E-12024 DSM Annual Progress Report
14 passages
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.
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.
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 Figures 2 5 show E1's results in 2023 and 2024 and the forecasted savings for 2025 in comparison to the NSUARB approved three- - 2 year Performance Targets. Date Filed: March 31, 2025 Page 15 of 46
AI summary The text references figures showing E1's energy efficiency results in 2023 and 2024, along with forecasted savings for 2025, compared to the NSUARB approved three-year Performance Targets.
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.
CUSTOM INCENTIVES (2024) executed more quickly than conventional projects. This alternative, customer-driven approach resulted in two industrial customers achieving significant savings as a result of compressed air leak audits. - The Evalu...
AI summary The 2024 Custom Incentives program highlights successes in energy savings, including a 10% reduction in Retrofit service free-ridership, high New Construction participation, and tripled Building Optimization savings. Challenges include cost barriers for modeling in new construction and delays from compliance requirements. Pay-for-Performance projects initiated in 2024 will yield savings starting in 2025. Marketing efforts targeted BNI and commercial sectors.
5 Table 10: 2024 Direct Installation DIRECT INSTALLATION (2024) Direct Installation Energy Savings (GWh) Demand Savings (MW) Expenditure ($ million) 2024 Results 10.9 2.2 7.3 2024 MCA Target 10.6 2.6 6.5 • Direct Installation met its mid-c...
AI summary Table 10 shows Direct Installation met its 2024 mid-course adjusted energy savings target (10.9 GWh vs. 10.6 GWh) but fell short of demand savings (2.2 MW vs. 2.6 MW). Increased unitary incentives in Q2 drove higher-than-target expenditure ($7.3M vs. $6.5M).
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.
5.5.2 Performance Indicator The NSUARB also established a Performance Indicator of incidental cumulative annual energy savings of 23.6 GWh applicable to low-income and underserved communities from non-targeted programs. [24](#page-42-1) In...
AI summary The NSUARB set a performance indicator for 23.6 GWh annual energy savings from non-targeted programs targeting low-income and underserved communities. E1 updated its methodology in 2023 and met its 2024 mid-course target, achieving 26.4 GWh (112% of the three-year goal). Results reflect revised low-income estimation assumptions, with 2024 non-targeted program savings at 16.1 GWh.
2 5.7 Additional 2024 Performance Indicators - 3 The NSUARB approved additional Performance Indicators as identified in the Supply - Agreement.26 4 In 2024, results of E1's additional Performance Indicators are as follows: - 5 Total lifeti...
AI summary The NSUARB approved additional performance indicators in the Supply Agreement. In 2024, E1 achieved a Customer Satisfaction Index of 89.9 and total lifetime ratepayer benefits of $243 million from energy and demand savings. Awareness of Efficiency Nova Scotia was approximately 85 percent, consistent with 2023 results.
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.
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.
E-22024 DSM Programs Evaluation Reports
67 passages
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.
2.1.1 Tracking Sheet Audits The final tracking sheets submitted to the Evaluator by E1 contained both data for all completed projects for 2024 and the tracked results required to calculate final savings. The final tracking sheets were audi...
AI summary The Evaluator audited final tracking sheets submitted by E1 to assess data consistency and completeness for 2024 program projects. Corrected tracked values were determined to ensure accurate savings calculations, with evaluated savings based on audited results.
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.
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.
Calculation of the Standard Error Since the overall adjustment ratio is based on a stratified weighted average, the Evaluator also calculated a stratified weighted standard error of the adjustment ratio instead of a simple standard error....
AI summary The Evaluator calculated a stratified weighted standard error for adjustment ratios using a formula from the Uniform Methods Project (UMP) Chapter 11. The calculation involved evaluating savings across strata, resulting in a standard error of 369,784.
Where: - > H is the number of strata (4) and h represents each stratum. - $N_h$ is the number of projects in the population for a stratum. - $n_h$ is the number of projects in the sample for a stratum. & lt;sup>1 Khawaja, M.S., Rushton, J....
AI summary The text describes statistical methods for estimating energy savings in efficiency programs, including strata-based sampling, weighted average calculations, and standard error estimation. It references equations for stratum weight estimation and weighted standard error, with a focus on measurement and verification methodologies.
Calculations Using Evaluation Results Building on all the above methods and collected data, the Evaluator calculated first-year and lifetime energy and peak demand savings as per the calculation methodology presented in Section [7](#page-1...
AI summary The Evaluator calculated first-year and lifetime energy and peak demand savings using a methodology outlined in Section 7, building on prior data collection and analysis methods.
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 document defines key terms related to demand response (DR) capacity and baseline measurements used in evaluating energy efficiency programs. It mentions a follow-up on past evaluation report recommendations, indicating a focus on program evaluation and performance monitoring.
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.
Desk Reviews with Participant Interviews In October and November 2024, the Evaluator reviewed the documentation of 10 prescriptive projects, and LMMW Group Ltd performed simulation model reviews for five comprehensive projects. For compreh...
AI summary In October and November 2024, the Evaluator and LMMW Group Ltd. conducted desk reviews and simulation model reviews for 10 prescriptive and 5 comprehensive projects. The reviews aimed to validate the consistency of savings tracked by E1 with project data and simulation outputs, following a protocol detailed in Appendix V.
Note on Margins 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 condu...
AI summary The Evaluator aimed for a 10% margin of error at 90% confidence in quantitative evaluations, focusing on random sampling errors. This applies to the 2024 AMH evaluation and DSM MA document, which outline parameters for calculating energy savings and effective useful life values. Nonsampling errors are excluded from confidence level calculations.
Tracking Sheet Audit Prior to performing the savings calculation review, the Evaluator performed an audit of the final 2024 tracking sheet to ensure it was complete and the entered data were consistent. The detailed protocol used for the t...
AI summary The Evaluator conducted an audit of the 2024 tracking sheet to ensure completeness and data consistency, with detailed results in Appendix XI. This step precedes the savings calculation review.
9.1 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 as well as correcting...
AI summary The Evaluator conducted a tracking sheet audit to verify the completeness and consistency of data submitted by E1, correcting tracked savings as needed. The results, detailed in Appendix XI, ensure program component results are reliably compiled.
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.
Note on Margins 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 condu...
AI summary The Evaluator aimed for a 10% margin of error at 90% confidence level in evaluating installation rates, free-ridership, and spillover levels. Only random sampling errors were considered, excluding non-sampling errors like data entry issues or response inaccuracies.
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 evaluation of the 2024 HEA program aimed for a 10% margin of error at 90% confidence, acknowledging sampling errors but excluding nonsampling biases. Margins of error for adjustment ratios are detailed, with data sourced from Nova Scotia Power and Emera Inc.
23.2.2 Energy Savings HEA energy savings are calculated based on HOT2000 simulation results adjusted with billing analysis results and unitary savings values for prescriptive measures, as shown in the equation below. (ℎ) = ( (ℎ) − (ℎ) − (ℎ...
AI summary HEA energy savings are calculated using HOT2000 simulation results adjusted by billing analysis and prescriptive measure unitary savings values, as represented in the provided equation.
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.
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.
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 evaluation of the 2024 Residential Behaviour program reports a 10% margin of error at 90% confidence, accounting only for random sampling errors. Nonsampling errors (e.g., data entry, response bias) are excluded. Margins of error accompany savings values from billing analysis, emphasizing precision over accuracy.
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.
Home Energy Assessment Appendix XXVI: HEA Past Participant Survey Questionnaire Appendix XXVII: HEA Past Participant Survey Results Appendix XXVIII: HEA Expired Participant Survey Questionnaire Appendix XXIX: HEA Expired Participant Survey...
AI summary The document outlines appendices related to the Home Energy Assessment (HEA) program, including past and expired participant surveys, audit tracking sheets, billing analysis methodologies, reporting requirements, and 2024 recommendations. These appendices focus on program evaluation, performance monitoring, and participant engagement metrics.
This appendix presents the results of the EPI 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 submitted...
AI summary The appendix details an audit of the EPI tracking sheet conducted by the Evaluator, which corrected discrepancies in energy and peak demand savings data. Key corrections include a 20% reduction in smart thermostat savings and fixing unitary values for certain devices, leading to significant changes in tracked and corrected values.
On-site Visit Protocol The on-site visits were conducted to verify the number of products installed and recorded in the tracking system. The on-site observation protocol was developed to serve two main purposes: (1) Establish the installat...
AI summary The on-site visit protocol outlines procedures to verify installed product quantities, assess installation rates, and identify product removals. Evaluators collect data on lamp locations, domestic hot water systems, faucet configurations, and heating sources to validate assumptions about energy savings calculations.
Methodology The billing analysis consisted of calculating the change in electrical energy consumption by comparing levels before and after participation in Green Heat for a group of recent participants (treatment group). To account for var...
AI summary The methodology uses a difference-in-differences approach to measure Green Heat program savings by comparing treatment and control groups, normalizing energy data for weather, and calculating unitary savings for MSHPs and wood/pellet stoves. This isolates program impacts from external factors like the pandemic.
Weather Normalization The Evaluator normalized the consumption values obtained from AMI data using normal weather data to obtain annual consumption values that are aligned with a typical meteorological year. A regression was used to determ...
AI summary The Evaluator normalized AMI data using regression analysis and heating/cooling degree days from 2008-2023 for five weather stations (Debert, Greenwood, Halifax, Sydney, Yarmouth) to align consumption with a typical meteorological year. Multiple iterations were conducted to select optimal balance temperatures for accurate normalization.
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.
Selection of the Treatment and Control Groups The initial treatment group included 1,803 HEA participants who were selected based on the following criteria: - › Be 100% electrically heated according to HOT2000 (meaning that the primary hea...
AI summary The treatment group of 1,803 HEA participants was selected based on heating type, assessment dates, and AMI data availability. The control group of 1,227 past HEA participants was chosen to match the treatment group, ensuring similar characteristics per UMP guidelines. AMI data was incomplete due to missing NS Power account numbers, affecting the number of participants analyzed.
4.1 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 results obtained from the...
AI summary A tracking sheet audit was conducted by the Evaluator to verify the completeness and consistency of data submitted by E1. The audit results, presented in Appendix IV, led to corrected tracked savings figures being used in the report.
Table 1: Overview of Data Collection Activity Descriptor This Instrument Instrument Type Participant Survey Estimated Time to Complete 15 minutes Target Audience Business Expected Number of Completions 50 (17 LED linear lamps, 17 LED linea...
AI summary This document outlines data collection activities for a participant survey targeting businesses, with a focus on energy efficiency programs such as Instant Rebates and LED adoption. It includes research objectives related to participation, market opportunities, free-ridership, and satisfaction.
DEFINITIONS Accuracy Reflects the proximity of measurements to the true value. Baseline To determine gross savings, a baseline (or base case) is established, providing detailed information about the reference (e.g. pre-existing or standard...
AI summary The text defines key terms such as accuracy, baseline, bias, billing calibration, and confidence interval, providing detailed explanations for each. These definitions are relevant to energy efficiency and measurement processes.
SEM Findings and Recommendations This subsection provides the key findings and recommendations from the SEM evaluation. The recommendations are also outlined in Appendix XVIII. 2024 SEM-Finding: SEM exceeded its net electrical energy and p...
AI summary SEM exceeded 2024 energy and peak demand savings targets by 6% and 14% respectively, maintained stable participation levels over five years, and improved M&V accuracy. Seven of 12 participants achieved savings, with peak demand estimates showing significant methodological improvements.
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.
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.
Note on Margins 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, excluding nonsampling errors. Margins of error were calculated for Retrofit and New Construction programs in the 2024 Custom evaluation, but not for Building Optimization due to full project reviews. Examples of calculations are in Appendix II of the DSM Programs Evaluation Executive Summary.
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.
3.2 Gross Savings Gross savings correspond to the changes in energy consumption resulting from actions taken by Retrofit participants regardless of why they participated.[8](#page-185-4) E1 tracks the annual gross savings of each project....
AI summary Gross savings are calculated based on energy consumption changes from Retrofit participants' actions. E1 tracks annual gross savings using participant data, supplemented by engineering assumptions and professional judgments, following best measurement and verification practices for commercial/industrial energy efficiency projects.
3.2.2 Project Review Sampling Methodology For the regular Retrofit project category, the Evaluator used a stratified sampling approach to select 19 projects for review from a total of 44 projects completed in 2024. More specifically, the E...
AI summary The Evaluator used a stratified sampling approach to review 19 Retrofit projects from 44 completed in 2024, prioritizing larger projects and applying weighted average adjustment ratios. Two large compressed air leak audit projects were reviewed more thoroughly due to deviations from standard methodologies. Savings were extrapolated using adjustment ratios specific to solar PV estimation tools (Retscreen and PV Watts).
3.2.3 Project Review Findings The Evaluator reviewed the project sample to ensure the best measurement and verification practices were applied for commercial and industrial energy efficiency projects and adjusted the tracked savings accord...
AI summary The Evaluator reviewed energy efficiency projects to ensure best measurement and verification (M&V) practices were applied, adjusting tracked savings accordingly for commercial and industrial programs.
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.
6.1 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 taken by the Evaluator are detailed in Appendix IV, leading to corrected tracked savings results presented in the report.
6.2 Gross Savings Gross savings correspond to changes in energy consumption resulting from actions taken by Building Optimization participants regardless of why they participated. This subsection describes the review methodology used for a...
AI summary Gross savings in Building Optimization projects are calculated based on energy consumption changes, using data from participants and E1, supplemented by engineering assumptions. The Evaluator applied best measurement and verification practices for commercial and industrial energy efficiency projects.
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.
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.
Table 30: Implementation Status of Past Recommendations for SEM # Recommendations Status Comments 2023- SEM-R1 Consider including plant-level key performance indicators (KPIs) and tracking their progression since program component inceptio...
AI summary Table 30 outlines the implementation status of past recommendations for Strategic Energy Management (SEM). Three recommendations are discussed, with two in progress and one marked as complete. The recommendations focus on tracking performance indicators, project implementation, and communication of energy management benefits.
Table 31: 2024 SEM 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 for each project accuratel...
AI summary Table 31 outlines the 2024 SEM Evaluation Approach, focusing on calculating gross and net results from energy management initiatives. It includes evaluation objectives, research questions, and methodologies such as tracking sheet audits, project file reviews, and calculation of energy savings and GHG emissions.
11.2.1 Project Review Findings The Evaluator reviewed the calculation methodologies for the seven projects based on project documentation and information from interviews with participants and the Service Provider. All but two projects used...
AI summary The Evaluator reviewed seven projects' savings calculation methodologies, noting most used a bottom-up M&V approach consistent with Custom Retrofit standards. A top-down approach was deemed appropriate for whole-facility measures where component-level monitoring was impractical. The Service Provider's methodology selection aligned with approved M&V procedures.
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.
Project Review Protocol The Evaluator used the same project review protocol in 2024 as the one used for the 2023 Custom Retrofit evaluation. The protocol includes questions and assessment fields for measurement and verification (M&V) plans...
AI summary The Evaluator employed a consistent project review protocol in 2024, mirroring the 2023 Custom Retrofit evaluation. This protocol includes M&V plan assessments, measure-specific worksheets, and pre-review documentation analysis from EfficiencyOne (E1), covering feasibility studies, savings calculations, and M&V reports.
ECONOLER Efficency Nova Scotia On-Site Visit Protocol - 2024 1. General Information Virtual Visit Date: Team: Contact Name: Contact Title: Role on the projet: Company Name: Facility Name: Address: Consultant: Administer FR/SO? Alternate FR...
AI summary This document outlines an on-site visit protocol for Efficiency Nova Scotia in 2024, focusing on the collection of facility and project information, as well as M&V (Measurement and Verification) plans and documentation. It includes sections for general information, facility description, project description, and M&V documentation.
Baseline - 4. Does the baseline measurement match what the project says is the baseline, and is it aligned with program rules? - 9. Has anything changed between the baseline and reporting period? (Y/N) a.If #9 is Y, has a non-routine adjus...
AI summary The text outlines baseline measurement verification questions, focusing on alignment with program rules, changes between baseline and reporting periods, and equipment useful life. It emphasizes non-routine adjustments and existing equipment status as key considerations in regulatory evaluations.
Operating Schedule Include notes on schedule and seasonal variations. The operating schedule corresponds to the typical one (non-COVID). - 6. Is the M&V period appropriate? (Y/N) - a. Do both the baseline and reporting periods cover all ra...
AI summary The Operating Schedule section includes questions about the appropriateness of the M&V period, focusing on whether it covers all operational ranges, occurs near the implementation of an energy efficiency project, and captures seasonal effects.
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.
- € Strategic Energy Management Efficiency Nova Scotia ECONOL≣R Project Review Protocol 1. General Information Interview/Site Visit Date: Project ID: NSPI Rate Code: Company Name: Address: Team: Contact Name: Contact Title: Phone: Email: L...
AI summary The document outlines the Strategic Energy Management (SEM) Efficiency Nova Scotia Project Review Protocol, focusing on energy consumption drivers, participation history, and measurement and verification (M&V) protocols. It includes sections for baseline and reporting periods, regression analysis, and energy savings calculations.
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.
4.2 Gross Savings Gross savings correspond to the change in energy consumption resulting from actions taken by participants regardless of their reasons for participating.[13](#page-141-4) For each SBES Audit or DIY project, E1 calculates g...
AI summary Gross savings are calculated based on changes in energy consumption from participant actions using methods like the CIRx Screening Tool or custom calculations by auditors. The CDI pilot uses unitary algorithms.
3.1 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. It should be noted t...
AI summary The Evaluator conducted a tracking sheet audit to verify the completeness and consistency of data submitted by E1, excluding Residential DR due to pre-established methodology. The audit ensured necessary information was provided for savings calculations, with corrective actions detailed in Appendix I.
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.
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.
7.2.1 Project Reviews and Meter Data Analysis For the Aggregator pathway, available DR capacity is established by comparing the predicted loads (baseline) to the actual loads during DR events using meter data. Baselines for DR events are e...
AI summary The document outlines E1's methodology for calculating available DR capacity using meter data and adjustment factors, including a shift from scalar to additive adjustments. It describes project reviews of 30 projects, stratified sampling, and evaluation processes guided by the BNI DR Baseline Considerations document to verify compliance and assess new rules.
DEMAND RESPONSE PROGRAM Final Appendix Report 2024 DSM EVALUATION March 25, 2025
AI summary The document presents the Final Appendix Report for the 2024 Demand Side Management (DSM) Evaluation, dated March 25, 2025, focusing on the Demand Response Program. It includes a placeholder image reference, suggesting the report contains visual data or analysis.
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.
Residential DR: Event Day Graphs of Expected Vs Actual Loads 0 5 10 15 20 25 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Load (kW) Hour of Day Event Hour Tag Expected Load (kW) Actual Load (kW) H...
AI summary The document presents event day graphs comparing expected versus actual residential demand response (DR) loads, highlighting discrepancies between projected and real-world energy consumption during specific events. Visual data illustrates load (kW) by hour of day, including HDD15 metrics, to evaluate DR program performance.
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.
Balance Temperature The heating balance temperature is the outdoor temperature below which a heating system will operate to meet thermal comfort requirements in a building. The cooling balance temperature is the outdoor temperature above w...
AI summary The text defines heating and cooling balance temperatures, noting typical ranges for commercial (13°C–18°C) and residential (15.5°C–21°C) buildings. It references the Uniform Methods Project and New York's interactive effects calculation file, with the Evaluator adopting 13°C and 18°C for heating and cooling, respectively.
Spillover Scoring Typically, spillover is captured in a series of three to five questions to discern if any further energy efficiencyrelated actions took place after the participant took part in the program. If the participant took further...
AI summary Spillover scoring assesses additional energy efficiency actions taken by program participants post-enrollment. Savings from these actions are multiplied by a spillover score and divided by total program savings to calculate net spillover impact. Follow-up interviews are recommended for complex measures to estimate savings accurately.