impact of the Efficient Product Installation Service (EPI) on consumer behavior regarding LED bulb adoption. Respondents are asked about likelihood of purchasing, delaying replacement, and quantity purchased without EPI, evaluating its role in promoting energy efficiency.

[VOLUNTEERED] - 98. Don't know - 99. Refused - E8. [IF [E7=](#page-107-2)YES] Just to confirm: Have I understood correctly that you had already made the decision to purchase and install smart thermostats in your home before you learned abo...

AI summary Survey questions assess customer behavior regarding smart thermostat purchases and the Efficient Product Installation Service (EPI), including whether respondents would have bought thermostats without EPI and pre-existing decisions to install them.

[ASK [E10,](#page-108-0) [E11,](#page-108-1) [E12](#page-109-0) SEQUENCE IN ORDER/DO NOT RANDOMIZE] - E10. Without the Efficient Product Installation Service, how likely would have been to take the initiative to purchase small thermostats...

AI summary This question (E10) assesses customer behavior regarding thermostat installation without the Efficient Product Installation Service (EPI), asking respondents to self-report likelihood of purchasing and installing small thermostats independently.

[ASK [E16,](#page-110-0) [E17,](#page-110-1) [E18](#page-111-1) SEQUENCE IN ORDER/DO NOT RANDOMIZE] - E16. Without the efficient product installation service, how likely would you have been to take the initiative to purchase at a store and...

AI summary This survey question (E16) asks respondents about their likelihood of purchasing and installing a domestic hot water (DHW) measure without the Efficient Product Installation Service (EPI), with response options ranging from 'definitely would have' to 'definitely would not have'.

Table 1: Free-Ridership - LEDs Previous Algorithm n 2024 AI gorithm E5 [IF E4 ≥ 5] If there was no Efficient Product Installation Service, how likely would you have been to postpone by at least one year replacing your bulbs with LEDs? 1) A...

AI summary This table evaluates free-ridership related to LED bulb installations through the Efficient Product Installation Service. It uses a scale to measure the likelihood of customers postponing LED bulb replacements or purchasing fewer bulbs without the service.

Table 1: Jurisdictional Scan Findings Jurisdiction Program Entity Offering the Program Energy Efficiency Measures Source Canadian Direct-install Programs British Columbia Energy Conservation Assistance Program (ECAP) Fortis BC LED light bu...

AI summary Table 1 presents findings from a jurisdictional scan of energy efficiency programs in Canada, focusing on direct-install programs in British Columbia and Manitoba. It lists various energy efficiency measures offered by entities such as Fortis BC, BC Hydro, and Efficiency Manitoba.

APPENDIX XXIV Green Heat Detailed Methodology and Billing Analysis

AI summary Appendix XXIV outlines the methodology and billing analysis for the Green Heat initiative, focusing on energy efficiency programs, appliance retirement, and heat pump adoption in Nova Scotia. It includes technical details on cost recovery, program evaluation, and regulatory considerations for residential and commercial energy efficiency measures.

Background After inconclusive results in 2023, a billing analysis was again conducted for Green Heat as part of the 2024 DSM evaluation to obtain measured electrical energy savings generated through the installation of mini-split heat pump...

AI summary A 2024 billing analysis for Green Heat evaluated energy savings from mini-split heat pumps (MSHPs) and wood/pellet stoves, updating prior results and using AMI data for improved accuracy. MSHPs are the primary measure, with stove installations analyzed for the first time via billing rather than modeling.

Table 1: Details on the Point System Used for the Selection of Control Participants Parameters Points Average daily consumption of treatment and control candidates after their Add a maximum of 2 points based on a function of the relative d...

AI summary Table 1 outlines a point system for selecting control participants based on average daily consumption differences between treatment and control groups. Points are awarded based on the relative difference, with up to 2 points for a 10% difference and 0 points for a 50% or greater difference. Each participant had different pre and post-program periods, and control participants were matched to treatment participants for billing analysis.

Energy Savings Calculation Figure 1 below illustrates how electrical energy savings were obtained by calculating the difference in the treatment group normalized annual consumption of each participant for the year before and after program...

AI summary The text describes energy savings calculations using a difference-in-differences approach with AMI data, normalizing consumption before/after program participation and adjusting for control group trends. Unitary savings values are calculated per MSHP capacity and per unit for wood/pellet stoves.

2024 Oregon Billing Analysis This billing analysis[3](#page-180-0) conducted in 2024 covered MSHPs installed in Oregon and incentivized by Energy Trust from 2020 to 2022. Energy Trust used an in-house billing analysis tool named the Reside...

AI summary The 2024 Oregon Billing Analysis evaluated the energy savings from MSHPs installed between 2020 and 2022 using a difference-in-differences approach and site-level regression models. Energy Trust used the REBA tool for the analysis, and results are compared with Econoler's methodologies and presented in Table 4.

Table 5: Econoler and Guidehouse Savings Results Scenario Number of Participants Average Savings (kWh/tone) Displacement Type Sample Electrical Savings (kWh/tonne) Econoler Green Heat Results Guidehouse Results Fully electric 99 1,026 n/a...

AI summary Table 5 presents savings results from Econoler and Guidehouse, showing average electrical savings from green heat initiatives. The fully electric scenario had 99 participants with an average savings of 1,026 kWh/tonne, while the part-electric scenario had 24 participants with a negative saving of -15 kWh/tonne. The overall sample size was 123 participants, with an average savings of 799 kWh/tonne for PD displacement type.

Table 6: Econoler and Cadmus Savings Results Scenario Sample Average Savings per Capacity (kWh/Btu/h) Baseline Heating System Sample Electrical Savings (kWh/Btu/h) Econoler Green Heat Results Cadmus Results Fully electric 99 0.0855 Electri...

AI summary Table 6 presents the savings results from Econoler and Cadmus for different heating scenarios. The data includes average savings per capacity and electrical savings, with notes indicating that some baseline heating systems were not electric resistance and that savings were estimated rather than directly measured.

› For the differences in demand savings: - › The same explanations as for energy savings - › Correction of formula for biomass measures, which rounded the savings at the kW instead of the W for each participant - › Correction of some heat...

AI summary The text outlines three corrections related to demand savings: reusing energy savings explanations, fixing a biomass measure formula rounding error from kW to W, and updating heat pump specs via NEEP cross-reference. These adjustments aim to improve accuracy in demand-side management calculations.

Background After inconclusive results in 2023, a billing analysis was again conducted for HEA as part of the 2024 DSM evaluation to obtain measured electrical energy savings generated through the installation of energy efficiency upgrades....

AI summary The 2024 DSM evaluation shifted from using overestimation ratios (ORs) based on space heating data to adjustment ratios (ARs) derived from AMI data for whole-home consumption. This change addresses challenges posed by increased heat pump adoption and simplifies calculations by directly applying ARs to modelled savings estimates.

Control-Treatment Participant Matching To determine the best suited control group participant for each treatment group participant, the Evaluator first applied a set of minimum conditions with which to comply: - › The E assessment date of...

AI summary The Evaluator used a set of minimum conditions to match control group participants with treatment group participants, including assessment date gaps, identical weather stations, and heating types. A point system was then applied to identify the ideal control participant for each treatment participant.

Detailed Results Once the main steps of the billing analysis were completed, the Evaluator conducted several analyses to interpret the data. The three scenarios used to establish ORs, associated with the presence of a heat pump or not, rem...

AI summary The Evaluator conducted analyses after completing the billing analysis, focusing on scenarios with and without heat pumps. Survey results showed that participants who added heating or floor area had lower savings, indicating the need to account for such changes. Econoler recommended using average consumption values from the entire control group for more accurate results.

Table 3: Evaluated Adjustment Ratios by Heat Pump Scenario Scenario AR Margin of Error A participant who registered with a heat pump 1.24 57.7% A participant who registered without a heat pump and who did not have one installed 0.58 13.2%...

AI summary Table 3 evaluates adjustment ratios (AR) for different heat pump scenarios, showing significant variations in margin of error. The highest margin of error (93.0%) is for participants who registered without a heat pump and later had one installed, suggesting uncertainty in savings estimates for this scenario.

APPENDIX XXXII HEA Reporting Requirements HEA incentives originate from three sources of funding and are thus reported to different parties via the DSM evaluation and government-funded evaluation. The DSM evaluation is focused on reporting...

AI summary HEA incentives are funded by multiple sources and are reported differently depending on the funding type. EfficiencyOne updated equations in 2019 and 2024 to better account for energy savings from fuel switching and the distribution of savings between DSM-funded and government-funded programs. Solar PV measures are reported in the DSM evaluation report as they offset electricity use.

Table 1: Reporting Requirements for Different Energy Savings Scenarios[8](#page-13-0) Scenarios 1 2 3 4 Change in Overall Electrical Energy Consumption Increase Increase Decrease Decrease Change in Overall Non-electrical Energy Consumption...

AI summary Table 1 outlines the reporting requirements for different energy savings scenarios, including changes in electrical and non-electrical energy consumption, reporting types, and equations used to calculate DSM and government-funded savings. The rationale explains how savings are accounted for in each scenario to avoid double counting.

Table 1: 2024 MHEEP Corrected Tracked Savings Value Tracked by E1 Corrected Tracked Value Relative Difference Program Component Results Value Unit Value Unit Value MHEEP (Excluding Appliance Replacements) Gross Energy Savings at the Genera...

AI summary This table presents the corrected tracked savings for the 2024 MHEEP program, showing minor differences between the original tracked values and the corrected values. The adjustments were made due to a change in methodology to better account for electrical space heating percentages and the use of outdated unitary savings for certain appliance replacements.

APPENDIX XXXVI Residential Behaviour: Monthly Savings Approach While they are not used to claim savings, monthly savings were calculated to observe monthly trends and the ramp-up period in more detail. An equation similar to that of the cu...

AI summary The monthly savings approach uses a difference-in-difference (DiD) model to compare average daily consumption between treatment and control groups pre- and post-program participation, calculating monthly savings via a specified equation. This method tracks trends and ramp-up periods without directly claiming savings.

Table 1: 2024 Residential Behaviour Evaluated Electricity Monthly Energy Savings May 2024 June 2024 July 2024 August 2024 September 2024 October 2024 November 2024 December 2024 Wave 1 – High users Number of Active Participants (Treatment...

AI summary The table presents monthly energy savings data for residential participants in Nova Scotia from May to December 2024, categorized by user groups (high, medium, low). It shows average daily consumption, attrition rates, and savings percentages across different waves of participants, with savings being minimal or negative in some months.

APPENDIX XXXVII Residential Behaviour 2024 Recommendations The Evaluator made no specific recommendation as part of the 2024 Residential Behaviour evaluation. PROJECT NO. 6562 2475, Laurier boul., Suite 250 Quebec City, QC G1T 1C4 Canada T...

AI summary The Evaluator made no specific recommendation as part of the 2024 Residential Behaviour evaluation. The document references Project No. 6562, but no further details or claims are provided in the text.

EFFICIENT PRODUCT REBATES PROGRAM Final Report 2024 DSM EVALUATION March 26, 2025

AI summary The Final Report for the 2024 DSM Evaluation of Nova Scotia's Efficient Product Rebates Program assesses the program's performance in promoting energy efficiency. It highlights efforts to evaluate the program's impact on reducing energy consumption and achieving regulatory goals under Nova Scotia's energy efficiency initiatives.

DEFINITIONS Accuracy Reflects the proximity of measurements to the true value. Adjustment ratio The ratio of evaluated results to tracked results. This ratio expresses the adjustment made to tracked savings or other tracked values such as...

AI summary The text defines key terms used in the regulatory proceeding, including 'Accuracy,' 'Adjustment ratio,' and 'Available Demand Response Capacity.' These definitions are crucial for understanding how measurements and capacity calculations are evaluated in the context of energy efficiency and demand-side management programs.

EXECUTIVE SUMMARY This report presents the 2024 demand-side management (DSM) results of the Efficient Product Rebates program administered by EfficiencyOne (E1). This program is comprised of the Business Energy Rebates (BER) program compon...

AI summary The report details the 2024 demand-side management (DSM) results for EfficiencyOne's Efficient Product Rebates program, which includes the Business Energy Rebates (BER) program. BER offers prescriptive rebates and financing through Application Rebates (BER-AR) and Instant Rebates (BER-IR) to business, non-profit, and institutional (BNI) participants to reduce electricity consumption and demand.

Table 1: Summary of 2024 Efficient Product Rebates Program Evaluation Program Component Evaluation Type Impact Process Market Methodology BER Comprehensive (Instant Rebates) Condensed (Application Rebates) X X › Instant Rebates participant...

AI summary This table summarizes the evaluation of the 2024 Efficient Product Rebates Program, focusing on the Business Energy Rebates (BER) component. It outlines the evaluation types, including impact, process, and market, and details the methodologies used, such as participant surveys, distributor interviews, tracking sheet audits, and GHG emission reduction calculations.

BER Findings and Recommendations This subsection presents the key findings and recommendations from the 2024 BER evaluation. 2024 BER-Finding: BER net electrical energy savings almost reached the target of 39.747 GWh, falling short by less...

AI summary The 2024 BER evaluation found energy savings nearly met targets, with Instant Rebates participation dropping 26% due to LED market saturation, while Application Rebates grew by 18%. Satisfaction with Instant Rebates remained high, free-ridership decreased, and LED fixtures now dominate the market with near-total adoption.

INTRODUCTION EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (...

AI summary EfficiencyOne (E1) is an independent non-profit organization that delivers energy efficiency and demand-side management (DSM) programs in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. E1's 2024 Efficient Product Rebates program, part of the Business Energy Rebates (BER) component, is being evaluated by Econoler. The evaluation focuses on baseline definitions, savings calculation methodologies, and parameters such as net-to-gross ratios (NTGRs).

BER Application Rebates In 2024, 15,644 units were rebated through 262 projects that were implemented by 199 unique participant[s](#page-39-2) 2 under Application Rebates. [Figure](#page-40-0) 2 below illustrates how Application Rebates pa...

AI summary In 2024, 15,644 units were rebated through 262 projects under BER Application Rebates, with 18% more businesses participating than 2023. Lighting and motors accounted for 82% of energy savings, driven by a large project yielding 2.998 GWh. Energy savings per participant rose 16%.

2 BER Evaluation Approach The 2024 BER-AR evaluation included a condensed impact evaluation, while the BER-IR evaluation included a comprehensive impact evaluation as well as a process evaluation and market evaluation. The main objectives...

AI summary The 2024 BER evaluation aimed to collect participant and distributor perspectives, calculate gross and net BER results, and analyze the evolution of the BNI lighting market. It included both condensed and comprehensive impact evaluations, as well as process and market evaluations.

Table 5: 2024 BER Evaluation Approach Evaluation Objectives Research Questions Methodology Collect information on participant and distributor perspectives › How do participants become aware of BER-IR? › What is the level of satisfaction wi...

AI summary Table 5 outlines the 2024 BER Evaluation Approach, focusing on collecting participant and distributor perspectives, calculating gross and net results, and using methods such as surveys, interviews, and adjustment ratios. It also references the 2024-2025 DSM MA for evaluating energy savings and GHG emission reductions.

Efficient Product Rebates Program 8 Evaluation Objectives Research Questions Methodology Analyze the evolution of the BNI lighting market › How has the BNI lighting market evolved in Nova Scotia since 2021? › To what extent are LED product...

AI summary This section evaluates the evolution of the BNI lighting market in Nova Scotia since 2021, focusing on the availability of LED products compared to non-LED products and the implications of an updated baseline for E1's BNI lighting programs. The research methodology includes surveys, distributor interviews, and market characterization studies.

3.1 Participant Awareness and Decision to Purchase

AI summary This section discusses participant awareness and the decision-making process related to purchasing energy efficiency measures or programs. It likely addresses factors influencing consumer choices, program accessibility, and barriers to participation in initiatives like DSM or BER.

3.3 Distributor Satisfaction with Instant Rebates Distributors (n=10) were asked to express their level of satisfaction with various aspects of Instant Rebates using a 10-point scale where 1 means "Not at all satisfied" and 10 means "Very...

AI summary Distributors expressed high satisfaction with Instant Rebates, with an average score of 8.3 out of 10. Satisfaction with service support and communications was even higher at 9.3. However, some dissatisfaction arose due to the maturity of LED technology and time-intensive administrative tasks.

3.4 Service Influence on Distributors Similar to 2022, distributors of LED linear fixtures, LED linear lamps, and LED outdoor fixtures were questioned about the different strategies employed to sell each of these product categories in 2024...

AI summary Distributors in 2024 promoted LED products through strategies such as Instant Rebates, discounts, and training workshops, particularly for LED fixtures. These efforts were more common for LED fixtures than for LED lamps. Distributors noted limited availability of non-LED alternatives, which influenced their strategies.

Table 7: 2024 Distributor Sales Strategies Used for Qualifying LED Products Number of Distributors Using the Strategy (n=10) Distributor Sales Strategies LED Linear Fixtures LED Linear Lamps LED Outdoor Fixtures Promote the E1 rebate in an...

AI summary The table outlines the 2024 sales strategies used by distributors for qualifying LED products, highlighting the promotion of rebates, upselling LED products, and marketing efforts. Other strategies included bundled pricing, raffles, and training workshops, with some differences in focus between LED linear fixtures, lamps, and outdoor fixtures.

4.2.1 Adjustment Ratios The Evaluator applied adjustment ratios to the tracked gross savings to determine evaluated gross savings. These ratios were established using 2022 project review results, with the exception of the lighting measure...

AI summary Adjustment ratios were applied to tracked gross savings using 2022 project reviews, except for lighting measures using 2021 data. Solar PV projects had specific ratios (0.942 for RETScreen, 1.101 for PVWatts) not used by E1 in 2024, with a recommendation to adopt them for accuracy.

4.2.3 Effective Useful Life As part of the 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculations of electrical energy savings that are expected to persist over time. In 2024, for LED linear fixtures, L...

AI summary The Evaluator reviewed effective useful life (EUL) values for various efficiency measures as part of the 2024-2025 DSM MA activities. Adjustments were made for LED lighting products due to updated hours of use (HOU) values, while EUL values for other rebate measures remained unchanged from the 2021 evaluation.

Gross Savings NTGR Net Savings Realization Rate Value Unit Value Unit Energy Savings Tracked Savings by E1 15.526 GWh 0.74 11.489 GWh Evaluation Results 15.526 GWh 0.74 11.489 GWh 100% Peak Demand Savings Tracked Savings by E1 1.721 MW 0.7...

AI summary The table presents energy and peak demand savings tracked by E1, with NTGR values showing the ratio of net savings to gross savings for Application Rebates. The evaluation results show 100% realization for both energy and peak demand savings.

5.2.1 In-service Rates Research indicates that a percentage of measures purchased through rebate programs might be stored by customers for later use. As part of the 2024-2025 DSM Measure Assessment activities, the Evaluator maintained the...

AI summary Research suggests some rebate-program measures may be stored by customers. The Evaluator maintained 85% ISR for LED linear lamps and 100% ISR for fixtures/sensors, based on 2016 literature review during 2024-2025 DSM Measure Assessment.

5.2.2 Energy Savings As part of the 2024-2025 DSM MA update, the Evaluator reviewed the equation parameters such as the hours of use (HOU) used to determine the energy savings of measures eligible under Instant Rebates. In 2024, for LED li...

AI summary The Evaluator reviewed and updated energy savings parameters for LED fixtures and occupancy/motion sensors under the 2024-2025 DSM MA update. These updates were based on BER-AR and SBES values from 2021-2023, with results presented in tables.

Parameter Type of Occupancy Sensor Tracked Value [W] Updated Value [W] Unitary Peak Demand Interior Remote Ceiling or Wall-mounted 37.6 26.3 Savings (W) Fixture Mounted or Wall Switch 21.9 Exterior 31.1 35.3 5.2.4 Interactive Effects

AI summary The table presents the impact of occupancy sensors on unitary peak demand and energy savings, with data showing reductions in tracked values and updates for different sensor types. The section '5.2.4 Interactive Effects' likely discusses the combined effects of these sensors on energy efficiency and demand management.

5.2.5 Effective Useful Life As part of the 2024-2025 DSM Measure Assessment activities, the Evaluator reviewed the EUL values used in the calculations of electrical energy savings that are expected to persist over time. In 2024, for LED li...

AI summary The document discusses the 2024-2025 DSM Measure Assessment activities, focusing on the adjustment of Effective Useful Life (EUL) values for LED lighting fixtures due to HOU value updates, while other Instant Rebates measures retained their 2021 EUL values.

5.2.6 Evaluated Gross Savings The energy and peak demand savings associated with Instant Rebates were calculated using the unitary savings values (including baseline wattages, the actual wattages of efficient measures, ballast factors wher...

AI summary The document discusses the calculation of energy and peak demand savings from Instant Rebates using data from the 2024-2025 DSM Measure Assessment. Savings at the generator were calculated using weighted average line loss factors based on the distribution of rate code spending for other BNI programs in 2024, with references to the 2014 Cost of Service Study Progress Update submitted to the NSUARB.

Table 20: Evaluated 2024 Instant Rebates Gross Energy and Peak Demand Savings Measure Category LED Linear Fixtures LED Linear Lamps LED Outdoor Fixtures LED Directional and Architectural Fixtures Outdoor Motion Sensors Indoor Occupancy/ Mo...

AI summary Table 20 presents the evaluated 2024 instant rebates gross energy and peak demand savings for various measures, including LED fixtures, motion sensors, and circulator pumps, providing data on energy savings, demand savings, and associated factors like adjustment ratios and interactive effects.

[Figure](#page-65-0) 9 below compares the tracked and evaluated gross energy savings, and [Figure](#page-65-1) 10 further below compares the tracked and evaluated gross peak demand savings. The slight differences between evaluated gross el...

AI summary The text compares tracked and evaluated gross energy and peak demand savings from the 2024 Instant Rebates program. Differences in energy savings are attributed to updated HOU for LED measures and occupancy/motion sensors. GHG emission reductions are calculated using a Nova Scotia-specific factor applied to gross savings.

Table 23: 2024 Instant Rebates NTGRs Measure Free-ridership NTGR LED Linear Fixtures 8% 0.92 LED Linear Lamps 15% 0.85 LED Outdoor Fixtures 15% 0.85 Other Measures 0% 1.00 5.3.3 Evaluated Net Savings

AI summary Table 23 presents the 2024 Instant Rebates NTGRs for various energy efficiency measures, including free-ridership percentages and NTGR values. Section 5.3.3 discusses the evaluated net savings related to these rebates.

Table 24: Evaluated 2024 Instant Rebates Net Energy and Peak Demand Savings Measure Category LED Linear Fixtures LED Linear Lamps LED Outdoor Fixtures LED Directional and Architectural Fixtures Outdoor Motion Sensors Indoor Occupancy /Moti...

AI summary Table 24 evaluates the 2024 Instant Rebates program, detailing net energy and peak demand savings across various measure categories such as LED fixtures, motion sensors, and circulator pumps. It provides data on gross and net energy savings, line loss factors, and net lifetime energy savings, as well as peak demand savings at both the meter and generator levels.

Table 26: Comparison of 2024 BER Tracked and Evaluated Savings at the Generator NTGR Net Savings Value Unit Value Unit Realization Rate 47.777 GWh 0.81 38.557 GWh 46.852 GWh 0.84 39.529 GWh 103% 6.752 MW 0.81 5.479 MW 6.756 MW 0.85 5.746 M...

AI summary Table 26 compares the 2024 Business Energy Rebates (BER) tracked and evaluated savings at the generator level, showing net savings, gross savings, and realization rates for different rebate types. The table highlights metrics such as Net Total Generation Reduction (NTGR) and realization rates for Application Rebates and Instant Rebates.

e remaining retrofit opportunities once LED baseline is adopted for BER-IR. Recommendation #6 : Focus efforts on BER-AR to target market laggards for early replacement retrofits of their lighting. Recommendation #7: A LED baseline should a...

AI summary The text outlines recommendations for improving LED retrofit programs under BER-IR and BER-AR, emphasizing baseline adjustments for EUL, targeting market laggards, and tracking multi-wattage lighting products. It highlights evolving control products and the need for updated application processes to ensure accurate wattage tracking.

C. Participation and Decision to Purchase - C1. [ASK IF CONTRACTOR [A3=](#page-90-0)2] Who usually makes the decision to buy rather than standard ? Is it… [READ CODE 1-2 AND 96. SINGLE RESPONSE] . - 1. You or someone else in your organizat...

AI summary The document outlines a series of questions aimed at understanding decision-making processes related to purchasing energy-efficient products, such as LED lighting, and the factors influencing these decisions. It explores who makes the purchase decisions, whether customers seek advice, and the importance of various factors like energy efficiency, cost savings, and incentives.

[READ IF [E5=](#page-99-0)1] For the following statements, please indicate whether you agree or disagree. E6. [IF E5=1] Do you agree or disagree that Efficiency Nova Scotia promotional materials or communications were a major factor in the...

AI summary The text contains survey questions related to the impact of Efficiency Nova Scotia promotional materials on purchasing decisions for efficient products, specifically LED lighting. It also asks about the proportion of lighting projects involving rebate recipients.

C9. Did your distributor recommend to you? Distributor Recommended Product 2018 2019 2020 2021 2022 2024 Sample Size 49 60 51 50 50 50 Yes 51% 62% 71% 60% 62% 52% No 43% 33% 29% 40% 38% 40% Don't know 6% 5% - - - 8% C10. For what type of p...

AI summary The text presents survey data on distributor recommendations for efficient products, the types of projects for which these products were purchased, and the condition of existing fixtures or lamps when the products were acquired. The data spans multiple years and includes percentages of responses for each category.

G4. What was the most important reason you were not more satisfied with the program overall? Any other reasons? Most Important Reason Not More Satisfied with BER Overall 2019 2020 2021 2022 2024 Sample Size 7 (#) 12 (#) 5 (#) 7 (#) 2 (#) D...

AI summary The document presents survey results from participants in the Business Energy Retrofit (BER) program, highlighting reasons for dissatisfaction and suggestions for improvement. Key issues include rebate size, program consistency, paperwork, and communication, while suggestions focus on increasing rebates, simplifying processes, and improving marketing.

Table 1: Instant Rebates Distributor Influence Level Algorithm 2022 Algorithm 2024 Algorithm Question Answer Score Question Answer Score Influence 1 [ASK IF B1=1-6 OR 96 OR B2=2] I'd like to learn more about what influenced your organizati...

AI summary Table 1 outlines an algorithm used to assess the influence level of distributors in the context of instant rebates for efficient products. It includes questions and scoring mechanisms to evaluate factors affecting decision-making related to the sale of efficient products in 2022 and 2024.

Table 2: Instant Rebates Participant and Overall Free-ridership Algorithm 2022 Algorithm 2024 J Algorithm Question Answer Score Question Answer Score INTE NTION Cost Efficiency Nova Scotia offered a rebate for 1) Answer: (scale 0-10) Answe...

AI summary Table 2 outlines the Instant Rebates Participant and Overall Free-ridership Algorithm, comparing the 2022 and 2024 algorithms. It includes questions related to rebate participation, such as the likelihood of participating in a rebate program for purchasing efficient products, with scores based on responses.

To further supplement the market evaluation findings, the Evaluator conducted a jurisdictional scan to investigate similar programs to BER-IR. The Evaluator found very few instant discount programs targeted at the BNI sector. BC Hydro, Eff...

AI summary The Evaluator conducted a jurisdictional scan and found very few instant discount programs targeted at the BNI sector, with only the IESO program identified as similar to BER-IR, which requires replacing inefficient lighting.

Jurisdiction - Program Measures Baseline Information Notes Massachusetts – MassSave Instant Lighting Incentives Lamps are not incented. Controlled lighting products only – fixtures and troffer retrofit kits with controls. Past studies indi...

AI summary The text discusses lighting incentive programs in Massachusetts, Wisconsin, and Ontario, highlighting differences in eligibility criteria, baseline assumptions, and implementation rules. Massachusetts and Ontario have specific rules for controlled lighting products and retrofit kits, while Wisconsin excludes lighting from its instant discount program. Ontario's program excludes new construction and has unclear enforcement mechanisms.

CUSTOM INCENTIVES PROGRAM Final Report 2024 DSM EVALUATION March 25, 2025

AI summary The document presents the Final Report of the 2024 Demand-Side Management (DSM) Evaluation for Nova Scotia's Custom Incentives Program, dated March 25, 2025. It includes visual elements but no substantive textual analysis or findings.

DEFINITIONS Accuracy Reflects the proximity of measurements to the true value. Adjustment ratio The ratio of evaluated results to tracked results. This ratio expresses the adjustment made to tracked savings or other tracked values such as...

AI summary The text defines key terms related to energy efficiency and demand response. It explains 'accuracy' as the proximity of measurements to true values and 'adjustment ratio' as the ratio of evaluated results to tracked results. It also defines 'available demand response capacity' as the capacity available to Nova Scotia Power to reduce system peak demand, calculated based on specific events.

Table 2: Overall 2024 Custom Incentives Participation and Savings Particip ation Level Gross s Savings NTGR Net Savings Value Unit Value Unit Value Unit Value Custom • Energy Savings 49.825 GWh 0.80 39.951 GWh Lifetime Energy Savings 628.9...

AI summary Table 2 shows the participation and savings from the 2024 Custom Incentives program, which exceeded its targets for net electrical energy and peak demand savings. The program achieved 44.429 GWh in net energy savings and 5.793 MW in net peak demand savings, with Custom being the largest contributor.

Custom General Key Findings and Recommendations 2024 Custom-Finding: Custom achieved 39.951 GWh in net electrical energy savings and 5.225 MW in net peak demand savings at the generator in 2024, thereby surpassing the planned electrical en...

AI summary Custom achieved 39.951 GWh in net electrical energy savings and 5.225 MW in peak demand savings in 2024, exceeding targets by 48% and 28%, respectively. Retrofit and Building-Optimization participation declined, while New Construction increased. Adjustments to savings included ratios of 1.012–1.068 across services. Free-ridership dropped to 15% for Retrofit and 28% for New Construction. Evaluated savings were 7% higher than E1's tracked data.

New Construction Key Findings and Recommendations 2024 New Construction-Finding: The demand savings calculation approach used by E1 for projects modelled in eQuest can generate results that are difficult to confirm. 2024 New Construction R...

AI summary The 2024 findings highlight issues with E1's demand savings calculation methodology in eQuest models, the need for M&V in large industrial projects, increased MURB participation in HRM, participant satisfaction, non-participant awareness, and decarbonization as a motivator. Recommendations include revising demand savings scripts and updating program eligibility criteria.

EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (DSM) for Nova...

AI summary EfficiencyOne (E1) is a non-profit organization that provides energy efficiency and demand response services in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. E1's 2024 DSM program portfolio includes the Custom Incentives program, which was evaluated by Econoler. The evaluation focuses on baseline definitions, savings calculation methodologies, parameter values, and net-to-gross ratios.

Table 5: Implementation Status of Past Recommendations for Custom # Recommendations Status Comments 2022 Retrofit R2 Require measurement and verification (M&V) efforts based on the International Performance Measurement and Verification Pro...

AI summary The document discusses the implementation status of past recommendations related to custom programs, including the use of M&V efforts based on the IPMVP Option C approach for retrofit projects, revisions to model review processes, monitoring free-ridership, and updating logic models for Custom Retrofit. Progress and completion statuses are outlined with specific comments.

Retrofit In 2024, energy and/or demand savings were generated by a total of 88 Retrofit projects, including - › 75 projects completed in 2024 - › 61 single-year projects - › 14 multiyear projects - › 13 projects that claimed partial saving...

AI summary In 2024, 88 Retrofit projects generated energy and demand savings, with 75 completed projects achieving 17% higher average savings per project compared to 2023. Despite a 19% decrease in completed projects from 2023, overall gross energy savings increased by 24%, and peak demand savings rose by 35%. Compressed air leak audits, motor upgrades, and solar PV measures were the top contributors to savings.

Table 6: 2024 Custom Evaluation Approach Evaluation Objectives Research Questions Methodology Calculate gross results › Are the data in the tracking sheet complete, accurate, and consistent? › Are the gross savings calculated for a sample...

AI summary Table 6 outlines the 2024 Custom Evaluation Approach, focusing on calculating gross and net results, exploring opportunities to expand program presence, and updating program service logic. It includes research questions, methodologies, and evaluation objectives for various projects and programs.

Logic Model Update The Evaluator reviewed E1's most recent New Construction service logic model and theory of change and analyzed how well these address key barriers to reaching service objectives.

AI summary The Evaluator assessed E1's updated New Construction service logic model and theory of change, evaluating their effectiveness in addressing barriers to achieving service objectives.

Calculations Using Evaluation Results Building on all the above methods and collected data, the Evaluator calculated the first-year and lifetime energy and peak demand savings as per the calculation methodology presented in Sections [3,](#...

AI summary The Evaluator calculated first-year and lifetime energy and peak demand savings using methodologies outlined in Sections 3, 5, and 6. These calculations build on prior data collection and evaluation methods to quantify program impacts.

3.1 Tracking Sheet Audit To ensure service results were reliably compiled, the Evaluator first performed a tracking sheet audit aimed at verifying the completeness and consistency of the data submitted by E1. The verification and correctiv...

AI summary The Evaluator conducted a tracking sheet audit to verify the completeness and consistency of data submitted by E1, ensuring reliable service results. Corrective actions are detailed in Appendix IV, leading to corrected tracked savings results.

Compressed Air Leak Audit Project Review The Evaluator also conducted an in-depth review of one of two large compressed air leak audit projects completed by the same participant at two different facilities. These projects were conducted us...

AI summary The Evaluator reviewed a compressed air leak audit project using an innovative all-in-one platform, finding the process sound and reliable. Documentation and savings calculations were accurate, leading to an adjustment ratio of 1.0. Technical expertise from the OEM and participant staff ensured project quality, with no adjustments made to energy savings estimates.

3.2.5 Effective Useful Life The Evaluator reviewed the EUL values of all sampled projects by selecting an appropriate EUL for each measure implemented. The revised measure level EUL values were selected based on the values outlined in the...

AI summary The Evaluator adjusted Effective Useful Life (EUL) values for sampled projects based on the 2024–2025 DSM MA. Four Retrofit projects required weighted average EUL adjustments due to multiple measures, compressed air leak audit EULs were reduced to two years, and solar PV EULs were extended to 30 years aligning with DSM MA data.

Table 10: Example of a 2024 True-up Adjustment 2023 2024 Total Tracked Savings (kWh) 469,584 108,249 577,833 Year-specific Adjustment Ratio 1.017 1.021 Revised Savings Prior to True-up (kWh) 477,567 110,522 588,089 Total Project Revised Sa...

AI summary Table 10 provides an example of a 2024 True-up Adjustment, showing tracked savings, adjustment ratios, and revised savings for energy efficiency projects. Table 11 outlines overall evaluated gross savings for Retrofit, incorporating adjustment ratios and true-up adjustments to account for multiyear project completions in 2024.

enable this, the service could conduct an analysis to identify high-opportunity non-MURB sectors, including relevant stakeholders, and develop targeted outreach and engagement plans for these sectors. E1 program staff explained that popula...

AI summary The service aims to boost non-MURB participation in energy efficiency programs by analyzing high-opportunity sectors, engaging stakeholders like architects early, and hiring Energy Managers with non-MURB expertise. Challenges include limited data outside HRM and higher costs for smaller buildings, with public sector buildings seen as key opportunities outside HRM.

ty requirements and municipal building rules limit these opportunities to some degree, this information indicates that there may be some opportunity for the service to widen its reach outside the HRM. While insufficient modelling support c...

AI summary The text highlights opportunities to expand energy efficiency programs beyond the HRM, noting barriers like limited modeling support and lower program awareness in rural areas. Energy modellers recommend tailored outreach, in-person engagement, and targeted messaging to address rural builders' needs, such as familiarity with oil-based systems over heat pumps. Growth rates inside HRM (24% annually) outpace those outside (15% annually).

4.5 Modeller and Participant Suggestions for Improvements Participants shared a number of suggestions for improving Custom New Construction, including conducting outreach to developers and builders to inform them of available incentives; f...

AI summary Participants and modellers suggested improving the Custom New Construction program through targeted outreach to developers, tiered incentives for complex buildings, simplified processes, increased awareness via platforms like LinkedIn, and reduced barriers to entry. Modellers emphasized training, government engagement, and adjusting incentive levels to boost participation.

uilders, particularly with respect to the cost of energy modelling, which is required for the service. For clarity, the Evaluator recommends that this barrier be renamed "High energy modelling costs." The second barrier was found in the ev...

AI summary The Evaluator identifies two key barriers: high energy modelling costs for builders and limited developer awareness of efficiency opportunities. The first barrier is recommended to be renamed for clarity, while the latter is proposed as a logic model addition. Insufficient modeller participation is also flagged as a barrier.

ven the region's cold climate. The service could help to overcome this barrier by providing evidence to builders to address their lack of awareness on the performance of heat pumps and other measures. All modellers interviewed said clients...

AI summary The document discusses barriers to energy-efficient building practices, emphasizing the role of education and services to address builder awareness gaps. Energy modellers are critical in promoting efficiency, but current capacity is insufficient. E1 collaborates with modellers and developers to expand participation, offering training and increasing modeller numbers to meet demand.

Peak Demand Savings In previous evaluations, to establish peak demand savings, the Evaluator extracted the hourly results of the revised building energy model and averaged the monthly peak demand reduction for the months of December, Janua...

AI summary The Evaluator and E1 used different methods to calculate peak demand savings, with minor discrepancies observed. The Evaluator adopted E1's eQuest script method for 2024 and recommends investigating discrepancies to ensure consistent calculation methodologies across software. Adjustment ratios showed an average of 1.003 with a 6.9% margin of error.

5.2.4 Effective Useful Life The NC tracking sheet records savings on a measure-by-measure basis for each project. The Evaluator reviewed the EUL values of all measures in the tracking sheet as part of the Tracking Sheet Audit and selected...

AI summary The NC tracking sheet records savings measure-by-measure, with the Evaluator reviewing and selecting correct EUL values based on the 2024–2025 DSM Measure Assessment during the Tracking Sheet Audit.

Table 19: Evaluated 2024 New Construction Gross Energy and Peak Demand Savings Partial Savings Claimed Final Savings for Projects Fully Claimed in 2024 Total Number of Projects 1 27 28 Energy Savings Tracked Gross Energy Savings – at the M...

AI summary Table 19 evaluates the 2024 new construction gross energy and peak demand savings, providing data on tracked and adjusted energy and peak demand savings at the meter and generator levels. It also includes the effective useful life and lifetime energy savings, as well as line loss factors.

Gross Savings NTGR Net Savings Realization Rate Value Unit Value Value Unit Value Energy Savings Tracked Savings by E1 20.900 GWh 0.72 14.421 GWh Evaluation Results 21.495 GWh 0.72 15.476 GWh 107% Peak Demand Savings Tracked Savings by E1...

AI summary Evaluated energy and peak demand savings were higher than tracked savings due to adjustments in energy models and slightly lower free-ridership in 2024, which increased the realization rate.

6.2.1 Project Review Findings The Evaluator reviewed both completed Building Optimization projects. Following the review, the Evaluator revised the energy savings of one project, and the demand savings of both projects. - › One project inv...

AI summary The Evaluator reviewed two completed Building Optimization projects, adjusting energy savings upward for one and demand savings downward for both due to miscalculations in original assessments. No average adjustment ratio was established, with total savings calculated by summing individual project evaluations. Partial savings claims will require future reviews.

Table 27: Evaluated 2024 Building Optimization Net Energy and Peak Demand Savings Partial Savings Claimed Final Savings Claimed for Single Year Projects Total Number of Projects 2 2 4 Gross Energy Savings – at the Meter (GWh) 1.540 0.318 1...

AI summary Table 27 presents evaluated energy and peak demand savings from the 2024 Building Optimization program. It shows gross and net energy savings at the meter and generator, along with peak demand savings, effective useful life, and line loss factors for single-year projects and partial savings claimed.

6.4 Realization Rate A comparison of the energy and peak demand savings values established through this evaluation and those tracked by E1 is presented in [Table](#page-20-1) 28 below. It also includes the realization rate, representing th...

AI summary This section compares energy and peak demand savings values evaluated and tracked by E1, presenting a realization rate that measures the ratio of evaluated net savings to tracked net savings for both energy and peak demand savings.

7 Overall Savings for Custom A comparison of the energy and peak demand savings values established through this evaluation and those tracked by E1 is presented in [Table](#page-22-0) 29. The realization rate, representing the ratio of eval...

AI summary This section compares energy and peak demand savings values evaluated in the proceeding with those tracked by E1, showing a realization rate of 108% for energy savings and 105% for peak demand savings.

Table 29: Comparison of 2024 Custom Tracked and Evaluated Savings at the Generator Gross Savings NTGR Net Savings Realization Rate Value Unit Value Value Value Unit Energy Savings Tracked Savings by E1 26.115 GWh 0.79 20.719 GWh Evaluation...

AI summary Table 29 compares tracked and evaluated energy and peak demand savings from 2024 custom programs. It highlights differences between gross and net savings, along with realization rates, showing that evaluation results often exceed tracked savings, with realization rates ranging from 90% to 109%.

New Construction Key Findings and Recommendations 2024 New Construction-Finding: The demand savings calculation approach used by E1 for projects modelled in eQuest can generate results that are difficult to confirm. The Evaluator found min...

AI summary The Evaluator identified issues with E1's demand savings calculation approach for eQuest-modeled projects, noted discrepancies in industrial project savings claims, and observed increased MURB participation. Recommendations include revising E1's methodology for consistency, requiring M&V for large industrial projects, and aligning with market trends.

9 SEM Overview This section describes the Strategic Energy Management (SEM) program component, follows up on past evaluation recommendations, and provides an overview of participation history.

AI summary This section outlines the Strategic Energy Management (SEM) program, addresses past evaluation recommendations, and provides an overview of participation history within the program.

9.1 SEM Description SEM provides industrial and institutional participants with funding and support to implement energy management practices within their organizations. It also provides participants with energy management information syste...

AI summary SEM supports industrial and institutional participants in implementing energy management practices through funding, EMIS support, and structured approaches to achieve energy savings. Eligibility requires resources, commitment, and collaboration with E1. Activities are delivered by third-party providers, with options for extended participation and EMIS funding.

Figure 10: 2024 SEM Participation Process Summary Eligibility Check, Memorandum of Understanding (MOU), and Kick-off Meeting - Once approved, eligible participants must first sign a MOU that outlines the project scope, participant requirem...

AI summary The 2024 SEM Participation Process involves eligibility checks, MOUs, energy team formation, policy development, energy improvement events, and savings verification. Participants receive performance-based incentives ($0.04-$0.06/kWh) to achieve energy savings targets of 4.222 GWh and 0.470 MW. The process includes M&V, weekly calls, and energy management planning.

9.2 Follow-up on Past Evaluation Report Recommendations The Evaluator evaluated SEM in previous years and issued improvement recommendations. [Table](#page-31-0) 30 provides a summary of the implementation status of the recommendations pre...

AI summary The Evaluator assessed SEM in previous years and provided improvement recommendations. Table 30 summarizes the implementation status of these recommendations from the 2023 SEM Evaluation Report.

11 SEM Impact Evaluation The objectives of the 2024 SEM comprehensive impact evaluation were to determine project gross and net electrical energy and peak demand savings as well as annually avoided GHG emissions, EUL values, and associated...

AI summary The 2024 SEM comprehensive impact evaluation aimed to assess project energy and peak demand savings, GHG emissions reductions, EUL values, and lifetime electrical energy savings from Strategic Energy Management initiatives.

11.2 Gross Savings For SEM, gross savings correspond to the change in energy consumption resulting from actions taken by participants regardless of their reasons for participating.[39](#page-36-1) The subsections below provide a descriptio...

AI summary Gross savings for Strategic Energy Management (SEM) are defined as changes in energy consumption from participant actions, irrespective of motivation. The section outlines methodology for reviewing seven 2024 SEM projects, including assessments of interactive effects, Effective Useful Life (EUL) values, and revised electrical savings.

reporting period. The Service Provider selected a bottom-up or top-down approach in a manner that is consistent with the M&V procedure developed by the Service Provider and approved by the Evaluator. The M&V reports for each measure were c...

AI summary The M&V reports for energy efficiency measures were found thorough and aligned with best practices, using pre- and post-measurements. Compressed air leak repairs were validated using ultrasound detectors, and savings calculations used conservative compressor efficiency assumptions. No adjustments were needed as all projects followed the methodology correctly.

11.2.3 Effective Useful Life As part of the 2024-2025 Demand-side Management Measure Assessment (DSM MA)[40](#page-38-0) activities, the Evaluator reviewed the EUL values for all measure categories to ensure they were still valid and revis...

AI summary The Evaluator updated Effective Useful Life (EUL) values for all measure categories in the 2024-2025 DSM MA, resulting in a weighted average EUL of 2.6 years after revising values where necessary.

11.2.4 Evaluated Gross Savings The 2024 evaluated SEM gross energy and peak demand savings at the generator are listed in [Table](#page-38-1) 32 below. The gross energy and peak demand savings at the generator were estimated by using diffe...

AI summary The document discusses the evaluation of gross energy and peak demand savings from the 2024 SEM program, using line loss factors from the 2014 Cost of Service Study Progress Update to estimate savings at the generator level.

Table 32: Evaluated 2024 SEM Gross Electrical Energy and Peak Demand Savings New Projects Continuing Projects Total Number of Projects 1 6 7 Energy Savings Tracked Gross Energy Savings (GWh) 1.186 3.083 4.269 Adjustment Ratio for Energy Sa...

AI summary Table 32 presents evaluated 2024 SEM gross electrical energy and peak demand savings, including tracked energy savings, adjustment ratios, and line loss factors. The data show total gross energy savings of 4.267 GWh and gross peak demand savings of 0.513 MW. GHG emission reductions were calculated using Nova Scotia-specific factors derived from Nova Scotia Power's 2023 emissions data.

Table 34: Comparison of 2024 SEM Tracked and Evaluated Savings at the Generator Gross Savings NTGR Net Savings Realization Rate Value Unit Value Value Unit Value Energy Savings Tracked Savings by E1 4.480 GWh 1.00 4.480 GWh Evaluation Resu...

AI summary Table 34 compares the 2024 SEM tracked and evaluated savings at the generator level, showing minimal differences between tracked and evaluated savings, with the evaluated net electrical energy savings being less than 1% below the tracked value due to adjustments in two projects.

12 SEM Key Findings and Recommendations As mentioned previously, the main objectives of the 2024 SEM evaluation were as follows: › Calculate SEM gross and net results, namely electrical first-year and lifetime energy savings, peak demand s...

AI summary The 2024 SEM evaluation found that SEM exceeded its net electrical energy and peak demand savings targets by 6% and 14%, respectively. Participation remained stable with 12 participants, and M&V methodologies showed improved accuracy, particularly for peak demand savings. The Evaluator noted no recommendations for the program.

Table 35: Overall 2024 Custom Incentives Participation and Evaluated Savings Participa ation Level Gross Gross Savings Net S Savings Value Unit Value Unit Value Unit Value Custom ' ' • Energy Savings 49.825 GWh 0.80 39.951 GWh Lifetime Ene...

AI summary Table 35 summarizes the participation and evaluated savings from the 2024 Custom Incentives program. The program exceeded its targets for net electrical energy and peak demand savings. The data includes energy savings, lifetime energy savings, peak demand savings, GHG emission reductions, and energy use life (EUL).

SEM Appendix XV: SEM Tracking Sheet Audit Appendix XVI: SEM Project Review Protocol Appendix XVII: SEM Detailed Project Review Adjustments Appendix XVIII: SEM 2024 Recommendations PROJECT NO. 6562 2475, Laurier boul., Suite 250 Quebec City...

AI summary The document outlines appendices related to Strategic Energy Management (SEM), including tracking sheets, project review protocols, and 2024 recommendations. It references Project No. 6562 with contact details for a Quebec City-based entity.

2024 DSM EVALUATION March 26, 2025 In collaboration with:

AI summary The 2024 Demand-Side Management (DSM) Evaluation document outlines a collaborative effort involving unspecified partners, though specific content details are not provided in the text. The evaluation focuses on assessing DSM programs and their outcomes.

A. Identifying Key Decision-makers - A1. We would like to speak with someone that played a key role in the financial decision to implement the energy efficiency project for your organization. Are you that person? - 1. Yes - 2. No - 98. Don...

AI summary This section outlines a process to identify key decision-makers involved in implementing energy efficiency projects. It includes questions to determine if the respondent was directly involved in financial decisions, their role, or to obtain contact information for someone who was.

[ASK IF $ ≥0] - C5. As part of its Custom Retrofit program, Efficiency Nova Scotia gave your organization a $ incentive for the [Investigation or Feasibility] study. Without this incentive, what is the likelihood that you would have conduc...

AI summary The text asks respondents about the impact of financial incentives on their likelihood of conducting energy efficiency studies and implementing projects. It focuses on Efficiency Nova Scotia's Custom Retrofit program, evaluating how incentives influence decision-making and project timelines.

[READ AND ROTATE (D1 + D2 TO D3) AND (D4 + D5 TO D7) SEQUENCES] - D1. Before participating in the Custom Retrofit program for this project, had your organization already participated in this program or in other Efficiency Nova Scotia progr...

AI summary The document outlines survey questions assessing prior participation in Efficiency Nova Scotia programs and the influence of promotional materials on energy efficiency project decisions. It evaluates whether past involvement or exposure to promotional content prompted technical evaluations and cost-effectiveness considerations.

E. Spillover - E1. Since taking part in the Custom Retrofit program, have you implemented any additional energy efficiency measures outside of the program? - 1. Yes - 2. No [THANK AND TERMINATE] - 98. Don't know [THANK AND TERMINATE] - 99....

AI summary The survey explores whether participants in the Custom Retrofit program implemented additional energy efficiency measures outside the program, their financing sources, and the influence of the program on these decisions. It also requests details on measures and reasons for not using Efficiency Nova Scotia programs.

APPENDIX II Retrofit Algorithm for Free-Ridership Calculation Question Answer Score C8 I would like you to rate the importance of the following five factors on your decision to implement energy efficiency measures through the Custom Retrof...

AI summary This section presents a questionnaire assessing the importance of various factors influencing participation in the Custom Retrofit program, focusing on financial incentives, information provision, and technical support from Efficiency Nova Scotia.

Table 1: 2024 Retrofit Corrected Tracked Savings Program Component Result Tracked by E1 Corrected Tracked Value Relative Difference Value Unit Value Unit Gross Energy Savings – at the Generator 26.198 GWh 26.115 GWh -0.32% Gross Demand Sav...

AI summary The tables present 2024 corrected tracked savings for energy and demand from retrofit and building optimization programs. There were no substantial differences between tracked and corrected values, with minor changes attributed to adjustments in line loss factors.

ECONOLER Efficency Nova Scotia On-Site Visit Protocol - 2024 Market qu estion naire fille d-in (Y/N) Free-rider ship q uestionna ire filled-in (Y/N) Spillover questic onnaire fil lled-in (Y/N) Sections below to be filled aft ter the vi sit...

AI summary The document outlines an on-site visit protocol for Efficiency Nova Scotia in 2024, including sections for filling in market questionnaire, free-rider questionnaire, spillover questionnaire, and estimating the useful life of projects. It also includes tracking sheets for energy savings and peak demand adjustments.

Savings calculation approach - Projects with M&V 5. Are the M&V boundaries capturing all the energy consumption that's impacted by the project? (Y/N) 7. Are M&V results measured in a short period extrapolated to annual results appropriatel...

AI summary The document discusses the evaluation of M&V (Measurement and Verification) boundaries, extrapolation methods, regression validity, and the impact of COVID on energy savings calculations. It includes questions to assess the accuracy of savings calculations and the consideration of seasonal load profiles and peak demand savings.

Tracked Savings Calculation For this project, an E1 project engineer created an Excel spreadsheet to calculate electrical energy consumption for all four tanks for both the base cases and efficient cases, using the refrigeration unit perfo...

AI summary An E1 project engineer developed an Excel spreadsheet to calculate electrical energy consumption and peak demand savings for refrigeration units, using manufacturer data, weather conditions, seawater temperatures, and on-site measurements to validate heat exchange efficiency during winter peak demand periods (December-February, 5-7 pm).

D. Cross-Influence - D1. [SINGLE RESPONSE] Before participating in the Custom New Construction program for the [INSERT PROJECT NAME] project, had your organization already participated in Custom New Construction or in another Efficiency No...

AI summary The document outlines a survey assessing cross-influence effects of Efficiency Nova Scotia programs on new construction projects. It evaluates whether prior participation in energy efficiency programs or exposure to promotional materials influenced technical evaluations and cost-effectiveness considerations for building projects.

B. Free-ridership - B1. Why did your organization decide to build a better-than-code building? [DO NOT READ. MULTIPLE RESPONSE] - 1. Lower operation costs - 2. Energy policy in my organization - 3. Environmental reasons/energy efficiency -...

AI summary The section includes survey questions about motivations for building better-than-code structures, pre-incentive design decisions, and confidence in receiving Efficiency Nova Scotia incentives, focusing on free-ridership implications in energy efficiency programs.

C. Cross-Influence - C1. Before participating in the Custom New Construction program for the building we discussed today, had you already taken part in Custom New Construction or another Efficiency Nova Scotia program? - 1. Yes, Custom New...

AI summary The text outlines survey questions assessing whether prior participation in Efficiency Nova Scotia programs or exposure to promotional materials influenced decisions in new construction projects, focusing on cross-influence effects on energy efficiency choices and cost evaluations.

D4. [ASK IF D2=1] Why did you decide not to include these technologies? [RECORD VERBATIM RESPONSE] - 1. (Higher cost) - 2. (Lower profits) - 3. (Slower speed of construction) - 4. (Unavailability of the technologies in the marketplace) - 5...

AI summary The response lists 11 reasons for excluding technologies, including higher costs, lower profits, slower construction, unavailability, labor shortages, lack of expertise, complexity, performance issues, and occupant satisfaction concerns. Other unspecified factors and refusals are also noted.

Table 5: Overview of Data Collection Activity Descriptor This Instrument Instrument Type Semi-directed in-depth interview Estimated Time to Complete 15 Minutes Target Audience › New or Non-Participant Modelers › Expected Number of Completi...

AI summary The document outlines data collection activities, including semi-directed in-depth interviews targeting new or non-participant modelers, with a focus on expanding program presence beyond electrically heated MURB markets and updating program service logic. The research aims to assess awareness, motivations, barriers, and potential actions for increasing participation.

APPENDIX XVI SEM Project Review Protocol The SEM project review protocol used for the 2024 evaluation was the same protocol used in 2023. It includes sections that served to review both bottom-up and top-down approaches that were both used...

AI summary The 2024 SEM Project Review Protocol reused the 2023 protocol, incorporating bottom-up and top-down evaluation approaches. Evaluations involved phone interviews/site visits after reviewing EfficiencyOne digital files, with a net-to-gross ratio (NTGR) of 1.00 from the 2023 evaluation applied to the current DSM cycle.

DIRECT INSTALLATION PROGRAM Final Report 2024 DSM EVALUATION March 25, 2025

AI summary The document outlines the 2024 evaluation of the Direct Installation Program under Demand-Side Management (DSM), part of Efficiency Nova Scotia's initiatives. The report, dated March 25, 2025, assesses program performance and outcomes as part of Nova Scotia's energy efficiency efforts.

ABBREVIATIONS BER Business Energy Rebates BNI Business, non-profit and institutional CDI Commercial Direct Install DC direct current DIY Do-it-yourself DSM Demand-side management E1 EfficiencyOne EER energy efficiency ratio ENS Efficiency...

AI summary This section defines abbreviations used in the regulatory proceeding, including terms related to energy programs, organizations, and technical metrics. Key entities include Nova Scotia Power and Efficiency Nova Scotia, with topics covering energy rebates, demand-side management, and utility regulations.

EXECUTIVE SUMMARY This report presents the 2024 Demand-Side Management (DSM) results of the Direct Installation program administered by EfficiencyOne (E1). This program is comprised of the Small Business Energy Solutions (SBES) program com...

AI summary This report details the 2024 Demand-Side Management (DSM) results of EfficiencyOne's Direct Installation program, which includes the Small Business Energy Solutions (SBES) initiative. The SBES program provides incentives to encourage Nova Scotia's small businesses to implement energy efficiency upgrades.

Table 2: Overall 2024 Direct Installation Participation and Evaluated Savings 1 Participation Level Gross Savings NTGR Net Sa avings Value Unit Value Unit Value Value Unit Energy Savings 673 2 Units 13.459 GWh 0.81 10.854 GWh Lifetime Ener...

AI summary Table 2 presents the 2024 participation and evaluated savings from direct installation programs, including energy savings, lifetime energy savings, peak demand savings, and GHG emission reductions. The table also includes the net-to-gross ratio (NTGR) for these savings, with an indication that 741 of the rebated units were part of the CDI Pilot projects.

SBES Findings and Recommendations This subsection presents the key findings from the SBES evaluation. 2024 SBES-Finding: SBES surpassed its net electrical energy savings by 2% and fell short of its net peak demand savings target by 16%. 20...

AI summary The 2024 SBES evaluation found that the program exceeded energy savings targets by 2% but missed peak demand goals by 16%. Participation rose by 53%, with DIY being the dominant route. Non-participant spillover was nil, and discrepancies were noted between evaluator and E1's tracked savings.

Table 3: Comparison of 2024 SBES Tracked and Evaluated Savings at the Generato[r](#page-129-1) 3 Gross Savings Net Savings Realization Value Unit NTGR Value Unit Rate Energy Savings Tracked Savings by E1 12.936 GWh 0.81 10.426 GWh 104% Eva...

AI summary Table 3 compares the tracked and evaluated energy and peak demand savings from the 2024 SBES and CDI Pilot programs. It shows gross and net savings, along with realization rates, indicating that evaluated savings slightly exceed tracked savings for both energy and peak demand.

INTRODUCTION EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (...

AI summary EfficiencyOne (E1) is an independent, non-profit organization responsible for delivering demand-side management (DSM) programs in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. E1's DSM activities are regulated by the Nova Scotia Utility and Review Board (NSUARB), and it receives funding from Nova Scotia Power (NS Power) ratepayers. The 2024 evaluation report focuses on the Direct Installation program, specifically the Small Business Energy Solutions (SBES) component, using parameters like net-to-gross ratios (NTGRs) from previous evaluations.

Table 4: Type of Evaluation Conducted for SBES, 2024 Program 2024 Program Component Process Market Impact Direct Installation SBES - - Condensed For each program, the Evaluator prepared a DSM evaluation report presenting key findings, elec...

AI summary Table 4 outlines the type of evaluation conducted for the Small Business Energy Solutions (SBES) program in 2024. The Evaluator prepared a DSM evaluation report highlighting first-year and lifetime energy savings, peak demand savings, and avoided GHG emissions.

2 SBES Evaluation Approach The 2024 SBES evaluation consisted of a condensed impact evaluation, which also covered the Commercial Direct Install (CDI) pilot. The main objectives of the 2024 SBES evaluation were as follows: - › Calculate gr...

AI summary The 2024 SBES evaluation focused on calculating energy savings and GHG emissions reductions from the SBES and CDI pilot programs. The evaluation included objectives such as calculating gross and net results and spillover levels using non-participant surveys. Research questions and methods were outlined in a table.

Table 6: 2024 SBES Evaluation Approach Evaluation Objectives Research Questions Methodology Calculate gross results › Are the data in the tracking sheet complete, accurate, and consistent? › What are the formulas and savings parameters for...

AI summary Table 6 outlines the 2024 SBES Evaluation Approach, detailing objectives, research questions, and methodologies for evaluating the Small Business Energy Solutions program. It includes calculating gross and net results, collecting non-participant perspectives, and conducting a tracking sheet audit.

4.2.1 Unitary Energy and Peak Demand Savings Savings for SBES Audit and DIY projects are established through calculations using data specific to each project. The 2024-2025 DSM MA provides a detailed description of inputs, references, and...

AI summary The 2024-2025 DSM MA outlines changes to unitary savings parameters for SBES measures, including updates to SEER, EER, and PCF values. The Evaluator validated these changes for top measures, with other parameters unchanged. The CIRx tool provides methodology details.

4.2.4 Effective Useful Life As part of the 2024-2025 DSM MA activities, the Evaluator reviewed the EUL values used in the calculation of electrical energy savings that are expected to persist over time. The EUL values established in previo...

AI summary The Evaluator reviewed effective useful life (EUL) values for energy efficiency measures as part of the 2024-2025 DSM MA update. EUL values for air-source heat pumps, photovoltaic systems, and lighting measures were revised due to changes in baseline energy use over time. The gross weighted average EUL value is 16.4 years.

Table 8: Evaluated 2024 SBES Gross Energy and Peak Demand Savings – Audit Path Category of Measure Commercial Kitchen DHW HVAC Lighting Laundry Refrigeration Envelope Total for All Categories Gross Peak Demand Savings – at the Generator (M...

AI summary Table 8 presents the evaluated 2024 SBES gross energy and peak demand savings, including adjustments for interactive effects and line loss factors, across various measure categories such as lighting, water heating, and HVAC.

4.3.3 Non-Participant Spillover Using the approach described in Section 2, the Evaluator assessed non-participant spillover from a sample of 30 commercial customers. Respondents were asked if they had implemented energy efficiency projects...

AI summary The Evaluator assessed non-participant spillover by surveying 30 commercial customers, finding that 14 implemented energy efficiency measures without E1's assistance, with minimal influence from E1's actions. The non-participant spillover level was determined to be nil.

Table 15: Evaluated 2024 SBES Net Energy and Peak Demand Savings Measure Category Audit DIY CDI Pilot Total Energy Savings Gross Energy Savings – at the Meter (GWh) 0.388 12.077 0.124 12.589 NTGR 0.88 0.80 1.00 - Net Energy Savings – at th...

AI summary Table 15 evaluates the 2024 SBES program's net energy and peak demand savings. The program exceeded its energy savings target by 2% but fell short of its peak demand savings target by 16%. The data includes gross and net savings at both the meter and generator levels, as well as lifetime energy savings and peak demand savings metrics.

4.4 Realization Rate [Table](#page-151-1) 16 below compares the tracked energy and peak demand savings values established through this evaluation to those calculated in the 2024 tracking sheet. It also includes the realization rate, repres...

AI summary Section 4.4 discusses the realization rate, which compares evaluated net savings to tracked net savings for energy and peak demand savings. A table is referenced that compares these values from the current evaluation to those in the 2024 tracking sheet.

5 SBES Key Findings and Recommendations The main objectives of the 2024 SBES evaluation were as follows: - › Calculate gross and net SBES results, namely electrical first-year and lifetime energy savings, peak demand savings, as well as av...

AI summary The 2024 SBES evaluation found that the program met its net electrical energy savings target (10.854 GWh, 2% over target) but missed its peak demand savings target (2.155 MW, 16% short). Evaluated savings exceeded E1's tracked results by 4% and 6% respectively. Participation rose 53% year-over-year, with DIY Path dominating. Spillover surveys found no non-participant impact.

[IF NO, ASK "CAN YOU SHARE THE CONTACT INFORMATION FOR THE OWNER OR PRIMARY DECISION MAKER?" & RECORD CONTACT INFO.] Please indicate your business' annual electrical energy consumption. Please choose from the from the following options: [R...

AI summary The text presents a conditional request for contact information if the answer is 'no' and asks businesses to report their annual electrical energy consumption with specific options. It includes termination instructions if the response is 'C/99 (Refused)'.

DEMAND RESPONSE PROGRAM Final Report 2024 DSM EVALUATION March 25, 2025

AI summary The Final Report from the 2024 DSM Evaluation, dated March 25, 2025, provides an assessment of the Demand Response Program. It focuses on evaluating the effectiveness of demand-side management initiatives, including program performance, cost recovery, and alignment with regulatory objectives.

Evaluation Approach The 2024 evaluation was aimed at calculating program component results, namely new and total available DR capacity. [Table](#page-195-1) 1 summarizes the types of evaluation conducted for each program component and the...

AI summary The 2024 evaluation focused on calculating program component results, specifically new and total available DR capacity, with Table 1 summarizing the evaluation types and methodologies used for each program component.

Table 1: Summary of Demand Response Program Evaluation Evaluation Type Methodology Program Component Process Market Impact Residential Demand Response Comprehensive › Tracking sheet audit › Whole-house consumption data analysis › Measure A...

AI summary The document evaluates the performance of demand response programs, including residential and BNI demand response, using methodologies such as tracking sheet audits, whole-house consumption data analysis, and calculations based on evaluation results. Table 2 provides participation levels and evaluated capacity for these programs.

Table 2: Overall 2024 Demand Response Participation and Evaluated Results Participation Level Evaluated Results Value Unit Value Unit Residential DR New DR Capacity 353 Participants 0.057 MW Total DR Capacity 353 Participants 0.057 MW BNI...

AI summary In 2024, the Demand Response (DR) program achieved 8.091 MW in total DR capacity, exceeding the target of 7.240 MW. However, the Residential DR program underperformed, while the BNI DR program was the main contributor to the success of the program.

Residential DR Findings and Recommendations This subsection presents the key findings and recommendations from the Residential DR evaluation. 2024 Res DR-Finding: In 2024, Residential DR new and total available DR capacities both amounted...

AI summary The 2024 Residential DR evaluation found underperformance in achieving planned DR capacity (0.057 MW vs. 0.210 MW). Mysa thermostats dominated (83% of devices), but preheating errors reduced capacity. Whole-house data regression models were used for baseline calculations, with plans to refine unitary savings metrics. A 2025 recommendation calls for re-analyzing Eco Shift Pilot data to improve accuracy.

[Table](#page-198-0) 3 below presents a comparison of E1 tracked available DR capacity compared to evaluated results as well as the realization rate. Table 3: Comparison of 2024 Tracked and Evaluated Savings at the Generator Available DR C...

AI summary Table 3 compares the tracked and evaluated demand response (DR) capacity for residential and BNI DR programs in 2024. The realization rates for BNI DR are high (99% and 100%), but residential DR data is incomplete due to ongoing methodological considerations.

INTRODUCTION EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (...

AI summary EfficiencyOne (E1) manages Nova Scotia's demand-side management (DSM) programs, including a demand response (DR) program evaluated in 2024. The DR program's available capacity is measured based on potential load reduction during winter events (Dec-Feb), excluding weekends/holidays, with capacity calculated per participant over two hours of DR events.

1.1 Residential DR Description In 2023, E1 officially launched the Residential DR program component. Since the fall of 2020, E1 has implemented several pilot initiatives focused on reducing demand during the Nova Scotia peak period. The Ec...

AI summary In 2023, E1 launched the Residential DR program, including the Eco Shift Pilot, which allows residents to contribute to demand reduction using eligible devices. The program involves remote control of devices during peak periods, with participants notified in advance and given the option to opt out. NS Power called 10 DR events during the 2023-2024 winter peak season.

Table 5: Eco Shift Pilot 2023-2024 Event Summary Event # Date Start Time Duration (Hours) Outdoor Temperature at Event Start (°C) 1 01-09-2024 6 p.m. 2 -5 2 01-16-2024 8 a.m. 2 -4 3 01-19-2024 5 p.m. 3 -5 4 01-24-2024 7 a.m. 4 -13 5 01-31-...

AI summary Table 5 outlines the Eco Shift Pilot 2023-2024 events with details including dates, times, durations, and outdoor temperatures. It also defines terms related to residential demand response, distinguishing between enrolled and participating devices.

1.2 Follow-up on Past Evaluation Report Recommendations The Evaluator evaluated Residential DR in 2023 and issued one improvement recommendation. [Table](#page-1-0) 6 below provides a summary of the implementation status of the recommendat...

AI summary The Evaluator assessed Residential Demand Response (DR) in 2023 and issued one improvement recommendation. Table 6 summarizes the implementation status of this recommendation from the 2023 report.

Table 6: Implementation Status of Past Recommendation for Res DR # Recommendation Status Comments 2023 –Res DR – R1 Ensure that available DR capacity tracked by E1 includes the in-service rate as well as the unitary available DR capacity v...

AI summary Table 6 outlines the implementation status of past recommendations for Residential Demand Response (Res DR), noting that one recommendation remains unaddressed due to its applicability to an unevaluated pathway. In 2024, 353 participants enrolled 1,307 devices in Residential DR, with Mysa thermostats comprising 83% of installed devices.

Figure 1: Overview of Residential DR Participation, 2024 Since participants continued to enroll throughout the season and they could opt out of any event, not all enrolled devices participated in each event. [Table](#page-2-0) 7 below pres...

AI summary The text discusses residential demand response (DR) participation in 2024, noting that not all enrolled devices participated in each event due to opt-outs and connectivity issues. The number of enrolled devices is referenced in Table 7, and participation details are covered in Subsection 3.2.1.

Table 7: Eco Shift Pilot Device Enrollment per 2023-2024 Event Devices Enrolled per Event Event # Date Connected EVs and EV Chargers Mysa Thermostats Sinopé Thermostats Total 1 01-09-2024 17 934 140 1,091 2 01-16-2024 36 947 141 1,124 3 01...

AI summary Table 7 presents the number of devices enrolled in the Eco Shift Pilot during events from January to February 2024, including connected EVs, thermostats, and total enrollment. The document also references a section on the evaluation approach for residential demand response (DR) programs.

The 2024 Residential DR evaluation comprised a comprehensive impact evaluation. The main objective of the 2024 Residential DR evaluation was as follows: › Calculate Residential DR results, namely new and total available DR capacities The E...

AI summary The 2024 Residential DR evaluation aimed to calculate new and total available DR capacities. The Evaluator identified key research questions and methods to achieve this objective, as outlined in Table 8.

Table 8: 2024 Residential DR Evaluation Approach Evaluation Objectives Research Questions Methodology Calculate available DR capacity results › Are the data in the tracking sheet complete, accurate, and consistent? › What was the average p...

AI summary This section outlines the evaluation approach for the 2024 Residential Demand Response (DR) program. It includes objectives such as assessing data completeness and calculating available DR capacity, along with methodologies like tracking sheet audits and metering data analysis.

Metering Data Analysis To establish the available DR capacity generated from Mysa thermostats, the Evaluator analyzed the available participant whole-house consumption meter data and established a unitary available DR capacity value per pa...

AI summary The Evaluator used whole-house meter data to calculate DR capacity from Mysa thermostats, converting it to per-thermostat values and updating the 2024-2025 DSM MA. Other products like EVs and batteries were excluded due to insufficient data, while Sinopé thermostats were included based on similar control strategies. No DR capacity was claimed for EVs due to low participation.

3 Residential DR Impact Evaluation The objective of the 2024 Residential DR impact evaluation was to determine new and total available DR capacities.

AI summary The 2024 Residential DR Impact Evaluation aimed to assess new and total available demand response (DR) capacities, focusing on residential programs. This evaluation seeks to quantify the potential for DR participation and its implications for grid management and energy efficiency initiatives in Nova Scotia.

3.2.1 In-service Rates For the Eco Shift Pilot pathway, not all devices participated in each event since participants continued to be registered throughout the season and participants could opt out of any event. Additionally, connectivity...

AI summary The in-service rate for the Eco Shift Pilot pathway includes all enrolled devices, even those that opted out or had connectivity issues, which affects the average available DR capacity. This approach is necessary due to the continuous registration of participants and the use of whole-house data analysis.

Table 9: Eco Shift Pilot Mysa Thermostat In-service Rate per 2023-2024 Event Devices Enrolled per Event Event # Date Number of Enrolled Devices Number of Participating Devices In-service Rate 1 01-09-2024 934 919 98% 2 01-16-2024 947 928 9...

AI summary Table 9 presents the in-service rate of Mysa thermostats during the Eco Shift Pilot events from January to February 2024, showing a consistent participation rate above 96%. The table tracks the number of enrolled and participating devices, as well as the in-service rate per event.

Metering Data Analysis Methodology After reviewing existing literature to identify the most appropriate baseline methodology for the 2024 evaluation, the Evaluator decided to rely on whole-house consumption data to establish a regression m...

AI summary The Evaluator used whole-house consumption data and regression models with time-of-week and outdoor temperature variables to assess smart thermostat program impacts. 168 hourly regression models were created, excluding inconsistent data, resulting in analysis of 199 households. This approach accounts for interactive heating effects and uses a large dataset with cold-temperature data.

Metering Data Analysis Results Using the methodology presented above, the Evaluator established the DR capacity made available at each event hour of the 2023-2024 heating season. As mentioned in the introduction, available DR capacity is m...

AI summary The analysis of metering data from the 2023-2024 heating season indicates that some DR events resulted in negative DR capacity due to incorrect preheating implementation. This occurred during the events instead of before, leading to higher load than expected. The methodology used is based on heating degree days to represent heating load accurately.

Unitary Available DR Capacity As outlined in [Table](#page-8-0) 10 above, the average available DR capacity across all events was 177 W per household. The household data included in the metering analysis were used to establish an average o...

AI summary The Evaluator calculated a unitary available DR capacity of 39.4 W per thermostat for 2023-2024, based on household data. A higher expected capacity of 57.7 W/thermostat was proposed if preheating errors are resolved. The Evaluator recommends re-analyzing Eco Shift Pilot data in 2025 to confirm improved capacity, though the higher value was not used for 2023-2024 savings calculations.

3.2.3 Interactive Effects In a home, interactive effects occur when the implementation of energy efficiency products has an impact on the energy consumption of other elements such as heating and cooling. For the Eco Shift Pilot pathway, al...

AI summary Interactive effects occur when energy efficiency measures impact other home systems like heating/cooling. The Eco Shift Pilot evaluates these effects using whole-house metering data (AMI) over device-level data, capturing interactions with uncontrolled systems (e.g., heat pumps) and offsetting thermostat load reductions.

3.2.4 Effective Useful Life Although no electrical energy savings are expected from DR initiatives, the Evaluator established an EUL value since the available DR capacity can persist over time. For the Eco Shift Pilot pathway, an EUL value...

AI summary The Evaluator assigns an Effective Useful Life (EUL) of one year to Demand Response (DR) capacity in the Eco Shift Pilot pathway, as participation includes all active participants and lacks data on program longevity. This avoids extrapolating capacity over a participant's lifetime, ensuring accurate evaluation of available DR resources.

3.2.5 Evaluated New and Total Available DR Capacities Available DR capacity is obtained by multiplying the number of controlled thermostats by the unitary available DR capacity value as presented in the equations below. = ℎ × [Table](#page...

AI summary The document evaluates new and total available demand response (DR) capacities for residential DR in 2024, which amounted to 0.057 MW at the generator. The calculation uses the number of controlled thermostats and unitary available DR capacity values, with line loss factors submitted to the NSUARB in 2014.

Table 11: Evaluated 2024 Residential DR New and Total Available DR Capacities Results Number of Enrolled Thermostats 1,2578 Unitary Available DR Capacity (Watts per thermostat) 39.4 Available DR Capacity – at the Meter (MW) 0.050 Line Loss...

AI summary Table 11 presents evaluated 2024 residential demand response (DR) capacities, showing 1,2578 enrolled thermostats and available DR capacity at the meter and generator. It also notes that preheat implementation errors reduced the available DR capacity, which could have been 0.083 MW at the generator if these errors had not occurred.

5.1 BNI DR Description In 2023, E1 officially launched the BNI DR program component. Since the fall of 2020, E1 implemented several pilot initiatives focused on reducing demand during the Nova Scotia peak period. One of them was the DR Agg...

AI summary In 2023, E1 launched the BNI DR program, building on pilot initiatives since 2020. The DR Aggregator Pilot, led by Parsons Inc., allows E1 to hire aggregators who manage groups of BNI customers to reduce load during peak periods through remote control or direct participant action, such as adjusting heating, cooling, and lighting systems.

Table 12: DR Aggregator Event Criteria Criteria Requirement Event Season Winter is from December through February. Event Windows From 7:00-11:00 am and 5:00-9:00 pm, Monday to Friday excluding holidays, during winter. Event Initiation Crit...

AI summary Table 12 outlines the criteria for Demand Response (DR) Aggregator events, including event season, windows, initiation based on load forecasting, limits on the number of events, durations, frequency, and notification procedures. NS Power called 10 events during the 2023-24 winter peak season.

5.2 Follow-up on Past Evaluation Report Recommendations The Evaluator evaluated BNI DR for the first time during the 2022-2023 pilot season and issued improvement recommendations. [Table](#page-14-1) 14 below provides a summary of the impl...

AI summary The Evaluator assessed the BNI DR program during the 2022-2023 pilot season and provided improvement recommendations. Table 14 outlines the implementation status of these recommendations from the 2023 report.

Table 14: Implementation Status of Past Recommendation for BNI DR # Recommendation Status Comments 2023 – BNI DR – R1 Use an additive same-day adjustment factor to establish the baseline load instead of a scalar same day adjustment factor....

AI summary Table 14 outlines the implementation status of past recommendations for the BNI DR program. Key actions include updating methodologies for baseline load calculations, establishing enrolled capacity based on test events, and identifying participants best suited for different event times. Some recommendations are complete, while others are in progress.

6 BNI DR Evaluation Approach The 2024 BNI DR evaluation comprised a comprehensive impact evaluation. The main objective of the 2024 BNI DR evaluation was as follows: › Calculate BNI DR results, namely new and total available DR capacities...

AI summary The 2024 BNI DR evaluation aimed to calculate new and total available DR capacities. The Evaluator identified key research questions and methods to achieve this objective, as outlined in Table 15.

Table 15: 2024 BNI DR Evaluation Approach Evaluation Objectives Research Questions Methodology Establish available DR capacity results for the DR Aggregator pathway › Are the data in the tracking sheet complete, accurate, and consistent? ›...

AI summary This document outlines the evaluation approach for the 2024 BNI Demand Response (DR) program, focusing on assessing the completeness, accuracy, and consistency of data in the tracking sheet, as well as the application of measurement and verification (M&V) methodologies.

7 BNI DR Impact Evaluation The main objective of the 2024 BNI DR impact evaluation was to determine new and total available DR capacities. In addition, the Evaluator validated the correct application with the M&V methodology approach recom...

AI summary The 2024 BNI DR impact evaluation aimed to assess new and total available DR capacities, validate the 2023 M&V methodology application, and evaluate the appropriateness of updated M&V rules.

7.1 Tracking Sheet Audit To ensure program component results were reliably compiled, the Evaluator first performed a tracking sheet audit aimed at verifying the completeness and consistency of the data submitted by E1. The verification and...

AI summary The Evaluator conducted a tracking sheet audit to verify the completeness and consistency of data submitted by E1, ensuring reliable compilation of program results. Corrective actions are detailed in Appendix VI, leading to the presentation of corrected tracked available DR capacity in the report.

7.2 Demand Response Capacity For BNI DR, the evaluated metric is referred to as available DR capacity and corresponds to the load reduction made available for peak demand events in participating businesses.

AI summary The section defines 'available DR capacity' for BNI DR programs, focusing on load reduction during peak demand events in participating businesses. It emphasizes the metric used to evaluate demand response capacity in the context of business, non-profit, and institutional sectors.

Project Review Findings The most frequent adjustment made by E1 to the available DR capacity calculation was to set the available DR capacity to zero due to non-participation in events. If no obvious load shed was observable for an event w...

AI summary E1 frequently adjusted DR capacity calculations by setting values to zero due to non-participation, incorrect metering data, or operational hour mismatches. The Evaluator agreed with these adjustments but recommended explicitly documenting criteria for zeroing DR capacity and ensuring consistent lookback window adjustments. Overall, E1 project reviews were deemed appropriate.

Available DR Capacity In their compilation, E1 automatically set the available DR capacity to zero when an event resulted in negative available DR capacity. The Evaluator agreed with this compilation approach since all reviewed events that...

AI summary E1 set available DR capacity to zero when negative values occurred, a practice the Evaluator agreed with. Adjustments were made based on reviewed projects, but the sampling method led to some large projects being excluded. The Evaluator's reviews showed minor differences in available DR capacity compared to E1's reviews.

Table 16: Reviewed Available DR Capacity Stratum 1 Projects Stratum 2 Projects Metric E1 Values Evaluator Values Difference (%) E1 Values Evaluator Values Difference (%) Available DR Capacity Before Project Review (kW) 6,167 6,167 N/A 419...

AI summary Table 16 shows the reviewed available demand response (DR) capacity before and after project review, including adjustment ratios for Stratum 1 and Stratum 2 projects. The adjustment ratios slightly reduced the available DR capacity in Stratum 1 and increased it in Stratum 2.

Table 17: Evaluated Adjustment Ratios Stratum Adjustment Ratio Margin of Error Percentage of Total Available DR Capacity Stratum 1 0.99 0% 81% Stratum 2 0.73 16% 19% Overall 0.93 7% 100% Overall, the Evaluator project reviews resulted in a...

AI summary Table 17 presents evaluated adjustment ratios for different strata, showing a 0.93 overall adjustment ratio with a 7% margin of error. Stratum 1 has a higher ratio (0.99) and accounts for 81% of total available demand response (DR) capacity, while Stratum 2 has a lower ratio (0.73) and accounts for 19%.

7.2.2 Interactive Effects In a building, interactive effects occur when the implementation of energy efficiency products has an impact on the energy consumption of other elements such as heating and cooling. For the Aggregator pathway, int...

AI summary Interactive effects in buildings occur when energy efficiency measures influence heating and cooling systems. For the Aggregator pathway, these effects are factored into demand response (DR) capacity calculations because meter data reflects whole-building consumption.

7.2.3 Effective Useful Life Although no energy savings were expected under the Aggregator pathway, the Evaluator established an effective useful life (EUL) value to express for how many years available DR capacity might persist, i.e. as lo...

AI summary The Evaluator assigned an Effective Useful Life (EUL) of one year to DR capacity under the Aggregator pathway, as participation includes all active participants annually. No extrapolation over lifetime is needed due to lack of data on long-term participation, and the average number of winters subscribed should be used for new DR capacity.

7.2.4 Evaluated New and Total Available DR Capacities [Table](#page-21-0) 18 below presents the new and total available DR capacity results of BNI DR for 2024. For BNI DR, the new available DR capacity was defined as the increase in availa...

AI summary The section evaluates new and total available demand response (DR) capacities for BNI DR in 2024. New available DR capacity increased compared to the previous year, with new and total capacities at 5.669 MW and 8.034 MW, respectively. Line loss factors were used to estimate DR capacity at the generator, based on rate codes and submissions to the NSUARB from the 2014 Cost of Service Study Progress Update.

Table 18: Evaluated 2024 BNI DR Available DR Capacity Results New Available DR Capacity Total Available DR Capacity Number of Participants 70 76 Available DR Capacity Before Adjustment Ratio – at the Meter (MW) 5.775 8.181 Adjustment Ratio...

AI summary Table 18 presents the evaluated 2024 BNI DR available demand response capacity, including metrics like the number of participants, available capacity before and after adjustment ratios, and line loss factors. The data highlights the capacity contributions from new and continuing participants.

Comparison of Enrolled and Evaluated Available DR Capacity [Table](#page-22-1) 19 presents the difference between enrolled DR capacity and evaluated DR capacity.

AI summary The text introduces a comparison between enrolled and evaluated DR (Demand Response) capacity, referencing Table 19 which highlights the differences between these two measures.

Table 19: Evaluated 2024 BNI DR Available DR Capacity Metric Stratum 1 Projects Stratum 2 Projects Overall Enrolled DR Capacity (MW) 5.699 6.525 12.224 Evaluated DR Capacity (MW) 6.124 1.463 7.587 Percentage Achieved of Estimate 107% 22% 6...

AI summary Table 19 evaluates the 2024 BNI DR available DR capacity, showing enrolled and evaluated DR capacity across different strata. The data indicates that while Stratum 1 achieved 107% of its estimated capacity, Stratum 2 only achieved 22%, with an overall achievement of 62%. The table emphasizes the importance of accurate estimates for reliable DR capacity during peak events.

7.3 Program Realization Rate [Table](#page-23-0) 20 below compares the available DR capacity established through this evaluation to the value in the 2024 tracking sheet. The realization rates, representing the ratio of evaluated available...

AI summary The document discusses the program realization rate for demand response (DR) capacity, comparing evaluated available DR capacity to tracked available DR capacity. The realization rates are established at 99% for new DR capacity and 100% for total available DR capacity.

Table 20: Comparison of 2024 BNI DR Tracked and Evaluated New and Total Available DR Capacities at the Generator Available DR Capacity Value Unit New Available DR Capacity Available DR Capacity Tracked by E1 5.702 MW Evaluation Results 5.6...

AI summary Table 20 compares the new and total available demand response (DR) capacities tracked by E1 and the evaluation results for 2024 BNI DR. Evaluated capacities are slightly lower than tracked values due to adjustments made during project reviews, as discussed in Subsection 3.2.1.

8 BNI DR Key Findings and Recommendations As mentioned previously, the main objective of the 2024 BNI DR evaluation was as follows: › Calculate BNI DR results, namely new and total available DR capacities This section provides the Evaluato...

AI summary The 2024 BNI DR program exceeded its total available DR capacity target (7.030 MW) with 8.034 MW achieved, including 5.669 MW of new capacity. Participation increased 700% (9 to 76 participants), but average capacity per participant dropped from 263 kW to 106 kW. E1's calculation guidelines were mostly appropriate but had gaps identified by the Evaluator.

CONCLUSION [Table](#page-26-0) 21 presents the participation levels and evaluated new and total available DR capacity for each program component and for the Demand Response program as a whole.

AI summary The conclusion section references Table 21, which outlines participation levels and evaluated new and total available DR capacity for each program component and the Demand Response program as a whole.

Table 21: Overall 2024 Demand Response Participation and Evaluated Results Participation Level Evaluated Results Value Unit Value Unit Residential DR New DR Capacity 353 Participants 0.057 MW Total DR Capacity 353 Participants 0.057 MW BNI...

AI summary Table 21 presents the 2024 Demand Response (DR) participation and evaluated results, highlighting that the BNI DR program was the largest contributor to total DR capacity, although residential DR fell short of its target. The program as a whole exceeded the planned total available DR capacity.

Demand Response Appendix I Residential DR: Tracking Sheet Audit Appendix II Residential DR: Detailed Metering Data Analysis Methodology Appendix III Residential DR: Regression Coefficients Appendix IV Residential DR: Event Day Graphs of Ex...

AI summary The document outlines appendices for residential and BNI (Business, Non-profit, and Institutional) Demand Response (DR) programs, including audit tracking sheets, metering data analysis, regression coefficients, load graphs, and 2024 recommendations. EfficiencyOne is referenced in an image caption.

Form of the Regression After reviewing existing literature to identify the most appropriate baseline methodology for this evaluation, the Evaluator decided to use a regression model that considers the time of week and outdoor temperature t...

AI summary The Evaluator chose a regression model incorporating time-of-week and outdoor temperature to establish a baseline for evaluating a smart thermostat program. This approach accounts for temperature impacts on electricity consumption and leverages a large dataset, contrasting with methods using previous similar days. The model's preference stems from its common use in similar programs and ability to differentiate temperature impacts across varying times of the week.

Where: - › , is the calculated baseline load in kW on a specified day of week (D) and hour of day (H), for a given temperature. - › , is the time of the week where D is from 1 to 7 (Sunday to Saturday) and H is from 00 to 24 (midnight to 1...

AI summary The text outlines a methodology for calculating baseline load using heating degree days (HDD) and temperature data, with participants assigned to weather stations based on postal codes to align outdoor temperature with their location.

Where: - $\rightarrow$ $\beta_{D,H}$ is the regression intercept. - $\alpha_{D,H}$ is the regression slope. - $\rightarrow$ RMSE h is the hourly model root mean square error. - $n_h$ is the number of observations. - $\rightarrow \bar{x}_h$...

AI summary The text outlines statistical methods for calculating uncertainty in demand response (DR) load reduction measurements, including equations for standard error propagation, root mean square error (RMSE), and HDD-based evaluation frameworks to quantify DR capacity accuracy.

APPENDIX III Residential DR: Regression Coefficients Hour of Week Baseload (𝜷𝑫,𝑯) HDD Coefficient (𝜶𝑫,𝑯) 1_00 0.595 0.0882 1_01 0.532 0.0830 1_02 0.661 0.0756 1_03 0.397 0.0907 1_04 0.390 0.0930 1_05 0.425 0.0996 1_06 0.535 0.1066 1_07 0.6...

AI summary This appendix presents regression coefficients for residential demand response (DR) programs, detailing baseload and HDD coefficients for various hours of the week. These coefficients are used to model energy consumption patterns and inform demand-side management strategies.

This appendix summarizes all the recommendations made by the Evaluator as part of the 2024 evaluation of Residential DR. Section Recommendations Executive Summary 2024 Res DR Recommendation 1: Gather and analyze the Eco Shift Pilot data ag...

AI summary This appendix summarizes the Evaluator's 2024 recommendations for the Residential Demand Response (DR) evaluation, including a recommendation to re-analyze the Eco Shift Pilot data in 2025 to improve DR capacity value accuracy and consistency.

This document presents the detailed results of the tracking sheet audit performed by the Evaluator, which was aimed at: - › Verifying that all data fields required for the evaluation were included and filled out in the tracking sheet submi...

AI summary The document details the results of a tracking sheet audit conducted by the Evaluator, aimed at verifying data completeness and accuracy in E1's submitted tracking sheet. The audit corrected the methodology used to calculate average savings for certain participants, resulting in slight differences in tracked new and available DR capacities.

Table 1: 2024 BNI DR Corrected Tracked Available DR Capacity Program Component Result Value Tracked by E1 Corrected Tracked Value Relative Difference Value Unit Value Unit Value New Available DR Capacity 5.553 MW 5.702 MW 3% Total Availabl...

AI summary Table 1 presents the 2024 BNI DR corrected tracked available DR capacity with values before and after correction, showing a 3% increase in new available capacity and a 2% increase in total available capacity. Appendix VII provides an example calculation for available DR capacity.

As per the definition agreed upon by E1 and NSP, available DR capacity is evaluated based on events called from December to February excluding weekends and holidays. Events called at any time during the day (morning or evening)[4](#page-46...

AI summary The document outlines how available DR capacity is calculated based on participant performance during events called from December to February, excluding weekends and holidays. It explains that only participants called to events are included in the calculation and highlights two instances in 2024 where participants were not called for certain events.

better suited to morning or to evening events, and for each event, E1 can decide to only call participants that are better suited to that time, or to call all participants to take part in that event. To illustrate how available DR capacity...

AI summary The text discusses the calculation of available DR capacity by segmenting events into morning and evening peak times, with E1 able to target specific participant groups. An example is provided using five participants and five events to illustrate the method, though the numbers are for illustrative purposes only.

Table 1: Available DR Capacity Calculation Example Type of Load Reduction per Participant per Event (kW) Event Event Participants for Which the Period Event Was Called Part. #1 Part. #2 Part. #3 Part. #4 Part. #5 Sum of Load Reduction per...

AI summary Table 1 presents an example calculation of available DR capacity, showing that E1 sums the average available DR capacity of each participant rather than the event average. The resulting available DR capacity is 2,667 kW, which differs from the average load reduction per event of 1,952 kW.

APPENDIX VIII BNI DR: Baseline Considerations

AI summary This appendix outlines baseline considerations for the BNI DR (Business, Non-profit, and Institutional Demand Response) program, likely addressing eligibility criteria, performance metrics, or program design elements within a regulatory proceeding context.

General Guidelines The general guidelines that serve as the de facto assumptions for any DR M&V are presented below: - › High 6 of 10 baseline - › Additive adjustment - › Adjustment lookback window spans two hours - › Adjustment lookback w...

AI summary The guidelines outline baseline assumptions for Demand Response (DR) Measurement and Verification (M&V), including a 6/10 baseline threshold, symmetric adjustments capped at ±20%, a two-hour lookback window starting three hours pre-event, exclusion of holidays/weekends, and additive adjustment methodology.

Additive Adjustment Additive adjustments involve a fixed kW adjustment across all event time intervals. It's suitable for situations where there's a known, constant change in the load that is not proportional to the baseline, like the addi...

AI summary Additive adjustments involve a fixed kW adjustment across all event time intervals, suitable for constant load changes not proportional to the baseline, such as adding/removing equipment. Examples include industrial processes running on event days not present in baseline days or shutdowns on slow days.

Scalar Adjustment Scaler adjustment are a percentage multiplier applied across all event time intervals. It's appropriate when the baseline needs to be adjusted proportionally due to predictable changes that affect the entire load profile,...

AI summary Scalar adjustment involves applying percentage multipliers across all event time intervals to proportionally adjust baselines for predictable load profile changes, such as those caused by operational hour variations or production level shifts. An example highlights HVAC load sensitivity to outdoor temperature during a cold day.

Set Timing Align the timing of the lookback window with the DR event's expected impact on consumption.

AI summary The text emphasizes aligning the lookback window's timing with the anticipated impact of Demand Response (DR) events on consumption, ensuring accurate evaluation of DR program effectiveness.

Prevent Overcompensation It should prevent overcompensation for reductions that would have occurred without the DR event.

AI summary The text emphasizes the need to prevent overcompensation for energy reductions that would have occurred naturally without demand response (DR) interventions, ensuring program incentives align with actual program contributions.

Based on Historical Data The cap can be based on a percentage of historical adjustments observed during similar DR events or established through statistical analysis.

AI summary The cap for adjustments can be determined using historical data from similar demand response (DR) events or through statistical analysis, providing a methodological basis for establishing limits.

Consider Program Goals The cap should align with the overall goals of the DR program, whether it's peak shaving, load shifting, or emergency response.

AI summary The cap should align with the DR program's goals, including peak shaving, load shifting, and emergency response, to ensure effectiveness and alignment with overall program objectives.

Symmetry Adjustments are applied symmetrically, i.e. results could go up or down. The symmetric approach considers that day-of conditions can have a real impact on customer demand in both directions and therefore it can be argued that symm...

AI summary Symmetric adjustments in demand baseline calculations account for both upward and downward demand fluctuations, enhancing accuracy. A ±20% cap from General Guidelines mitigates risks from downward adjustments. The text proposes shifting the adjustment window timing to improve factor accuracy.

Business Rules 5) If the lookback window provides an abnormal positive or negative adjustment factor, the event should be flagged and considered with more detailed, as a new adjustment period may need to be selected, including the hours pr...

AI summary The text outlines procedures for handling abnormal adjustment factors in a lookback window, recommending detailed analysis and potential reselection of adjustment periods. It emphasizes evaluating whether events like building opening/closing times are valid comparison points for load accuracy.

Exclusion rules Exclusion rules – Some days are excluded from consideration such as holidays, previous DR event days, weekends, thresholds and scheduled shutdowns (as these are not representative of "normal" operation). Example: A facility...

AI summary Exclusion rules specify that certain days (e.g., holidays, weekends, previous DR events, shutdowns) are excluded from baseline calculations to ensure accuracy, as they do not reflect normal operations. An example illustrates a facility closure due to renovations leading to abnormally low energy usage, which should be excluded.

Business Rules 6) If there are known irregularities in customer usage that are not representative of typical operation, such days should be excluded from the baseline calculation. Rationale for exclusion must be documented in CIS for that...

AI summary Irregular customer usage days should be excluded from baseline calculations if they are not representative of typical operations, with documentation required in CIS. This ensures accurate baseline data for energy efficiency programs and regulatory compliance.

Presented below is the template used by the Evaluator to review projects to determine evaluated savings. PID Customer Name Agreed Curtailment (kW) Event 1 Event 2 Event 3 Event 4 Event 5 Event 6 Event 7 Event 8 Event 9 Event 10 Average Sav...

AI summary The document presents a template for evaluating energy savings from demand response (DR) projects under the BNI DR program. It includes fields for tracking customer names, curtailment agreements, savings calculations, and adjustments based on guidelines.

This appendix summarizes all the recommendations made by the Evaluator as part of the 2024 evaluation of BNI DR. Section Recommendations Executive Summary 2024 BNI DR Recommendation 1: Update the BNI DR Baseline Considerations document to...

AI summary This appendix outlines two key recommendations from the 2024 evaluation of the BNI DR program. The first recommendation focuses on updating the BNI DR Baseline Considerations document to address issues related to building operating hours, lookback windows, and event exclusions due to missing AMI data. The second recommendation suggests continuing to use project reviews to evaluate available DR capacities in future evaluations.

2024-2025 DSM MEASURE ASSESSMENT Final Report 2024 EVALUATION EDITION March 25, 2025

AI summary The 2024-2025 DSM Measure Assessment Final Report evaluates demand-side management initiatives in Nova Scotia. It is presented as the 2024 Evaluation Edition, dated March 25, 2025, though no detailed content is provided in the excerpt.

Purpose The objectives of this document are to: - › Ensure consistency in gross savings values throughout the three-year DSM cycle and thus improve E1's ability to define and track targets for energy and peak demand savings - › Consolidate...

AI summary This document aims to ensure consistency in gross savings values across E1's three-year DSM cycle, enhancing its ability to track energy and peak demand savings targets. It also consolidates these values into a single reference document for program staff and to populate E1's internal e-Technical Reference Manual (e-TRM).

Use and Application For the evaluations conducted during the last two years of the 2023-2025 demand-side management (DSM) cycle, the Evaluator will refer to the values presented in the 2024-2025 DSM MA. The MA includes the following elemen...

AI summary The evaluation of the 2023-2025 DSM cycle references the 2024-2025 DSM MA, which includes interactive effects, peak demand ratios, installation rates, unitary savings, and EUL values. DR measures are distinguished from demand reduction measures by their event-dependent savings generation.

Development and Review Process Savings are established using one or more of the following approaches: Literature reviews of TRMs; metering studies and evaluation reports; engineering calculations; adjustments based on data collected throug...

AI summary The document outlines methods for establishing savings in the Measure Assessment (MA), including data collection approaches and parameter calculation guidelines. It details the use of three-year averages for most parameters, exceptions for rapidly changing data, and updates to specific parameters in the 2024 MA. New measures added in 2024 are also highlighted.

Electrical Energy Savings The following equation is used to calculate electrical energy savings. (ℎ) - = (ℎ) × (1 - + (%)) × " " " - − " (%) 1 The average retired appliance efficiency levels and sizes are likely to evolve overtime as the y...

AI summary The text provides an equation for calculating electrical energy savings, factoring in appliance efficiency and size changes over time. It references the 2024-2025 DSM Measure Assessment, noting that retired appliance efficiency levels evolve as manufacturing years become more recent.

Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand of the electricity system. The projected electricity peak demand period in Nova Scotia is between 5 p.m. and 7 p.m. from De...

AI summary Peak demand savings refer to electricity demand reductions during Nova Scotia's peak period (5-7 p.m., Dec-Feb non-holiday weekdays). The text outlines a calculation method for quantifying these savings, though the formula is partially obscured in the original document.

Available Demand Response Capacity Available DR capacity differs from peak demand savings since the former considers the potential load reduction that was made available instead of the actual load reduction that coincided with the actual u...

AI summary Available DR capacity measures potential load reduction during scheduled events, excluding weekends and holidays, with capacity evaluated over the first hour or two hours of each event. E1 calculates total available DR capacity by summing individual participant contributions based on average demand reductions.

1 Residential Measure Assessment Scope [Table](#page-85-2) 2 below lists the residential measures and associated programs included in the 2024-2025 DSM MA. The MA includes all necessary parameters and calculations to obtain gross energy an...

AI summary The document outlines the residential measure assessment scope for the 2024-2025 Demand-side Management Measure Assessment (DSM MA), including parameters for calculating energy and peak demand savings for prescriptive, semi-prescriptive, and custom measures. Prescriptive measures use fixed assumptions, while custom measures use unit-specific inputs.

Table 2: Included Residential Measures Measure Program Component Lighting LED Lamps EPI Instant Savings LED Fixtures Instant Savings Dimmer Switches Instant Savings Motion Sensors Instant Savings LED Nightlights EPI Solar Fixtures Instant...

AI summary Table 2 lists various residential energy efficiency measures and their associated program components, including lighting, water heating, space heating, appliances, and demand reduction. Each measure is linked to specific programs such as EPI, Instant Savings, Green Heat, and others.

Interactive Effects on Electrical Heating The Hydro-Québec study found that efficient lighting installed in electrically heated single-family homes without air conditioning results in an interactive effects factor for heating of -58%. 4 Ba...

AI summary A Hydro-Québec study found that efficient lighting in electrically heated single-family homes without AC creates a -58% interactive effects factor for heating. The analysis assumes 10% heat loss through exterior walls/ceilings does not contribute to these effects. The 2024-2025 DSM Measure Assessment references data from EPI participants and excludes 2021 data due to skewed results.

Peak Demand The Hydro-Québec report assumes that 10% of the heat is released through exterior walls and ceilings and does not contribute to interactive effects. Since the peak demand period occurs during the heating period when lighting an...

AI summary The Hydro-Québec report assumes 10% heat loss through exterior walls/ceilings in electrically heated homes, leading to a -90% interactive effects factor for peak demand savings. This factor also applies to heat pumps, assuming 100% efficiency during peak cold conditions when heating and lighting systems are active.

2.1.2 Peak Demand Savings Factors For all indoor and outdoor LED lamps, nightlights, and fixtures, the peak demand-to-energy ratio is based on the Northeast Residential Lighting Hours-of-Use (NERHOU)[13](#page-92-2) study, which establishe...

AI summary The text discusses peak demand savings factors for lighting measures, referencing studies and data from the NERHOU and EPI Residential Lighting Metering Study. It recommends using a peak demand-to-energy ratio of 0.162 W/kWh and references the 2016-2018 DSM Plan by Navigant for motion sensors.

Table 6: Peak Demand-to-energy Ratios for Residential Lighting Measures Measure Peak Demand-to energy Ratio (W/kWh) Source LED Lamps 0.162 Northeast Residential Lighting Hours-of-Use Study LED Fixtures LED Nightlights Dimmer Switches Solar...

AI summary Table 6 presents peak demand-to-energy ratios for various residential lighting measures, including LED lamps and motion sensors, with sources cited. The table highlights efficiency metrics for different lighting technologies and control systems.

Table 7: LED Lamp Measure Summary Parameter EPI Instant Savings Reference Measure Description and Identification Measure LED lamps (A-type, Reflector, and Decorative) with direct installation ENERGY STAR Certified LED non-A-type Lamps (R,...

AI summary Table 7 summarizes the LED lamp measure, including parameters such as installation rates, energy savings, and interactive effects. It compares different programs like EPI and Instant Savings, providing details on baseline lamps, energy savings in kWh/year, and peak demand-to-energy ratios.

Table 8: Electrical Unitary Energy Savings Values for LED Lamps Old New Displaced Operating Unitary Type of LED Wattage Wattage Wattage Hours Savings Value (W) (W) (W) (hrs/day) (kWh/year) EPI 9 W Replacing 25 W 25 9 16 2.6 15.2 9 W Replac...

AI summary Table 8 presents electrical unitary energy savings values for LED lamps, comparing old and new wattages, displaced wattage, operating hours, and annual energy savings in kWh. The table includes various LED replacements, such as 9 W and 9.5 W lamps replacing higher wattage bulbs, as well as specific programs like EPI and Instant Savings. It also notes corrections from the 2024 DSM evaluation.

ation requirements as the baseline. This methodology is recommended in the principles of the Uniform Methods Project (UMP)[16](#page-95-0) to better represent the real baseline wattages for LED lamps. Since R, BR, and decorative lamps are...

AI summary The text discusses methodology for calculating baseline wattages for LED lamps, referencing the Uniform Methods Project (UMP). It notes assumptions about incandescent-to-LED replacements, regulatory wattage limits for ST19 and PAR lamps, and the continuation of E1 in Instant Savings despite incandescent lamp unavailability.

Table 9: Electrical Unitary Peak Demand Savings Values for LED Lamps Type of LED Unitary Energy Savings Value (kWh/year) Peak Demand-to energy Ratio (W/kWh) Unitary Peak Demand Savings Value (W/year) EPI 9 W Replacing 25 W 15.2 0.162 2.46...

AI summary Table 9 presents electrical unitary peak demand savings values for various LED lamps, including different wattage replacements and types. The table includes energy savings, peak demand-to-energy ratios, and unitary peak demand savings values for a range of LED lamps, such as EPI, PAR20, PAR30, PAR38, and GU10. These values are used to assess the impact of LED lamp installations on energy efficiency and demand reduction.

Summary Table 14 presents a summary of the values used to calculate dimmer switch savings. The detailed methodology follows.

AI summary Table 14 provides a summary of values used to calculate dimmer switch savings, with a detailed methodology outlined subsequently.

Table 14: Dimmer Switch Measure Summary Parameter Instant Savings EPI Reference Measure Description and lo dentification Measure Indoor dimmer switches r Savings) or with direct ins • - Baseline Existing indoor fixture without dimmer contr...

AI summary Table 14 summarizes the energy and demand savings associated with the installation of indoor dimmer switches. It includes parameters such as unitary energy savings, peak demand savings, and interactive effects factors, providing a detailed breakdown of the efficiency improvements and potential impacts on heating and cooling systems.

Table 16: Motion Sensor Measure Summary Parameter Instant Savings EPI Reference Indoor Motion Sensor Indoor Motion Sensor with Dimmer Switch Outdoor Motion Sensor Indoor Motion Sensor Outdoor Motion Sensor Measure Descrip tion and I dentif...

AI summary Table 16 presents a summary of motion sensor measures, including parameters such as installation rates, energy savings, and interactive effects factors for both indoor and outdoor sensors. The table compares different types of motion sensors and their associated energy and demand savings, providing details on their effective useful life and baseline conditions.

Table 18: Unitary Savings Values for Indoor Motion Sensors with Dimmer Switch Parameter Value Reference Average Wattage [W] 2 x 21.6 W = 43.2 W Weighted average wattage per installed lamp of different lighting technologies observed in resi...

AI summary Table 18 presents unitary savings values for indoor motion sensors with dimmer switches, including parameters such as average wattage, dimmed wattage, daily hours of operation, and energy savings calculations. These values are based on references from the United States Department of Energy and the Ontario Power Authority.

Table 19: Unitary Savings Values for Outdoor Motion Sensors Parameter Instant Savings EPI Reference Average Wattage [W] (85% x 2 x 44.6 W) + (15% x 2 x 21.6) = 82.3 W 2 x 15 W = 30.0 W Assumed two controlled lamps per motion sensor Instant...

AI summary Table 19 provides unitary savings values for outdoor motion sensors, including average wattage, operating times, and energy savings calculations. It references the 2011 OPA Prescriptive Measures and Assumptions List and the United States Department of Energy for data on lamp wattages and operating times. The table also mentions the 2024-2025 DSM Measure Assessment.

Table 24: Solar Fixture Measure Summary Parameter Instant Savings EPI Reference Measure Description and Identification Measure LED solar fixtures rebate with direct installation (Ef d in store (Instant Savings) or PI) - Baseline Standard-c...

AI summary Table 24 presents a summary of the Solar Fixture Measure, including parameters such as installation rates, energy savings, and peak demand savings. The table compares baseline and measure descriptions, and provides details on energy efficiency metrics like unitary energy savings and peak demand-to-energy ratios.

2.2 Water Heating

AI summary Section 2.2 outlines water heating programs and initiatives, referencing acronyms like DSM, ARet, and CGH Grant. It highlights regulatory frameworks and efficiency measures, though no explicit arguments or stakeholder positions are presented in the provided text.

For most water heating measures, peak demand savings are calculated using the peak demand-to-energy ratios developed by Navigant in the 2016-2018 DSM Plan. These ratios were established for various product categories based on modelled syst...

AI summary The document discusses the calculation of peak demand savings for water heating measures using ratios developed by Navigant for the 2016-2018 DSM Plan. It notes that solar domestic hot water systems do not provide peak demand savings and recommends using the same ratios for the 2023-2025 DSM cycle.

Table 29: Peak Demand-to-energy Ratios for Water Heating Measures Measure Peak Demand-to energy Ratio (W/kWh) Source Drain Water Heat Recovery 0.162 RES-Water Heat, Navigant 2016-2018 DSM Plan Heat Pump Water Heater Low-flow Showerhead Fau...

AI summary Table 29 presents peak demand-to-energy ratios for various water heating measures, including Drain Water Heat Recovery and Solar Domestic Hot Water. The table highlights that solar DHW systems provide no peak demand savings during peak periods as the sun sets. The section '2.2.3 Water Heating Measures' discusses these measures in detail.

Unitary Peak Demand Savings Solar DHW systems provide no peak demand savings because the sun has set during the peak demand period.

AI summary Solar DHW systems do not provide peak demand savings because they are inactive during peak demand periods when the sun has set, limiting their contribution to demand-side management initiatives.

The unitary savings for HPWHs are taken from a metering study conducted for NEEA. [54](#page-119-0) They include interactive effects. Instant Savings Average Unitary Energy Savings is a weighted average based on the share of space heating...

AI summary The document discusses unitary savings for heat pump water heaters (HPWHs) derived from a metering study by NEEA. It highlights the calculation of average unitary energy savings, considering the share of space heating systems in Nova Scotia and the type of water heaters replaced. A table provides equation parameters and resulting savings values.

Table 44 lists the installation rate for thermostatic shower valves. & lt;sup>74 Pennsylvania Public Utility Commission, Technical Reference Manual Volume 2: Residential Measures , September 2024, p. 82. Final Report 48 2024-2025 DSM Measu...

AI summary The text references a table listing the installation rate for thermostatic shower valves and includes citations from technical manuals and studies related to residential water use and efficiency measures.

2.3.2 Peak Demand Savings Factors For most space heating measures, peak demand savings are not calculated using a peak demand-to-energy ratio. For more details, refer to Subsection [1.1.1(1)](#page-136-3) for mini-split heat pumps, Subsect...

AI summary The document discusses the calculation of peak demand savings factors for various space heating measures. It notes that for most measures, peak demand savings are not calculated using a peak demand-to-energy ratio, while for air sealing products, ratios established by Navigant in the 2016-2018 DSM Plan are recommended for use in the 2020-2023 DSM cycle. Programmable and smart thermostats are assumed to have nil peak demand savings unless part of a demand response program.

For Green Heat, the electrical unitary energy savings for MSHPs are based on the billing analysis results of the 2024 Green Heat evaluation, which yielded savings per unit of capacity for both homes that were fully electrically heated and...

AI summary The document outlines how energy savings for Green Heat, HEA, and MHEEP are calculated. For Green Heat, savings are based on billing analysis results from the 2024 evaluation, while for HEA and MHEEP, savings are derived from HOT2000 simulation outputs adjusted by ratios from the same evaluation. Different methods are used depending on whether homes are fully or mainly electrically heated.

$Peak \ Demand \ Savings_W = Previous \ Peak \ Demand \ Savings_W \times \frac{Energy \ Savings_{kWh}}{Previous \ Energy \ Savings_{kWh}}$ Table 61: Unitary Peak Demand Savings Values for Wood or Pellet Stoves and Fireplace Inserts Green n...

AI summary The document provides a formula for calculating peak demand savings and includes a table with values for wood or pellet stoves and fireplace inserts. It references past and current demand-side management (DSM) measures and their energy savings.

For Green Heat, unitary peak demand savings are based on program design data.[92](#page-148-2) It is assumed that during the peak demand period, existing ASHPs operate exclusively on the electric resistance backup in the air handler. Thus,...

AI summary The document discusses the calculation of unitary peak demand savings for Green Heat, assuming existing air-source heat pumps (ASHPs) use electric resistance backup during peak demand. It notes that peak demand savings for wood and pellet boiler and furnace measures with an ASHP baseline are the same as with an electric resistance baseline.

Summary [Table](#page-155-3) 71 presents a summary of the values used to calculate programmable thermostat savings. The detailed methodology follows.

AI summary Table 71 summarizes the values used to calculate programmable thermostat savings, with a detailed methodology provided afterward.

105 Apex Analytics LLC, Energy Trust of Oregon Nest Thermostat Heat Pump Control Pilot Evaluation , October 10, 2014, p. 123. [Table](#page-160-1) 75 below presents the variables used for the unitary savings calculation and the resulting v...

AI summary The text references a study by Apex Analytics LLC on a heat pump control pilot program and mentions a table presenting variables for unitary savings calculation. No direct arguments or entities are discussed in the provided text.

2.4.1 Interactive Effects Retiring old appliances causes an increase in the heating load in the winter and a decrease in the cooling load in the summer since compressors on old appliances release significantly more waste heat than newer, m...

AI summary Retiring old appliances increases heating loads in winter and decreases cooling loads in summer, but interactive effects are largely offset by factors such as the proportion of households using electric heating, appliance placement, and air conditioning usage. Overall, interactive effects are considered negligible, leading to a 0% factor for energy and peak demand savings.

For most appliances, peak demand savings are calculated using the peak demand-to-energy ratios developed by Navigant in the 2016-2018 DSM Plan. These ratios were established for various product categories based on modelled system-coinciden...

AI summary The text discusses the methodology for calculating peak demand savings using peak demand-to-energy ratios developed by Navigant for the 2016-2018 DSM Plan. These ratios are recommended for continued use in the 2020-2023 DSM cycle, with specific exceptions for certain appliances like clotheslines and retired air room conditioners.

Table 80: Peak Demand-to-energy Ratios for Appliances Measure Peak Demand to-energy Ratio (W/kWh) Source Clothesline and Outdoor Drying Racks 0.000 Calculated by the Evaluator Refrigerator Retirements/Replacements 0.138 RES-Appliance-Fridg...

AI summary Table 80 presents peak demand-to-energy ratios for various appliances, calculated by the Evaluator or based on specific data sources like the NREL ResStock end-use load profiles. The data includes ratios for refrigerators, freezers, dehumidifiers, and other appliances, with sources citing the Navigant 2016-2018 DSM Plan and other studies.

(2) Refrigerator Retirements/Replacements

AI summary The section discusses refrigerator retirements/replacements programs, focusing on Appliance Retirement (ARet) initiatives under Efficiency Nova Scotia (ENS) and Nova Scotia Power (NSP). It highlights energy efficiency gains, GHG emission reductions, and alignment with DSM goals.

t factor is based on the metering protocol of the New York Department of Public Service[128](#page-170-7) and the part-use factor is calculated based on 2022 participant survey data.[129](#page-170-8) For refrigerators replaced through Hom...

AI summary The calculation of unitary savings for refrigerators replaced via HomeWarming and MHEEP uses 1994-era energy consumption data, assumes primary refrigerator replacements, and relies on EnerGuide and ENERGY STAR product ratings from tracking sheets (2021–2023). Part-use factors are omitted due to program criteria.

nt of Public Service, New York Standard Approach for Estimating Energy Savings from Energy Efficiency Programs - Residential, Multi-Family, and Commercial/Industrial Measures, April 15, 2019, p. 29. 129 Econoler, Residential Efficient Prod...

AI summary The text references a report on estimating energy savings from efficiency programs and a DSM evaluation report, including a table with parameters and unitary savings calculations for refrigerator retirement and replacement measures.

t factor is based on the metering protocol of the New York Department of Public Service[138](#page-173-7) and the part-use factor is calculated based on 2022 participant survey data.[139](#page-173-8) For freezers replaced through HomeWarm...

AI summary The text details methodology for calculating unitary energy savings for freezers replaced via HomeWarming and MHEEP programs, using 1994-era freezer data and EnerGuide ratings. It notes no part-use factor applies due to primary freezer replacement criteria and cites data sources including survey results and ENERGY STAR tools.

(5) Dehumidifier Replacements or Retirements/ENERGY STAR Certified Dehumidifiers

AI summary The section discusses dehumidifier replacements or retirements and ENERGY STAR certified dehumidifiers, likely within the context of energy efficiency programs. Key themes include appliance standards, demand-side management, and energy efficiency initiatives. No detailed content is provided in the text chunk.

The electrical unitary energy savings of the ENERGY STAR certified clothes dryer measure are calculated using the equations below. Energy Savings $$_{kWh} = ADL \times ALW \times \left(\frac{1}{CEF_{base}} - \frac{1}{CEF_{new}}\right)$$ Th...

AI summary This text discusses the calculation of electrical unitary energy savings for ENERGY STAR certified clothes dryers using equations involving average daily loads and combined energy factors. It references data from Natural Resources Canada and outlines the parameters used in the 2024-2025 DSM Measure Assessment.

Adjusted Energy Consumption [Table](#page-183-0) 97 and [Table](#page-183-1) 98 present the calculations of total electricity consumption for baseline and efficient clothes washers respectively. 158 Ad Hoc Recherche, Rapport d'évaluation,...

AI summary The document discusses the calculation of electricity consumption for baseline and efficient clothes washers, referencing reports and data from the EPI program. It highlights discrepancies in data from NRCan and the use of updated EPI tracking sheets from 2022 and 2023 due to program changes in 2021.

2.5 Plug Load Controls

AI summary The section titled '2.5 Plug Load Controls' is introduced, though no further details or content are provided in the text. The topic likely relates to energy efficiency measures targeting plug loads within demand-side management initiatives.

Table 116: Interactive Effects Factors for Plug Load Control Measures Measure Interactive Effects Factor for Energy Savings Interactive Effects Factor for Peak Demand Savings Reference Smart Power Controller for Audiovisual Equipment 0% 0%...

AI summary The text presents Table 116, which outlines interactive effects factors for plug load control measures, focusing on energy savings and peak demand savings. The table includes measures such as smart power controllers and timers, though many entries are incomplete. The section also references peak demand savings factors in 2.5.2.

For most plug load control measures, peak demand savings are calculated using the peak demand-to-energy ratios developed by Navigant in the 2016-2018 DSM Plan. These ratios were established for various product categories based on modelled...

AI summary The document discusses the methodology for calculating peak demand savings for plug load control measures using peak demand-to-energy ratios from the 2016-2018 DSM Plan. It notes that ENERGY STAR certified variable speed pool pumps have a zero ratio due to their usage patterns. A table summarizes the established ratios for each measure.

Table 117: Peak Demand-to-energy Ratios for Plug Load Control Measures Measure Peak Demand-to-energy Ratio (W/kWh) Source Smart Power Controller for Audiovisual Equipment 0.000 RES-Plug Load Controls, Navigant Power Bar with Integrated Tim...

AI summary Table 117 presents peak demand-to-energy ratios for various plug load control measures, including a smart power controller and an ENERGY STAR certified pool pump. The table includes sources such as RES-Plug Load Controls and Navigant, and references the 2016-2018 DSM Plan.

2.6 Demand Reduction For one category of demand reduction measures, namely demand response measures, the peak demand savings are replaced by the available DR capacity that differs from peak demand savings because the available DR capacity...

AI summary The text clarifies that for demand response (DR) measures, available DR capacity differs from peak demand savings by considering potential load reduction rather than actual load reduction during utility peaks.

2.6.1 Interactive Effects For demand reduction measures, interactive effects are assumed to be nil.

AI summary The analysis assumes no interactive effects for demand reduction measures within the Nova Scotia regulatory proceeding, focusing on the absence of synergistic or conflicting impacts between such measures.

2.6.2 Peak Demand Savings Factors For demand reduction measures, peak demand savings are not determined using a peak demand-to-energy ratio since they do not generate energy savings. For more details, refer to Subsections [1.1.1(1),](#page...

AI summary The document explains that peak demand savings for demand reduction measures are not calculated using a peak demand-to-energy ratio, as these measures do not generate energy savings. It references Subsections 1.1.1(1) to (4) for further details.

Table 127: Three-element Water Heater Measure Summary Parameter Green Heat Reference Measure Description and Identification Measure Description Three-element water heaters rebated after purchase - Baseline Conventional two-element water he...

AI summary The table outlines a program offering rebates for the purchase of three-element water heaters, with a focus on peak demand savings. No energy savings are expected from this measure, and the baseline is conventional two-element water heaters. The effective useful life is 12 years, and the unitary peak demand savings are 200 W.

Table 128: Domestic Water Heater Timer Measure Summary Parameter Green Heat Reference Measure Description and Identification Measure Description Domestic water heater timers rebated after purchase - Baseline Electric water heaters without...

AI summary Table 128 summarizes the Domestic Water Heater Timer Measure, including details such as installation rate, useful life, and peak demand savings. The measure involves rebating timers for electric water heaters, with a focus on energy efficiency and demand management.

Summary [Table](#page-6-1) 130 presents a summary of the values used to calculate domestic water heater load control savings. The detailed methodology follows.

AI summary Table 130 summarizes the values used to calculate domestic water heater load control savings, with a detailed methodology provided thereafter.

Table 130: Domestic Water Heater Load Control Measure Summary Parameter Demand Response Reference Measure Description and Identification Measure Direct load control for domestic water heaters with direct installation - Baseline Domestic wa...

AI summary Table 130 outlines the Domestic Water Heater Load Control Measure Summary, including parameters like installation rate, effective useful life, and available DR capacity. It provides details on energy savings and demand response for direct load control measures applied to domestic water heaters.

Table 131: Domestic Water Heater Load Control Measure In-service Rates Type In-service Rate Margin of Error Source Shifted 81% 4% Residential DR 2023 Evaluation 2.7 Renewables

AI summary Table 131 presents the in-service rate for the Domestic Water Heater Load Control Measure at 81% with a 4% margin of error, sourced from the Residential DR 2023 Evaluation. The section also introduces a discussion on renewables under section 2.7.

3 Effective Useful Life This section outlines the EUL values used to calculate lifetime energy savings. This section also presents EUL values for demand reduction measures; these values are not used to calculate lifetime energy savings sin...

AI summary This section defines Effective Useful Life (EUL) values for calculating lifetime energy savings and demand reduction measures. It clarifies that EUL for demand response measures reflects the persistence of demand reduction, not energy savings, and emphasizes using EUL in cost-effectiveness ratio calculations to capture lifetime benefits.

[Table](#page-17-0) 137 below lists the commercial measures and associated programs included in the 2024- 2025 DSM MA. The MA includes all necessary parameters and calculations to obtain gross energy and peak demand savings for all portfol...

AI summary The text discusses the 2024-2025 DSM MA, which includes parameters and calculations for energy and peak demand savings across various measures. It highlights the inclusion of new semi-prescriptive measures and the use of effective useful life values. Savings calculations differ between prescriptive, custom, and semi-prescriptive measures.

Table 137: Included Commercial Measures Program Components Lighting LED Lamps SBES LED Linear Fixtures BER-IR, BER-AR, SBES LED Linear Lamps BER-IR, BER-AR, SBES LED Outdoor Fixtures BER-IR, BER-AR, SBES LED Directional and Architectural F...

AI summary Table 137 outlines included commercial measures under various program components, such as lighting, pumps, heating, HVAC, water heating, and compressed air. Each component is associated with specific programs like BER-AR, SBES, and Custom, indicating the initiatives supporting these measures.

5.1.1 Interactive Effects The interactive effects methodology for commercial lighting was reviewed as part of the 2024 DSM Measure Assessment update. The updated interactive effect factors and methodology are presented in [Appendix IV.](#p...

AI summary The interactive effects methodology for commercial lighting was reviewed in the 2024 DSM Measure Assessment update. Updated factors will be applied in 2025, aligning with BER-AR adjustment ratios. The 2024 factors remain unchanged from 2023 and are based on building type and heating/cooling systems.

Table 138: BER-AR Interactive Effects (IE) Factors for Lighting IE Energy Building Type IE Peak Demand (if electrical heating) Electrical Heating Electrical Cooling Electrical Heating and Cooling Agriculture 0.75 0.81 1.04 0.85 Banking / F...

AI summary The document discusses interactive effects factors for lighting in different building types, based on data from BER-AR and SBES programs. These factors are used to calculate energy and peak demand savings, with adjustments made for recessed fixtures. The methodology follows ASHRAE guidelines and applies a 57% multiplier for limited interaction with heating and cooling systems.

Table 140: Interactive Effects for BNI Lighting Measures Measure Interactive Effects Factor for Energy Savings Interactive Effects Factor for Peak Demand Savings BER Instant Rebates Linear LED Fixtures Recessed -8.7% x 57% = -4.9% -16.1% x...

AI summary Table 140 presents interactive effects factors for energy and peak demand savings associated with various BNI lighting measures, including LED fixtures, occupancy sensors, and other efficiency initiatives, showing varying degrees of impact depending on the type and installation.

Unitary Peak Demand Savings Calculations Peak demand savings are calculated by multiplying the unitary demand savings value by the peak coincidence factor as detailed in the equation below. () = () × (%) The unitary demand savings value co...

AI summary Peak demand savings are calculated by multiplying unitary demand savings by a peak coincidence factor. Unitary demand savings are derived by dividing unitary energy savings (in kWh) by hours of use (HOU), then converting to watts (W) via multiplication by 1,000. The methodology emphasizes quantifying energy efficiency impacts through standardized formulas.

Table 144: Electrical Unitary Peak Demand Savings Values for LED Lamps Type of LED Displaced Wattage (W) Peak Coincidence Factor (%) Unitary Peak Demand Savings Value (W) 9 W Replacing 25 W 16 54% 8.6 9 W Replacing 29 W 20 10.8 9 W Replaci...

AI summary Table 144 presents electrical unitary peak demand savings values for various LED lamps replacing traditional incandescent bulbs. The table includes different types of LEDs, the wattage they replace, peak coincidence factors, and corresponding savings in wattage.

Unitary Peak Demand Savings $$[kW] = \frac{(W_b[W] \times Qty_b[-] - W_e[W] \times Qty_e[-]) \times PCF \times IE_{PD}}{1,000}$$

AI summary The formula calculates unitary peak demand savings in kilowatts, factoring in baseline and expected wattage, quantities, a power conversion factor, and a peak demand efficiency factor.

5.2.2 Peak Demand Savings Factors For pumps, the methodology used to determine peak demand savings is detailed in the measure-specific sections below.

AI summary The section outlines that methodology for determining peak demand savings for pumps is detailed in measure-specific sections, indicating a focus on technical evaluation processes for energy efficiency measures.

Unitary Demand Savings [W] = $(Unitary\ Energy\ Savings\ [kWh])/(HOU\ [h] \times 1,000) \times Peak\ Coincidence\ Factor\ [(\%)]$ Since circulator pumps are typically used as part of residential and commercial hydronic heating system appli...

AI summary The document explains how unitary demand savings are calculated for circulator pumps, using a formula that incorporates unitary energy savings, hours of use, and a peak coincidence factor. A peak coincidence factor of 100% is applied due to continuous operation during winter, and calculations are based on parameters in Table 170.

Peak Demand Savings W = $$(172 \times HP_{ee} + 671 \times \Delta HP_{sys}) \times PCF$$

AI summary The formula provided calculates Peak Demand Savings using variables such as HPee, ΔHPsys, and PCF, indicating a technical approach to assessing energy efficiency impacts on peak demand.

Table 173: Unitary Peak Demand Savings Values for Booster Pumps Parameter Symbol BER-AR Reference Annual Unitary Demand Savings per Rated Horsepower from the Use of a VFD Booster Pump [W/HP] - 172 2020 Hawaii TRM219 Annual Unitary Demand S...

AI summary Table 173 presents unitary peak demand savings values for booster pumps, including annual demand savings per rated horsepower, peak coincidence factor, and calculated unitary peak demand savings. The data is sourced from a 2020 Hawaii TRM219 and calculated using project data from eight Custom Retrofit booster pump projects.

5.3.2 Peak Demand Savings Factors For all HVAC measures, the methodology used to determine peak demand savings is detailed in the measure-specific sections below.

AI summary The section outlines the methodology for determining peak demand savings for HVAC measures, referencing measure-specific details in subsequent sections. It does not present specific arguments, entities, or citations.

2024-2025 DSM Measure Assessment Final Report 173 224 Supply fan energy savings factors were calculated based on the difference between the normalized energy savings per cooling capacity values for the Chicago DCV and VFD w/3-speed fan con...

AI summary The document discusses the calculation of energy savings factors for supply fan, cooling, and heating energy based on data from the 2021 Illinois TRM and climate-specific degree days for Chicago and Halifax. The calculations involve normalized energy savings values and COP assumptions.

Table 179: Unitary Peak Demand Savings Values for Advanced RTU Controls Parameter Symbol BER-AR Reference Demand Savings Factor [kW/hp] 𝐷𝑆𝑉𝐺 0.252 2020 Massachusetts Peak Coincidence Factor [-] 𝑃𝐶𝐹 1.0 TRM230 Conversion Factor [W/kW] - 1,0...

AI summary Table 179 presents unitary peak demand savings values for advanced RTU controls, including parameters like demand savings factor, peak coincidence factor, and calculated unitary peak demand savings based on specification data for rebated units. The reference is to the 2020 Massachusetts Technical Reference Manual.

The electrical unitary energy savings for smart thermostats for electric baseboards for commercial applications are assumed to be equal to the savings for a residential application because the heating power of controlled thermostats is exp...

AI summary The text discusses the assumption that smart thermostat energy savings for commercial electric baseboards are similar to residential applications. It references a formula for calculating energy savings and cites studies on Nest thermostats for central heating systems, noting a 12% savings for air-source heat pumps as a relevant benchmark.

[Table](#page-58-0) 182 below presents the variables used for the unitary savings calculation and the resulting value per thermostat. 231 Apex Analytics LLC, Energy Trust of Oregon Nest Thermostat Heat Pump Control Pilot Evaluation , Octob...

AI summary The text references two studies on thermostat savings, including an evaluation of Nest thermostats in a heat pump control pilot and a savings assessment of Nest learning thermostats. It also mentions a 2024-2025 DSM Measure Assessment Final Report.

$$\Delta kWh = \left(HC \left[\frac{1}{HEF_{base}} - \frac{1}{HEF_{ee}}\right] FLH_h + CC \left[\frac{1}{CEF_{base}} AC - \frac{1}{CEF_{ee}}\right] FLH_c\right)$$ 2024-2025 DSM Measure Assessment 241 Efficiency Vermont, Technical Reference...

AI summary The document presents a formula for calculating energy savings from efficiency measures and references the 2024-2025 DSM Measure Assessment. It also cites Efficiency Vermont's Technical Reference Manual (TRM) for Program Year 2023, page 90.

Table 190: Unitary Peak Demand Savings Values for Air-source Heat Pumps Greater Than 65,000 Btu/h Parameter Symbol BER-AR, SBES Reference Heat Pump Rated Heating Capacity at -15°C (estimated temperature during NS peak demand) [kBtu/h] 𝐻𝐶𝑚𝑖...

AI summary The table presents unitary peak demand savings values for air-source heat pumps with capacities greater than 65,000 Btu/h, including parameters such as heating capacity, efficiency factors, and peak coincidence factor assumptions. The section also references dual enthalpy economizer controls.

Table 191: Dual Enthalpy Economizer Control Measure Summary Parameter BER-AR SBES Reference Measure Description and Identification Measure Dual enthalpy economizer controls which must be installed on new equipment that is also eligible for...

AI summary Table 191 outlines the Dual Enthalpy Economizer Control Measure Summary, including parameters such as energy savings adjustment ratios, peak demand savings, and effective useful life. The table provides details on how energy and peak demand savings are calculated based on specification data for each rebated unit.

$$\Delta kWh = SF \times Tonnes \times \frac{OTF}{EER} \times Quantity$$

AI summary The text presents a formula to calculate the change in kilowatt-hours (kWh) based on factors such as the seasonal factor (SF), tonnes, operating time factor (OTF), energy efficiency ratio (EER), and quantity. This formula is likely used in energy efficiency calculations or demand-side management contexts.

(7) HVAC Hotel Occupancy Sensors

AI summary This section outlines regulatory considerations for HVAC hotel occupancy sensors, though no detailed content is provided in the chunk. Key themes likely involve energy efficiency and demand-side management in commercial building systems.

Table 193: HVAC Hotel Occupancy Sensor Measure Summary Parameter BER-AR SBES Reference Measure Description and Identification Measure occupied, rebated after installation Year-round HVAC hotel occupancy sensor which controls electric heati...

AI summary Table 193 summarizes the energy and peak demand savings adjustment ratios and other parameters for HVAC hotel occupancy sensors. It includes details on baseline conditions, installation rates, energy savings, peak demand savings, and useful life. Table 194 provides specific electrical unitary energy savings values for these sensors.

The unitary peak demand savings for HVAC hotel occupancy sensors are provided in [Table](#page-69-2) 195 below, as well as variables from [Table](#page-68-1) 194 above. 2024-2025 DSM Measure Assessment 246 Massachusetts, Technical Referenc...

AI summary The document references tables containing unitary peak demand savings for HVAC hotel occupancy sensors and mentions a 2024-2025 DSM Measure Assessment. It also cites a Technical Reference User Manual (TRM) from Massachusetts from 2012.

Table 195: Unitary Peak Demand Savings Values for HVAC Hotel Occupancy Sensors Parameter Symbol BER-AR, SBES Reference Unitary Peak Demand Savings [kW] - 0.09 Massachusetts TRM, 2012 Installation Rates

AI summary Table 195 presents unitary peak demand savings values for HVAC hotel occupancy sensors, with a value of 0.09 kW based on the Massachusetts TRM, 2012. The table also includes installation rates, though specific details are not provided in the text.

Peak Demand As for the impact on peak demand savings, it is assumed that all hot water tanks in a conditioned or semiconditioned space create interactive effects. Therefore, similar to lighting products, the interactive effects factor for...

AI summary The text discusses the impact of hot water tanks on peak demand savings in electrically heated buildings, assuming an interactive effects factor of -90%. It references regulatory documents and evaluation reports related to energy efficiency and demand-side management (DSM) measures.

Table 197: Interactive Effects Factors for Water Heating Measures Measure Type of Space Heating Interactive Effects Factors for Energy Savings Interactive Effects Factors for Peak Demand Savings Reference Low-flow Showerheads - 0% 0% Assum...

AI summary Table 197 outlines interactive effects factors for water heating measures, including energy and peak demand savings. Measures like pipe insulation and hot water tank wraps show significant savings under specific heating conditions, while others like low-flow showerheads show no savings.

For hot water tank wraps, energy savings are assumed to occur all the time since the tank always exchanges heat with the space around it. Therefore, peak demand savings for this measure correspond to the average demand savings throughout t...

AI summary The document explains how peak demand-to-energy ratios are calculated for hot water tank wraps and other water heating measures. For tank wraps, savings are assumed constant, while for other measures, load shapes from Illinois TRM are used to estimate savings during peak periods, considering differences in peak definitions between Illinois and Nova Scotia.

5.4.4 Water Heating Measures

AI summary The section outlines water heating measures under energy efficiency programs, including rebates, grants, and technical standards. Key entities involve organizations like NS Power and Efficiency Nova Scotia, with topics centered on residential and commercial energy efficiency initiatives.

2024-2025 DSM Measure Assessment 251 Pennsylvania Public Utility Commission, Technical Reference Manual Volume 2: Residential Measures , September 2024, p. 82. 252 Ontario Power Authority (OPA), 2011 Prescriptive Measures and Assumptions V...

AI summary The document references technical resources related to the assessment of demand-side management (DSM) measures for the 2024-2025 period, including a technical reference manual from the Pennsylvania Public Utility Commission and a prescriptive measures document from the Ontario Power Authority.

Table 206: Thermostatic Shower Valve Measure Summary Parameter SBES Reference Measure Description and Identification Measure temperature has been reached, with direct installation Thermostatic shower valves cutting off water after the targ...

AI summary Table 206 provides a summary of the Thermostatic Shower Valve Measure, including parameters such as installation rate, effective useful life, unitary energy savings, and peak demand savings. The table outlines different subcategories based on showerhead flow rates and associated energy and demand savings.

$$GPD (if unknown) = Capacity x \frac{Consumption/cap}{365}$$ Table 213: Electrical Unitary Energy Savings Values for DHW Heat Pump Water Heaters BE Parameter Symbol Non-electric Space Heating or No Space Heating Electric Resistance Space...

AI summary The text provides a formula for calculating GPD, along with a table showing energy savings values for DHW heat pump water heaters in residential and commercial settings, and references the 2024-2025 DSM Measure Assessment.

Table 215: Unitary Peak Demand Savings Values for DHW Heat Pump Water Heaters Parameter Symbol BER-AR, SBES Reference Peak Demand-to-Energy Ratio PDTER If water heating not used during this peak, assume peak demand-to-energy ratio to be 0...

AI summary This table outlines the unitary peak demand savings values for DHW heat pump water heaters, including the peak demand-to-energy ratio and the calculation method for unitary peak demand savings. It also mentions installation rates as a relevant parameter.

5.5.2 Peak Demand Savings Factors For compressed air measures, the peak demand savings factor is assumed to be 77% where it is unknown for the specific project.

AI summary The document specifies that for compressed air measures, a default peak demand savings factor of 77% is assumed when project-specific data is unavailable. This assumption is used to estimate energy savings in demand-side management initiatives.

(1) Compressed Air Leak Repairs

AI summary The document section titled '(1) Compressed Air Leak Repairs' appears to focus on energy efficiency initiatives related to identifying and repairing leaks in compressed air systems. These repairs are likely part of broader demand-side management (DSM) programs aimed at reducing energy waste and improving system efficiency in industrial or commercial settings.

Unitary peak demand savings for compressed air leak repairs are calculated using the equation below and the variables defined and listed in [Table](#page-87-0) 217 above. [] = [] × [/(100 )] ×

AI summary The calculation for unitary peak demand savings from compressed air leak repairs is outlined, using a specific equation and variables defined in Table 217.

Table 219: Cycling Air Dryer Measure Summary Parameter BER-AR, SBES Reference Measure Description and Identification Measure Cycling refrigerated dryers up to 300 cubic feet per minute (CFM) capacity which automatically turn on and off in...

AI summary This table summarizes the Cycling Air Dryer Measure, including its description, baseline, installation rate, effective useful life, and electrical savings parameters. It outlines details on energy and peak demand savings calculations based on specification data for each rebated unit.

The electrical unitary energy savings for cycling air dryers are calculated using the variables defined and listed in the equation[274](#page-88-1) and [Table](#page-89-0) 220 below. $$\Delta kWh = 4 x hp_{comp} x 0.0087 x HOU x (1 - APC)x...

AI summary The document discusses the calculation of electrical unitary energy savings for cycling air dryers using a specific equation and data from a table. It references a technical manual from Efficiency Vermont and mentions a 2024-2025 DSM Measure Assessment.

Table 221: Unitary Peak Demand Savings Values for Cycling Air Dryers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.77 As per Subsection 5.5.2 Hours of Use [hours/year] HOU Actual or 1-shift (8/5) – 2,080 h...

AI summary Table 221 outlines unitary peak demand savings values for cycling air dryers, including parameters like peak coincidence factor, hours of use, and unitary peak demand savings. It provides calculation methods and reference points for determining these values.

∆ = ∆ℎ ⁄ Table 224: Unitary Peak Demand Savings Values for Air-entraining Air Nozzles Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.77 As per Subsection 5.5.2 Hours of Use [hours/year] HOU Actual 1-shift (...

AI summary This section presents a table detailing unitary peak demand savings values for air-entraining air nozzles, including parameters such as peak coincidence factor, hours of use, and unitary peak demand savings. It also mentions installation rates.

The electrical unitary energy savings for no-loss drains are calculated using the variables defined and listed in the equation[278](#page-92-1) and [Table](#page-93-0) 226 below. ∆ℎ = 278 Efficiency Vermont, Technical Reference User Manual...

AI summary The document discusses the calculation of electrical unitary energy savings for no-loss drains using a specific equation and table. It references a Technical Reference User Manual from Efficiency Vermont and mentions a 2024-2025 DSM Measure Assessment.

The unitary peak demand savings for no-loss drains are calculated using the variables defined and listed in the equation and [Table](#page-94-0) 228 below, as well as variables from [Table](#page-93-1) 227 above. ∆ = ∆ℎ ⁄

AI summary The unitary peak demand savings for no-loss drains are calculated using variables defined in equations and tables referenced in the text, including Table 228 and Table 227.

Table 228: Unitary Peak Demand Savings Values for No-loss Drains Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.77 As per Subsection 5.5.2 Hours of Use [hours/year] HOU Actual 1-Shift (8/5) – 2,080 hours 2-...

AI summary Table 228 presents unitary peak demand savings values for no-loss drains, including parameters such as the peak coincidence factor, hours of use, and unitary peak demand savings. The table provides calculation methods and references for these values.

5.6.2 Peak Demand Savings Factors For variable frequency drives measures, the methodology used to determine peak demand savings is detailed in the measure-specific subsections below.

AI summary The methodology for determining peak demand savings for variable frequency drives (VFDs) is outlined in measure-specific subsections below, focusing on how these measures contribute to demand-side management outcomes.

2024-2025 DSM Measure Assessment Final Report

AI summary This document is the 2024-2025 DSM Measure Assessment Final Report, which evaluates demand-side management measures for energy efficiency and conservation. It provides an analysis of programs and initiatives aimed at reducing energy consumption and promoting sustainable energy use.

5.7.2 Peak Demand Savings Factors For pool pump measures, peak demand savings factors are assumed to be nil since most pumps are installed on outdoor pools that do not operate during the winter peak.

AI summary For pool pump measures, peak demand savings factors are assumed to be nil since most pumps are installed on outdoor pools that do not operate during the winter peak.

Table 233: Pool Pump Measure Summary Parameter BER-AR, SBES Reference Measure Description and Identification Measure ENERGY STAR® multi-speed and variable frequency drive (VFD) commercial inground pool pumps, rebated after installation - B...

AI summary This table summarizes the ENERGY STAR® multi-speed and variable frequency drive (VFD) commercial inground pool pump measure, including its baseline, installation rate, useful life, and energy savings parameters. The unitary energy savings are calculated using the ENERGY STAR Pool Pump Calculator.

5.8.2 Peak Demand Savings Factors For solar PV projects, peak demand savings are nil since the solar energy production from those systems coinciding with the peak period is negligible.

AI summary The text states that solar PV projects yield no peak demand savings in Nova Scotia, as their energy production during peak periods is negligible. This highlights a limitation in relying on solar PV for reducing peak electricity demand.

5.9.2 Peak Demand Savings Factor For refrigeration measures, the peak coincidence factor (PCF) is assumed to be 100%, since all equipment is expected to be running continuously, with minor downtime that is addressed in the duty cycle varia...

AI summary The Peak Demand Savings Factor section assumes a 100% peak coincidence factor (PCF) for refrigeration measures due to continuous equipment operation, with minor downtime accounted for via duty cycle variables where applicable.

(1) Cooler Night Covers and Display Case Strip Curtains

AI summary The document section discusses 'Cooler Night Covers and Display Case Strip Curtains' as potential energy efficiency measures, likely within the context of demand-side management programs. The text does not elaborate on specific arguments, entities, or regulatory decisions related to these items.

= ( )⁄( 1000) Table 240: Unitary Peak Demand Savings Values for Cooler Night Covers and Display Strip Curtains Parameter Symbol BER-AR, SBES Reference Cooler Night Covers Display Strip Curtains Peak Coincidence Factor PCF 1 As per Subsecti...

AI summary The table presents unitary peak demand savings values for Cooler Night Covers and Display Strip Curtains, focusing on the Peak Coincidence Factor (PCF) and calculation methods for peak demand savings. The PCF is set at 1, as per Subsection 5.9.2.

$$\Delta kW = kW_{door} x BF x PCF$$

AI summary The formula provided calculates the change in kilowatts (ΔkW) based on the door kilowatts (kW_door), a base factor (BF), and the peak coincidence factor (PCF). This formula is used in energy efficiency calculations and may relate to load management or demand-side management practices.

Table 243: Unitary Peak Demand Savings Values for Zero-energy Doors Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 As per Subsection 5.9.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specification data f...

AI summary Table 243 provides unitary peak demand savings values for zero-energy doors, including the peak coincidence factor (PCF) and calculation methods for demand savings based on specification data for each rebated unit.

Table 246: Unitary Peak Demand Savings Values for Door Heater Controls Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 As per Subsection 5.9.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specification dat...

AI summary Table 246 presents unitary peak demand savings values for door heater controls, including the Peak Coincidence Factor (PCF) and calculation methods based on specification data for rebated units. The table references Subsection 5.9.2 for the PCF value.

$$\Delta kW = \left( \left( kW_{evap} \ x \ n_{fans} \ x \ DC_{evap} \right) - \ kW_{circ} \right) x \left( 1 - DC_{comp} \right) x \ BF \ x \ PCF$$ Table 249: Unitary Peak Demand Savings Values for Cooler Night Covers and Display Strip Cu...

AI summary The document presents a formula for calculating peak demand savings (∆kW) for energy efficiency measures such as Cooler Night Covers and Display Strip Curtains. It includes a table with parameters like the Peak Coincidence Factor (PCF) and unitary peak demand savings, referencing Subsection 5.9.2 of a regulation.

Table 252: Unitary Peak Demand Savings Values for Intelligent Freezer Defrost Controls Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 See Subsection 5.9.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on spec...

AI summary Table 252 presents unitary peak demand savings values for intelligent freezer defrost controls, focusing on the peak coincidence factor (PCF) and the calculation of unitary peak demand savings (∆𝑘𝑊) based on specification data for each rebated unit.

The electrical unitary energy savings for vertical refrigeration open to closed cooler conversion are calculated using the variables defined and listed in the equation[295](#page-115-1) and [Table](#page-116-0) 254 below. $$\Delta kWh = OC...

AI summary The text discusses the calculation of electrical unitary energy savings for converting vertical refrigeration open to closed cooler systems, referencing an equation and a table. It also mentions a 2024-2025 DSM Measure Assessment and cites a paper from the International Refrigeration and Air Conditioning Conference.

Table 255: Unitary Peak Demand Savings Values for Vertical Refrigeration Open-to-closed Cooler Conversion Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 See Subsection 5.9.2 Hours of Use [h/year] HOU Actual Use infor...

AI summary Table 255 presents unitary peak demand savings values for vertical refrigeration open-to-closed cooler conversion, including parameters such as the Peak Coincidence Factor (PCF), Hours of Use (HOU), and Unitary Peak Demand Savings (∆𝑘𝑊). It references Subsection 5.9.2 and the use of information in TS for HOU.

The electrical unitary energy savings for efficient refrigeration compressor are calculated using the variables defined and listed in the equation 297 and Table 257 below. $$\Delta kWh = (CAP_{avg,ee}) x \left[ \left( \frac{1}{EER_{avg,has...

AI summary The document discusses the calculation of electrical unitary energy savings for efficient refrigeration compressors using a specific equation and references a technical manual. It also mentions the 2024-2025 DSM Measure Assessment and includes a reference to a table and image.

2024-2025 DSM Measure Assessment The unitary peak demand savings for efficient refrigerator compressors are calculated using the variables defined and listed in the equation and [Table](#page-119-0) 260 below, as well as variables from [Ta...

AI summary The document discusses the assessment of DSM measures for the 2024-2025 period, focusing on calculating unitary peak demand savings for efficient refrigerator compressors using variables from two tables referenced in the text.

The unitary peak demand savings for refrigeration economizer are calculated using the variables defined and listed in the equation and [Table](#page-121-2) 263 below, as well as variables from [Table](#page-120-0) 262 above.

AI summary The text explains how unitary peak demand savings for refrigeration economizers are calculated using specific variables defined in equations and tables referenced in the document.

The unitary peak demand savings for brushless DC motors are calculated using the variables defined and listed in the equation and [Table](#page-122-4) 266 below, as well as variables from [Table](#page-122-1) 265 above. $$\Delta kW = \left...

AI summary The text explains the calculation of unitary peak demand savings for brushless DC motors using a specific formula that includes variables such as output power, efficiency, and factors like DC, LF, COP, and PCF, as defined in referenced tables.

Table 266: Unitary Peak Demand Savings Values for Brushless DC Motors Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 As per Subsection 5.9.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specification data...

AI summary Table 266 presents unitary peak demand savings values for brushless DC motors, including parameters like the Peak Coincidence Factor (PCF) and Unitary Peak Demand Savings (∆𝑘𝑊). The table references Subsection 5.9.2 and includes a calculation based on specification data for each rebated unit.

(10) Refrigerated Vending Machine Controllers

AI summary This section addresses refrigerated vending machine controllers, likely within the context of energy efficiency regulations or demand-side management initiatives in Nova Scotia.

2024-2025 DSM Measure Assessment Table 268: Electrical Unitary Energy Savings Values for Refrigerated Vending Machine Controllers Parameter Symbol BER-AR, SBES Reference Rated Power of Connected Equipment [kW] kWrated Actual Use informatio...

AI summary The document presents a table and explanation for calculating unitary energy savings values for refrigerated vending machine controllers, including parameters such as rated power, hours of use, percent savings factor, and quantity of vending machines.

$$\Delta kW = kW_{rated} \ x \ SAVE \ x \ QTY \ x \ PCF$$ Table 269: Unitary Peak Demand Savings Values for Refrigerated Vending Machine Controllers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 1 As per Subsection 5....

AI summary This section provides a formula for calculating unitary peak demand savings for refrigerated vending machine controllers, specifically focusing on the Peak Coincidence Factor (PCF) and the calculation method based on specification data for each rebated unit.

5.10.2 Peak Demand Savings Factors For agriculture measures, peak demand savings are determined by multiplying the wattage by a PCF value of 84%, which was established as part of the 2015 evaluation of BER.[308](#page-124-4) 308 Econoler,...

AI summary Peak demand savings for agriculture measures use an 84% Peak Demand Savings Factor (PCF), established in the 2015 evaluation of Business Energy Rebates (BER) by Econoler. This factor is applied to wattage calculations for demand-side management purposes.

Final Report 249 The unitary peak demand savings for dual & natural ventilation are calculated using the variables defined and listed in the equation and [Table](#page-131-0) 279 below, as well as variables from [Table](#page-130-0) 278 ab...

AI summary The document provides a formula for calculating unitary peak demand savings for dual and natural ventilation systems, referencing specific tables for variable definitions.

Table 285: Unitary Peak Demand Savings Values for Agriculture Heat Pads Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 0.84 As per Subsection 5.10.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specificatio...

AI summary Table 285 presents unitary peak demand savings values for agriculture heat pads, including parameters such as the Peak Coincidence Factor and Unitary Peak Demand Savings. The table provides calculation methods and references for these values.

Summary [Table](#page-136-0) 286 presents a summary of the values used to calculate tractor engine block heater timer savings. The detailed methodology follows.

AI summary Table 286 summarizes the values used to calculate tractor engine block heater timer savings, with a detailed methodology provided in the following text.

Table 286: Tractor Engine Block Heater Timer Measure Summary Parameter BER-AR SBES Reference Measure Description and Identification Measure Tractor engine block heater timer that controls an engine block heater of at least 400 W and is CSA...

AI summary This table outlines the parameters for the Tractor Engine Block Heater Timer Measure, including energy savings adjustment ratios, effective useful life, and other technical details relevant to the measure's implementation and evaluation.

Table 288: Unitary Peak Demand Savings Values for Tractor Engine Block Heater Timers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 0.84 As per Subsection 5.10.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on...

AI summary Table 288 outlines unitary peak demand savings values for tractor engine block heater timers, including parameters like the peak coincidence factor and unitary peak demand savings. Installation rates are also mentioned, though not detailed in the table.

Table 291: Unitary Peak Demand Savings Values for Dairy Scroll Compressors Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual As per Subsection 5.10.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specifi...

AI summary Table 291 presents unitary peak demand savings values for Dairy Scroll Compressors, including parameters such as the Peak Coincidence Factor and Unitary Peak Demand Savings. The table includes references to regulatory subsections and calculation methods.

Table 292: Heat Reclaimer Unit Measure Summary Parameter BER-AR SBES Reference Measure Description and Identification Measure Heat reclaimer unit that electrically heats water, rebated after installation - Baseline No heat reclaimer - Gene...

AI summary Table 292 outlines the parameters for a heat reclaimer unit measure, including energy savings adjustment ratios, peak demand savings adjustment ratios, and other technical specifications. It provides details on installation rates, useful life, and calculation methods for energy and peak demand savings.

Table 294: Unitary Peak Demand Savings Values for Heat Reclaimer Units Parameter Symbol BER-AR, SBES Reference Hours of Use [h/year] HOU Actual or 2,920 Use information in TS Peak Coincidence Factor PCF 0.84 As per Subsection 5.10.2 Unitar...

AI summary Table 294 provides unitary peak demand savings values for heat reclaimer units, including parameters such as hours of use, peak coincidence factor, and unitary peak demand savings. The table outlines the calculation methods and references for each parameter.

Table 297: Unitary Peak Demand Savings Values for Milk Pre-coolers Parameter Symbol BER-AR, SBES Reference Hours of Use [h/year] HOU Actual or 2,920 Use information in TS Default: Minnesota 2025 TRM334 Peak Coincidence Factor PCF 0.84 As p...

AI summary Table 297 outlines the unitary peak demand savings values for milk pre-coolers, including parameters such as hours of use, peak coincidence factor, and unitary peak demand savings. The table provides default values and references for calculations.

(10) VFDs for Milk Vacuum Pumps

AI summary The document analyzes the use of Variable Frequency Drives (VFDs) in milk vacuum pumps, focusing on their energy efficiency, cost-benefit analysis, and potential regulatory implications for Nova Scotia's utility sector. It evaluates technical performance and compliance with energy standards.

2024-2025 DSM Measure Assessment The electrical unitary energy savings for VFD for milk vacuum pump are calculated using the variables defined and listed in the equation and [Table](#page-145-0) 299 below. $$\Delta kWh = \left(HP \ x \ 0.7...

AI summary The document discusses the calculation of electrical unitary energy savings for a Variable Frequency Drive (VFD) used in a milk vacuum pump. The calculation uses specific variables and is based on an equation and a referenced table.

The unitary peak demand savings for VFD for milk vacuum pump are calculated using the variables defined and listed in the equation and [Table](#page-145-1) 300 below, as well as variables from [Table](#page-145-0) 299 above. $$\Delta kW =...

AI summary The text discusses the calculation of unitary peak demand savings for a VFD applied to a milk vacuum pump, using an equation and data from two tables. The formula involves variables such as horsepower, motor efficiency, load factor, motor savings factor, and power conversion factor.

Table 300: Unitary Peak Demand Savings Values for Milk Vacuum Pumps Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 0.84 As per Subsection 5.10.2 Unitary Peak Demand Savings [W] ∆𝑘𝑊 Calculation based on specification da...

AI summary Table 300 provides unitary peak demand savings values for Milk Vacuum Pumps, including parameters such as the Peak Coincidence Factor (PCF) and Unitary Peak Demand Savings (∆𝑘𝑊). The table references Subsection 5.10.2 for the Peak Coincidence Factor and outlines the calculation method for demand savings.

Table 303: Unitary Peak Demand Savings Values for VFD Milk Transfer Pumps Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF 0.84 As per Subsection 5.10.2 Hours of Use [h/year] HOU Actual or 2,920 Use information in TS Def...

AI summary Table 303 outlines unitary peak demand savings values for VFD Milk Transfer Pumps, including parameters like Peak Coincidence Factor, Hours of Use, and Unitary Peak Demand Savings. It references specific subsections and documents for calculation methods and default values.

Table 306: Unitary Peak Demand Savings Values for Dishwashers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.68 As per Subsection 5.11.2 Unitary Peak Demand Savings [W] 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑘𝑊 Calculation bas...

AI summary Table 306 presents unitary peak demand savings values for dishwashers, focusing on the Peak Coincidence Factor (PCF) and the calculation of peak demand savings based on specification data for each rebated unit. The table also references Subsection 5.11.2 and outlines installation rates.

Table 312: Unitary Peak Demand Savings Values for Fryers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.68 As per Subsection 5.11.2 Unitary Peak Demand Savings [W] 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑘𝑊 Calculation based on...

AI summary Table 312 presents unitary peak demand savings values for fryers, including the Peak Coincidence Factor (PCF) and calculation methods for peak demand savings. The table references Subsection 5.11.2 and outlines installation rates for energy efficiency programs.

Table 315: Unitary Peak Demand Savings Values for Griddles Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.68 As per Subsection 5.11.2 Unitary Peak Demand Savings [W] 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑘𝑊 Calculation based...

AI summary This table outlines the Unitary Peak Demand Savings Values for Griddles, focusing on parameters like the Peak Coincidence Factor and the calculation of peak demand savings based on specification data for each rebated unit.

The unitary peak demand savings for ice machines are calculated using the variables defined and listed in the equation and [Table](#page-157-1) 321 below, as well as variables from [Table](#page-157-0) 320 above. = ( )⁄( )

AI summary The text discusses the calculation of unitary peak demand savings for ice machines using variables defined in equations and tables referenced in the document.

Table 321: Unitary Peak Demand Savings Values for Ice Machines Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.68 As per Subsection 5.11.2 Unitary Peak Demand Savings [W] 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑘𝑊 Calculation ba...

AI summary Table 321 outlines unitary peak demand savings values for ice machines, focusing on parameters such as the peak coincidence factor and unitary peak demand savings. It references Subsection 5.11.2 and includes installation rates as a consideration.

Table 327: Unitary Peak Demand Savings Values for Steam Cookers Parameter Symbol BER-AR, SBES Reference Peak Coincidence Factor PCF Actual or 0.68 As per Subsection 5.11.2 Unitary Peak Demand Savings [W] 𝑃𝑒𝑎𝑘 𝐷𝑒𝑚𝑎𝑛𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑘𝑊 Calculation b...

AI summary Table 327 presents unitary peak demand savings values for steam cookers, including parameters such as the peak coincidence factor and calculation methods for peak demand savings. The table also includes installation rates, though no specific values are provided in the excerpt.

5.12.4 Commercial Laundry Measures

AI summary The section titled '5.12.4 Commercial Laundry Measures' outlines regulatory considerations for energy efficiency and demand-side management initiatives targeting commercial laundry operations in Nova Scotia.

Table 328: Commercial Washing Machine Measure Summary Parameter BER-AR, SBES Reference Measure Description and Identification Measure ENERGY STAR® rated commercial washing machine used for commercial purposes, rebated after installation •...

AI summary Table 328 outlines the parameters for the Business Energy Rebates (BER) program, focusing on ENERGY STAR® rated commercial washing machines. It details the measure description, baseline, installation rate, useful life, and energy and peak demand savings parameters, with references to specific subsections and reports.

2024-2025 DSM Measure Assessment Final Report 281 The electrical unitary energy savings for commercial washing machine are calculated using the variables defined and listed in the equation and [Table](#page-163-0) 329 below. $$\Delta kWh =...

AI summary The document provides a formula for calculating electrical unitary energy savings for commercial washing machines, using variables such as MEF_b, MEF_e, CAP, LOADS, and QTY, as defined in an equation and referenced table.

The unitary peak demand savings for commercial washing machine are calculated using the variables defined and listed in the equation and [Table](#page-163-2) 330 below, as well as variables from [Table](#page-163-0) 329 above. $$\Delta kW...

AI summary The calculation for unitary peak demand savings for commercial washing machines uses a formula involving variables such as Modified Energy Factors (MEF), Capacity (CAP), Load (LOADS), Quantity (QTY), Runtime, and Peak Demand Savings Factor (PCF).

Table 330: Unitary Peak Demand Savings Values for Commercial Washing Machines Parameter Symbol BER-AR, SBES Reference Length of Average Cycle [h] RUNTIME Actual or 1 Use information in TS Peak Coincidence Factor PCF 0.34 See Subsection 5.1...

AI summary Table 330 provides unitary peak demand savings values for commercial washing machines, including parameters such as runtime, peak coincidence factor, and calculation methods for peak demand savings. The table references specific subsections and technical specifications for determining these values.

Summary [Table](#page-166-0) 334 presents a summary of the values used to calculate electrical demand-controlled kitchen exhaust savings. The detailed methodology follows.

AI summary Table 334 summarizes the values used to calculate electrical demand-controlled kitchen exhaust savings, with a detailed methodology provided afterward.

The electrical unitary energy savings for demand-controlled kitchen exhaust are calculated using the variables defined and listed in the equation and [Table](#page-166-1) 335 below.

AI summary The document discusses the calculation of electrical unitary energy savings for demand-controlled kitchen exhaust, referencing an equation and a table for the variables involved.

ℎ = Table 335: Electrical Unitary Energy Savings Values for Demand Controlled Kitchen Exhaust Parameter Symbol BER-AR R, SBES Reference Energy Savings Factor [kWh/hp] ESVG 4,486 Pennsylvania TRM 2016341 Total Horsepower of Motor Controlled...

AI summary The document presents a table with electrical unitary energy savings values for demand controlled kitchen exhaust, including parameters like Energy Savings Factor, Total Horsepower, and Unitary Energy Savings. It references data from Pennsylvania TRM 2016 and 2016341, and is part of a 2024-2025 DSM Measure Assessment Final Report.

The unitary peak demand savings for demand controlled kitchen exhaust are calculated using the variables defined and listed in the equation and [Table](#page-167-1) 336 below, as well as variables from [Table](#page-166-1) 335 above. =

AI summary The calculation of unitary peak demand savings for demand controlled kitchen exhaust involves specific variables defined in equations and tables referenced within the document.

5.13.2 Peak Demand Savings Factor No standard PCF values have been determined for commercial IT and datacentre measures.

AI summary The document notes that no standard Peak Demand Savings Factor (PCF) values have been established for commercial IT and datacentre measures, indicating a gap in quantifying demand-side management (DSM) savings for these sectors.

$$\Delta kWh = (kWh_{BV} - kWh_{AV})/VH$$ $$kWh_{BV} = \frac{8,760}{1,000} \times RS \times \left(W_{rs, idle} + U_{rs} \left(W_{rs,full load} - W_{rs,idle}\right)\right)$$

AI summary The text contains mathematical formulas related to energy calculations, specifically focusing on kilowatt-hour (kWh) differences and calculations involving various energy-related variables such as RS, W_rs, idle, and U_rs. These formulas are likely used for energy efficiency or demand-side management purposes.

The unitary peak demand savings for server virtualization and decommissioning are calculated using the variables defined and listed in the equation and Table 342 below, as well as variables from Table 341 above. $$\Delta kW = \frac{\Delta...

AI summary The document outlines the calculation method for unitary peak demand savings from server virtualization and decommissioning, referencing a technical reference manual and including a table and equation for the assessment. It also mentions a final report for the 2024-2025 DSM Measure Assessment.

The electrical unitary energy savings for uninterruptible power supply (UPS) are calculated using the variables defined and listed in the equation[345](#page-172-2) and [Table](#page-173-0) 344 below. $$\Delta kWh = 204 x kVA$$ 345 Califor...

AI summary The document discusses the calculation of electrical unitary energy savings for uninterruptible power supply (UPS) using a specific equation and references a technical manual. It also mentions a 2024-2025 DSM Measure Assessment.

6 Effective Useful Life This section outlines the EUL values used to calculate lifetime energy savings. This section also presents EUL values for demand reduction measures; these values are not used to calculate lifetime energy savings sin...

AI summary The section explains that Effective Useful Life (EUL) values are used to calculate lifetime energy savings for energy-saving measures, while demand response measures use EUL to represent the duration of demand reduction. EUL values are crucial for cost-effectiveness ratio calculations to ensure lifetime benefits are accounted for.

Table 346: EUL Values for Non-LED Lighting BNI Measures Measure Name Program Component EUL Value Reference DHW Measures Custom NC 10 Based on the EUL value of heat pump water heaters and several other DHW measures such as faucet aerators,...

AI summary Table 346 outlines the Effective Useful Life (EUL) values for various Non-LED Lighting BNI measures, including DHW, cooling, heating, ventilation, motor equipment, refrigeration, and renewable power generation. The EUL values are derived from references such as NREL and SBW Consulting.

APPENDIX IV Interactive Effects Methodology for Commercial Lighting The interactive effects calculation methodology for commercial lighting was revised as part of the 2024-2025 DSM Measure Assessment update. This appendix details how the c...

AI summary The interactive effects methodology for commercial lighting was revised in the 2024-2025 DSM Measure Assessment update. The appendix explains revised calculation methods for interactive effect factors, applicable only to 2025 savings due to required changes in tracking data and adjustment ratios.

Calculation Approach Pursuant to a literature review of methodologies used by other jurisdictions and of the Uniform Methods Project recommendations, the Evaluator decided to continue applying an engineering calculation approach to estimat...

AI summary The Evaluator opted for an engineering calculation approach to estimate interactive effects, updating methods based on recent data and Uniform Methods Project recommendations. Equations for energy and peak demand savings interactive effects are outlined.

Where: - › %LightingIndoor : The percentage of fixtures and lamps that are installed in indoor conditioned spaces as only those result in interactive effects. - › %LightingHeatConditioned : The percentage of heat generated by lighting and...

AI summary The text defines technical variables used to calculate energy interactive effects factors for heating and cooling systems, including lighting impact fractions, building efficiency factors, and system COP values. It provides an equation for peak demand savings interactive effects, focusing on winter peak hours and excluding cooling system operations during these periods.

Table 360: Lighting Operation Schedule per Building Archetype Building Archetype 8 A.M to 4 P.M. 8 A.M. to 6 P.M. 24 hours Agriculture X Banking / Financial X Education X Entertainment / Public Assembly X Healthcare X Hospitality X Manufac...

AI summary Table 360 outlines the typical lighting operation schedules per building archetype, indicating when lighting is expected to be on during the day and throughout the 24-hour period. These schedules are used to calculate interactive effects factors for lighting operations across different building types.

Table 365: Tracking Sheet Fields Required for Interactive Effect Calculations Field Used for Variables Available in SBES? Available in BER-AR? Indoor/Outdoor %LightingIndoor installed almost all indoors or all outdoors. Not specifically. I...

AI summary Table 365 outlines tracking sheet fields required for interactive effect calculations, highlighting gaps in data availability between SBES and BER-AR. E1 plans to add tracking fields in BER-AR in 2025 to ensure required information is available. The Evaluator will classify measure types as recessed or non-recessed using new data and site visits. Assumptions are made about heat generation from high bay fixtures.

INTRODUCTION EfficiencyOne (E1), an independent, non-profit organization, is responsible for helping Nova Scotians improve the energy efficiency of their homes and workplaces by designing, marketing, and delivering demand-side management (...

AI summary EfficiencyOne (E1) manages Nova Scotia's demand-side management (DSM) programs, evaluated by Econoler to align net-to-gross ratio (NTGR) algorithms and free-ridership questionnaires with best practices. The review focuses on self-report methods for DSM programs, excluding non-participant spillover effects, and updates algorithms for EPI and BER-Instant Rebates.

0% score: › Would not have taken any action without the program 394 Massachusetts in 2022, New Jersey (new to DSM) in 2023, and Efficiency Maine's most recent C&I evaluation in 2023. 2024-2025 DSM Measure Assessment Final Report 327 393 Ma...

AI summary The text references DSM evaluations in Massachusetts, New Jersey, and Efficiency Maine, noting methods like cross-influence scores and free-ridership measurements. It highlights regional approaches to assessing program effectiveness and potential biases in evaluation metrics.

Table 367: Participant Spillover Types Like Spillover Program-induced actions taken outside of the program that are similar to the actions taken as part of the program. (i.e. if a participant in a program that offers rebates on a menu of m...

AI summary The text discusses 'like' and 'unlike' spillover effects in energy efficiency programs, explaining that like spillover occurs when participants take similar actions outside the program, while unlike spillover involves different actions. Like spillover is more commonly captured in studies, whereas unlike spillover is harder to measure through surveys and often requires educational components in program design.

are typically used for prescriptive and semi-prescriptive measures and for custom measures; technical questions are asked to quantify savings as accurately as possible.

AI summary The text discusses the use of combination methods in multi-measure programs, emphasizing prescriptive, semi-prescriptive, and custom measures. Technical questions are employed to quantify energy savings accurately, ensuring precise evaluation of program effectiveness.

Table 369: Summary of E1's Current NTGR Approach Program Component Measures for Which FR Is Established Year of Last FR Update Current Spillover Methodology Year of Last Spillover Update Proposed Changes ARet FR determined for all measures...

AI summary This table outlines EfficiencyOne's (E1) current approach to Net-to-Gross Ratio (NTGR) for various program components, including the establishment of Free-Rider (FR) rates, spillover methodologies, and proposed changes. The table includes details for ARet, IS, and HEA, with specific FR rates, spillover methodologies, and planned updates for 2025.

Final Report 341 Current A lgorithm Adjuste d Algorithm Quantity Score (PA4) E6 Quantity Score (QS) E6 Inconsistency Test IF E1=100% AND IF E3<70% OR E4<70% E1=EMPTY Inconsistency Test IF E1=100% AND IF E3<75% OR E4<75% E1= EMPTY Inconsist...

AI summary The text presents a comparison between current and adjusted algorithms for calculating quantity scores and inconsistency tests, including changes in thresholds and handling of empty values. It also includes a question about the influence of free installation of energy-efficient products on customer decisions.

Table 373: Instant Rebates Distributor Influence Level Algorithm Current Algorithm Adjusted Algorithm sustainability e. Your company/organization's interest in increasing your sales and profits 98/99) Don't know/Refused EMPTY Influence Sco...

AI summary Table 373 outlines the 'Instant Rebates Distributor Influence Level Algorithm,' comparing the current and adjusted algorithms. The current algorithm calculates the Influence Score (IS1) as B3 multiplied by 10%, while the adjusted algorithm uses B3 directly. The table also includes a sustainability-related question about a company's interest in increasing sales and profits.

This report, conducted by H. Gil Peach & Associates LLC, for the Nova Scotia Energy Board, verifies electricity energy savings and demand reduction for 2024. It reviews measurements, models, and estimates provided by Econoler, the Independ...

AI summary This report, conducted by H. Gil Peach & Associates LLC for the Nova Scotia Energy Board, verifies energy savings and demand reduction estimates for Efficiency Nova Scotia's DSM programs. It reviews Econoler's evaluations and provides recommendations for adjustments if necessary.

II. How Savings Verification fits in the Policy, Planning, Program Cycle It can be useful in approaching evaluation to review how the Policy, Planning and Program cycle works and where evaluation and savings verification fit.[2](#page-7-3)...

AI summary The document outlines the Policy, Planning, and Program cycle for Demand Side Management (DSM), emphasizing Savings Verification's role. Efficiency Nova Scotia, managed by E1 since 2010, leads DSM efforts, with Econoler conducting independent evaluations. The cycle includes evaluation, savings verification, and iterative improvements, such as the new Residential Behavior Program.

ate resiliency will eventually be authorized as explicit goals for energy savings and demand reduction projects. At that point, policy for the evaluation area will be adjusted to fit integrated goals. What can be said in the absence of sys...

AI summary The text discusses energy sufficiency principles promoted by the European Council for an Energy Efficient Economy (ECEEE), emphasizing resource limits and social equity. It notes that Nova Scotia's DSM program currently evaluates resource acquisition and energy savings but lacks explicit policy to integrate climate change goals. A 'duty of anticipation' from UNESCO is referenced as a guiding principle.

V. Summary of Evaluated Savings Results Evaluation of demand savings, first year energy savings, and lifetime net energy savings at the generator as reported by the Evaluator are summarized in this section.

AI summary This section summarizes evaluated savings results, including demand savings, first-year energy savings, and lifetime net energy savings at the generator, as reported by the Evaluator.

1. Evaluated Net Demand Reduction at the Generator In 2024, Efficiency Nova Scotia operated two residential programs (Residential Efficient Product Rebates and Existing Residential) with nine components; three BNI (business, non-profit and...

AI summary In 2024, Efficiency Nova Scotia operated several residential and BNI programs aimed at reducing energy demand, including rebate programs and demand response initiatives, with multiple components across each category.

Table 1: Net Demand Reduction at the Generator. Residential Demand Reduction (MW) Appliance Retirement 0.337 Rebate Programs Instant Savings 2.428 2.765 Affordable Multifamily Housing 0.570 Affordable Single-Family Housing 2.089 Existing R...

AI summary Table 1 presents the net demand reduction at the generator by program type and category, showing the contribution of residential and business programs to demand-side management (DSM) in 2024. The overall DSM portfolio total is 30.689 MW, with significant contributions from rebate programs, appliance retirement, and home energy assessments.

Table 2: First Year and Lifetime Net Energy Savings at the Generator. 2024 Net Evaluated Energy Savings (at Generator) Program Residential Annual Evaluated Net Savings (GWh) Lifetime E ifetime Evaluated Net Savings (GWh) Residential Effici...

AI summary Table 2 presents the first year and lifetime net energy savings at the generator for various energy efficiency programs in Nova Scotia. The table includes data for residential and non-residential programs, highlighting energy savings from initiatives such as appliance retirement, instant savings, home energy assessments, and business energy rebates.

3. Sector Contributions In Figure 4, BNI contributes net peak demand savings at the generator of about 45%; about 55% is contributed from the Residential sector. 19 The whole numbers shown on the bars in [Figure 3](#page-17-1) can be consi...

AI summary The document highlights sector contributions to demand reduction and energy savings, showing BNI contributing 45-55% of net peak demand savings and energy savings across short-term and lifetime evaluations, with the Residential sector accounting for the remaining share.

VI. Evaluation Effort for 2024 Programs As shown in [Table 3,](#page-20-0) the Econoler impact evaluation work was substantial, including sixteen impact evaluations. There are nine residential evaluations, of which six are comprehensive an...

AI summary The document outlines the evaluation effort for 2024 programs, detailing sixteen impact evaluations conducted by Econoler, including comprehensive and condensed evaluations for residential, BNI, and demand response programs. Comprehensive evaluations include baseline definitions, savings calculations, and net-to-gross ratios, while condensed evaluations rely on prior comprehensive evaluations. The evaluation plan is outlined in Table 3.

Table 3: Planned Evaluations for 2024 Programs. 2024 Portfolio Evaluation Plan D Impact Ev Impact Evaluation Market Program Component Comprehensive Condensed Evaluation Evaluation Residential Appliance Retirement Х Instant Savings Х Afford...

AI summary Table 3 outlines the planned evaluations for 2024 programs, including the Appliance Retirement Program, Instant Savings, and various residential and business energy efficiency initiatives. The evaluations are supported by Econoler and include references to reports on demand-side management programs.

VII. Savings Verification Approach The savings verification review was conducted as follows: - We focus on the "installed" annual energy savings and demand reductions. These are the annualized value of savings and demand reductions from th...

AI summary The savings verification approach focuses on annualized energy savings from installed measures, reviews methodological approaches for program analyses, and conducted 93 site visits for the 2024 program year. Mathematical calculations were not verified, but presentation of methods and interactions like free-ridership were assessed.

IX. General Recommendations SVR24-G-1. The Savings Verification study recommends acceptance of the 2024 evaluation estimates for energy savings and demand reduction except for four programs . These are the Residential Behavior program (6.2...

AI summary The Savings Verification study recommends accepting 2024 energy savings estimates for most programs but excludes four due to evaluation issues. The BNI Custom Incentive Program's compressed air audit lacks independent evaluation per protocols. Residential Behavior and Demand Response programs show statistically significant but impractically small savings, requiring reevaluation. Evaluators must flag low-impact programs and ensure transparency in statistical testing.

X. Individual Program Component Review There are nine residential programs, four Business, Non-Profit, Institutional programs and two Demand Response programs.

AI summary The document outlines the structure of energy efficiency programs in Nova Scotia, detailing nine residential programs, four Business, Non-Profit, and Institutional (BNI) programs, and two Demand Response programs under review.

a comprehensive evaluation. The main evaluation objectives were: - Calculate gross and net electrical first year and lifetime energy saving and peak demand savings. - Calculate avoided GHG emissions. The Evaluator's Green Heat evaluation a...

AI summary The Green Heat program evaluation found net energy savings of 0.872 GWh and 1.529 MW peak demand reduction in 2024, below targets. Participation dropped 21% due to CGH program closure. Evaluation methods adjusted savings calculations, lowering realization rates. The Evaluator recommends removing low-impact measures like wood pellet fireplace inserts.

Efficient Product Installation (EPI) The Efficient Product Installation program (EPI) provides free direct installation of energy-efficient products to homeowners and renters, provided through contractors. In 2024 the Evaluator conducted a...

AI summary The Efficient Product Installation (EPI) program offers free direct installation of energy-efficient products, including smart thermostats and air sealing measures. In 2024, a market evaluation identified new opportunities for electrician-installed measures as E1 phases out lighting programs. The program expanded to include eight new electrician-installed measures and conducted jurisdictional scans in nine regions to identify best practices.

EPI's two funding sources are: - 1. Electricity ratepayers to fund upgrades to reduce electricity consumption. - 2. Government of Nova Scotia and the federal Low Carbon Economy Fund funds upgrades that reduce the use of other fuels. The Ev...

AI summary The Efficient Product Installation (EPI) program's 2024 evaluation highlights a 2.4% increase in participation (9,993 vs. 9,763 in 2023) and 150,722 efficient products installed, though energy savings decreased by 6.3% per participant. LED lamps dominated installations (74%), but smart thermostat adoption dropped due to dissatisfaction. The Evaluator recommends improving installer education and customer support to address these issues.

The Evaluator: - Developed participant survey. - Conducted interviews with program staff, heat pump contractors, and delivery agents (DAs) To determine the gross and net electrical energy and peak demand savings and avoided annual GHG emis...

AI summary The evaluator conducted surveys and interviews to assess the Affordable Single-Family Homes (ASFH) program, finding high satisfaction but issues with wait times and information. The program exceeded energy savings and peak demand reduction targets, though adjustments reduced reported savings by 21% and 3%. Recommendations include improving delivery processes and communication.

I. Residential Behavioral Program (Efficiency Insights) The Residential Behaviour program, "Efficiency Insights", is new for Program Year 2024, and follows a similar previous residential behaviour program, administered by Efficiency Nova S...

AI summary The Residential Behaviour program, 'Efficiency Insights', launched in 2024, provides Home Energy Reports to participants, promoting energy-saving habits and participation in Efficiency Nova Scotia's programs. It is a standalone behavioral program, distinct from other DSM Administrator programs, focusing on behavioral change rather than physical measures.

Figure 11: Random Assignment from Usage Groups to Treatment and Control. Customers were partitioned into three energy use subgroups based on energy consumption [(Table 4)](#page-46-1).[33](#page-46-2) Then each energy use group was split i...

AI summary Customers were divided into three energy use subgroups (High, Medium, Low) based on annual kWh consumption and then randomly assigned into Treatment and Control groups. The energy use ranges were set to ensure uniformity and manage variability, with specific thresholds chosen to exclude outliers and improve homogeneity for analysis.

Table 5: Attrition from March to May 2024. Attrition (March 2024 - May 2024) Energy Use Treatment Group Subgroup March 2024 May 2024 Attrition n % n % n % High 101,273 39% 97,368 41% 3,905 4% Medium 93,233 36% 88,482 37% 4,751 5% Low 66,70...

AI summary Table 5 presents attrition rates from March to May 2024 for both the Treatment Group and Control Group in an energy use study. The data shows attrition percentages across high, medium, and low energy use subgroups, with the Low subgroup experiencing the highest attrition rate in both groups.

Treatment Group Attrition n % n % n % 97,368 41% 92,268 41% 5,100 5.2% 88,482 37% 83,903 37% 4,579 5.2% 53,459 22% 49,699 22% 3,760 7.0% 239,309 100% 225,870 100% 13,439 5.6% Control Group % n n % n % 17,228 34% 16,284 35% 944 5.5% 9,927 2...

AI summary The text presents data on attrition rates for treatment and control groups over time, with percentages and counts provided for different periods. The data is sourced from specific tables in the Econoler Residential Behaviour Evaluation report, highlighting changes in household participation and attrition rates.

The reduction of group sizes over time occurs due to several factors. First, there are account closures, for example, due to death of the last person in an elderly household. Second there are ordinary move-outs. According to the evaluator,...

AI summary The text discusses factors leading to the reduction of group sizes in a program, such as account closures and move-outs, and the discontinuation of Efficiency Insights reports for inactive accounts. It also mentions the exclusion of households with solar DSM resources from the program to avoid double-counting energy savings, while other DSM technologies are still included. The text also references the need for an overall comparison of treatment and control groups in the 2024 Evaluation report.

emonstrates the equivalence of the treatment and control groups on these variables for each usage group. For completion, there should be an overall comparison of Treatment vs. Control.[36](#page-49-1) Effective Useful Life. The evaluator p...

AI summary The text discusses the Effective Useful Life (EUL) for the Efficiency Insights program, noting that it is counted as one year due to the absence of physical measures and the focus on behavioral energy savings. It highlights that behavioral savings are generally assumed not to persist beyond the first year according to the Uniform Methods Project.

t there are many vendors and trying to acquire this information would be difficult. 36 Econoler Residential Behaviour Report, Existing Residential, Pp. 190-191. 37 Uniform Methods Project, Residential Behavior Protocol , August 2014 [(http...

AI summary The text discusses the challenge of acquiring vendor information and references studies on the measure life of energy efficiency programs. It notes that while some studies suggest longer measure life, a one-year value is considered reasonable. It also addresses free riders in randomized assignments and highlights that the Econoler evaluation of the Efficiency Insights program fully meets the requirement for independent third-party evaluation.

a, AH, Dawson, J., Gadbury, G, Mestre, LM, Pavela, G, Mehta, T, Vorlad, CJ, Xun, P. and Allison, DB, "Randomization Can Do Many Things – But It Cannot "Fail." Pp. 20-23, Significance , February 2022. 39 Econoler fully meets the requirement...

AI summary The text discusses data preparation and evaluation protocols for residential behavior-based energy efficiency programs, referencing standards from the State and Local Energy Action Network and the Uniform Methods Project. It highlights the cleaning of data sets by removing outliers, duplicates, and inactive accounts as standard practice.

by removing outliers, removing any duplicate data, and by removing customer accounts that became inactive due to moving or the customer closing the account. These steps in data cleaning are standard. Opt-Out Accounts. Opted-out accounts (a...

AI summary The residential behavior program aims to encourage participation in energy efficiency programs and influence customer behavior to reduce energy use. While there is a very small positive effect in some analyses, the overall effectiveness of the program is nearly zero, as measured by the Evaluator's analysis of nine similar tests.

participation in the measure-based programs[.42](#page-52-1) Out of nine similar analyses of possible effect, the Evaluator only claims a (very tiny) effect for three of the nine analyses (one-third). The three claims include for Green Hea...

AI summary The evaluation of measure-based programs shows minimal effectiveness, with only three out of nine analyses claiming a very small effect. Green Heat and Efficient Products Installation programs show effects of 0.1% and 0.4%, respectively, while the Home Energy Assessment program shows no effect. The analysis highlights concerns about selection bias and the importance of including opt-out subjects in evaluations.

Table 7: Evaluation Claimed Influence on Participation in Other Programs. Measure-Based Program Encouragement Results (Difference of Means) Subgroup Treatment Control (Size of) Difference (Is There a) Claimed Effect High Energy Use HEA 1.6...

AI summary Table 7 evaluates the influence of participation in energy efficiency programs on other programs. Results show no significant claimed effect for HEA and EPI in high and medium energy use subgroups, while Green Heat shows a claimed effect in high and medium energy use subgroups. The table references statistical methodologies and discussions on significance testing.

47 Initial Calculations of Energy Savings. An initial estimate of energy savings is developed by the Evaluator using a standard difference-in-difference approach. Savings are calculated in two ways, using average daily consumption and usin...

AI summary The text describes the initial calculation of energy savings using a difference-in-difference approach and the adjustment for participation in other DSM programs. It includes equations for cumulative savings and net energy savings, and references a table with final savings results.

Table 8: Evaluated Net Energy Savings. Evaluated Net Energy Savings Energy Use Subgroup Cumulative Energy Savings (GWh) Savings Deductions for Other Programs Net Energy Savings (GWh) High 3.051 0.354 2.696 Medium 2.837 0.044 2.793 Low 0.78...

AI summary Table 8 presents evaluated net energy savings across different energy use subgroups, showing cumulative savings, deductions for other programs, and net savings in gigawatt-hours. The total net energy savings across all subgroups is 6.270 GWh.

1. Findings (Observations Regarding the Evaluation) This evaluation is well constructed at a technical level, is well executed, and fully meets the requirements of the two most relevant evaluation protocols (Universal Methods Project and S...

AI summary The evaluation is technically sound but protocols fail to address challenges in residential behavior studies, such as large sample sizes and small effect sizes. Current protocols do not align with updated statistical practices emphasizing practical effect sizes over statistical significance for business decisions.

2. Recommendations SVR2024-Behaviour-5. Overall, we recommend that the program be continued, but not as a direct energy savings program. Rather, it should be redesigned and evaluated as a marketing and promotional program designed to (1) s...

AI summary The recommendations emphasize redesigning the program as a marketing initiative to boost recruitment for measure-based programs and raise energy efficiency awareness. Current evaluations lack data on effective household behaviors for real energy savings, necessitating improved measurement of participation and impact.

3. Residential Behaviour Program - Summary The table below summarized our review of the behaviour program evaluation. Green indicates good practice, and red indicates evaluation issues.

AI summary The document provides a summary of the evaluation of the Residential Behaviour Program, highlighting areas of good practice and evaluation issues using a color-coded system (green for good practice, red for issues).

Table 9: 2024 Residential Behavior Program - Summary. 2024 Residential Behavior Program - Summary Program Setup The basic program design "Separate Sample Pretest-Postest" or "Difference in Differences" design is sound. The choice of an opt...

AI summary The 2024 Residential Behavior Program uses a 'Separate Sample Pretest-Postest' or 'Difference in Differences' design. Random assignment to Treatment and Control groups was not initially done; instead, households were categorized by energy use (high, medium, low) before assignment. Subgroup analysis was conducted, though overall comparisons were not performed. Cutoffs for subgroups were provided upon request.

ogram found that enhanced engineering review of tracked measure savings produced good results, including high participant satisfaction a decreasing free ridership. [58](#page-67-0) In this regard, the 58 Free ridership for BNI New Construc...

AI summary The evaluation of the BNI Custom Incentives Program highlights its effectiveness in market transformation, particularly for multi-unit housing construction. It recommends increased education, outreach, and alignment with NECB 2020 building codes, as well as program logic model updates and enhanced partnerships with builders.

L. BNI Strategic Energy Management (SEM) Strategic Energy Management (SEM) is an approach for integrating energy management into business practice – so that a focus on continually advancing energyefficiency becomes an integral aspect of wo...

AI summary Strategic Energy Management (SEM) integrates energy efficiency into business practices, expanding from manufacturing to sectors like healthcare and education. The 2024 evaluation showed a 32.4% increase in tracked savings, driven by two major participants, with 53% from compressed air leak repairs. SEM's Net to Gross Ratio (NTGR) was set to 1, indicating no free ridership. Program targets for energy savings and peak demand were exceeded by 6% and 14%, respectively.

M. BNI Small Business Energy Solutions Program (SBES) Small Business Energy Solutions (SBES). SBES is available to businesses that use less than 350,000 kWh annually. There are two paths within the program, the audit path, and the do-it-yo...

AI summary The BNI Small Business Energy Solutions (SBES) program offers two paths (audit and DIY) and a CDI pilot for businesses under 350,000 kWh annual usage. 2024 saw 493 projects, 10.854 GWh energy savings, and 46% growth from 2023. Evaluation methods included audits, spillover surveys, and desk reviews, though non-participant results lacked statistical significance, suggesting a need for larger samples.

N. Demand Response (DR) There are two demand response programs, Residential Demand Response and Business-Nonprofit-Institutional (BNI) Demand Response. Demand response concerns capacity (Watts, kW, MW, GW) rather than energy (kWh, GWh). Th...

AI summary The document outlines Nova Scotia's Demand Response (DR) programs, distinguishing between Residential and BNI (Business-Nonprofit-Institutional) DR. DR focuses on capacity reduction during peak periods, with 353 residential and 76 BNI participants in 2024. The Evaluator defines DR as load reduction events triggered by Nova Scotia Power to manage capacity, not energy consumption.

2. BNI DR For BNI, the Evaluator first reviewed Efficiency Nova Scotia BNI tracking sheets to ensure consistency, resulting in a small correction (magnitude 2%-3%) to Efficiency Nova Scotia tracking values. By agreement between Efficiency...

AI summary The evaluation of BNI DR focuses on capacity calculation methods, including event timing (Dec-Feb, excluding weekends), customer classification for morning/evening events, and the use of whole-house AMI data. Efficiency Nova Scotia and Nova Scotia Power agreed on excluding non-participating customers from capacity calculations. Evaluation results show 8.034 MW for BNI DR and 0.057 MW for residential DR, with methodologies deemed logical and complete.

Recommendations SVR2024-Demand Response – 13 . In the next evaluation, include an analysis of the relative importance or lack of importance to the possible capacity shortfall problem to Nova Scotia Power, the roles of the load research sho...

AI summary The text recommends evaluating Demand Response (DR) programs to assess their value, noting minimal kW reductions and questioning their impact. It requests analysis of capacity shortfall relevance, roles of NSP and Efficiency Nova Scotia, and a discussion on whole-home vs. device-level approaches in residential DR. Evaluators are urged to justify the business case for these programs.

A. General Recommendations There are four general recommendations . SVR24-G-1. The Savings Verification study recommends acceptance of the 2024 evaluation estimates for energy savings and demand reduction except for four programs . These a...

AI summary The text outlines four general recommendations for evaluating energy savings programs. It highlights issues with the Residential Behavior and Demand Response programs, noting their minimal practical impact despite statistical significance. It also criticizes the lack of independent evaluation for compressed air projects under the BNI Custom Incentive Program and emphasizes the need for transparency in evaluation protocols and statistical reporting.

B. Program Specific Recommendations There are recommendations for only five of the program evaluations, Residential Behavior, BNI Efficient Product Rebates, the compressed air leak detection part of BNI Custom Incentives, and the two Deman...

AI summary The document highlights recommendations for five program evaluations: Residential Behavior, BNI Efficient Product Rebates, BNI Custom Incentives (compressed air leak detection), and two Demand Programs (Residential and BNI). Other programs lack evaluation issues, indicating focused attention on these specific initiatives.

1. Residential Behaviour Program SVR2024-Behaviour-5. Overall, we recommend that the program be continued, but not as a direct energy savings program. Rather, it should be redesigned and evaluated as a marketing and promotional program des...

AI summary The Residential Behaviour Program should be continued but restructured as a marketing initiative to support recruitment for measure-based programs and raise awareness. Current evaluations lack data on effective behaviors leading to real energy savings, necessitating targeted analysis of high-saving households to identify practical strategies.

4. BNI Demand Reduction Programs SVR2024-Demand Response – 13 . In the next evaluation, include an analysis of the relative importance or lack of importance to the possible capacity shortfall problem to Nova Scotia Power, the roles of the...

AI summary The evaluation of BNI Demand Response programs is criticized for failing to demonstrate value due to minimal kW demand reduction. The text requests analysis of program impacts on Nova Scotia Power's capacity shortfall, roles of NSP and Efficiency Nova Scotia, and cost/benefit details. It also demands justification of the business case for these programs and a comparison of whole-home vs. device-level residential approaches.

iewDoc.aspx?DocRefId=%7B7C7408AE-835B-4EEF-BBB2-5650390C75F5%7D)[4EEF-BBB2-5650390C75F5%7D](https://documents.dps.ny.gov/public/Common/ViewDoc.aspx?DocRefId=%7B7C7408AE-835B-4EEF-BBB2-5650390C75F5%7D) Gellings, Clark W. & John H. Chamberli...

AI summary The text lists references to academic works, reports, and technical documents on demand-side management, energy efficiency, and building analytics. Key sources include books by Gellings and Chamberlin, studies from Lawrence Berkeley National Laboratory, and protocols from the National Renewable Energy Laboratory (NREL).

XIII. Appendix 1: Questions to ask in DSM Evaluations Overall, the evaluation is excellent. However, there are some areas that could be worked on. Here is an overall summary.

AI summary The evaluation of Demand Side Management (DSM) is described as excellent, but with areas for improvement. The text provides an overview of the evaluation and highlights areas that require further attention.

230 Brownlow Avenue, Suite 300 Dartmouth, Nova Scotia B3B 0G5 efficiencyone.com T +1 902 470 3500 F +1 902 470 3599 March 31, 2025 Attn: Crystal Henwood Regulatory Affairs Officer/Clerk Nova Scotia Utility and Review Board 3rd Floor, Summi...

AI summary EfficiencyOne submits its 2024 DSM Annual Progress Report and Evaluation Reports to the Nova Scotia Utility and Review Board, requesting an exemption from paper copy requirements. The filing includes regulatory documents and a request for digital submission only.