Topic:"Energy Efficiency Budgets" in M12861

1 Figure 21: Economic Forecast Comparison GDP Employment Housing Starts 2026 (%) 2027 (%) 2026 (%) 2027 (%) 2026 2027 Signal49 9 1.3 1.8 0.6 0.2 8181 6771 BMO 10 1.4 1.8 0.5 0.6 8500 8000 RBC 11 1.5 1.6 0.4 0.4 7800 6000 TD 12 1.6 1.2 0.3...

AI summary The document discusses economic forecasts for GDP, employment, and housing starts through 2027, as well as the use of end-use data from NRCan and the EIA to develop load forecasts. Historical data and efficiency estimates are used to model residential and commercial energy consumption trends.

6 Heat pump usage continues to grow in the province as more customers find heat pumps an efficient 7 way to heat and cool buildings as well as providing environmental and financial benefits. The end- 8 use model uses an estimated saturatio...

AI summary Heat pump usage in Nova Scotia is growing, but the forecast for 2026 shows a decline due to the closure of rebate programs. The number of installations is expected to drop by around 20% in 2026, followed by a gradual decline of 2.5% per year. Despite this, strong uptake is still anticipated, though at lower levels than in recent years.

ters and printers 26 • Misc: other loads including motors, servers, escalators, medical equipment, etc. Small 27 scale solar and EV load have also been included in this category. 28 DATE: May 15, 2026 Page 53 of 105 REDACTED (CONFIDENTIAL...

AI summary The 2026 Load Forecast Report discusses historical and projected end-use intensities for commercial sectors, noting updated baseline data from the EIA 2025 Annual Energy Outlook. The report highlights a reduction in residential energy usage due to new appliance standards, while commercial energy intensity has increased.

1 4.8 Renewable to Retail 2 3 There is currently one Licensed Retail Supplier (LRS) approved to provide service under the RTR 4 tariffs. 33 The service is forecast to produce 500 GWh of energy through wind production when 5 fully operation...

AI summary The document outlines the Renewable to Retail (RTR) program, which is currently operated by one Licensed Retail Supplier (LRS) and is expected to produce 500 GWh of energy through wind by 2028. The RTR will serve various customer classes, with NS Power providing top-up energy under the Energy Balancing Service tariff. The forecast shows the impact of load migration to the RTR market starting in 2026.

1 6. COMMERCIAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General rate 4 classes. Like the residential model, the commercial SAE models express monthly sales as a 5 function of heating, cooling...

AI summary The Commercial SAE model forecasts electricity use for the Small General and General rate classes based on heating, cooling, and other loads, incorporating factors like GDP, employment, and price. Sales were impacted by the pandemic but rebounded in 2022 and 2023, with future projections showing a drop in 2026 and 2027 due to RTR sales migration and a rebound in the 2030s.

1 7.4 Municipal 2 3 The Municipal class comprises municipal electric utilities that purchase wholesale electricity from 4 NS Power and distribute it within their own service territories. Utility loads within these 5 municipalities include...

AI summary The Municipal class includes municipal electric utilities that purchase wholesale electricity from NS Power and distribute it within their service territories. Since 2007, these utilities have had the option to source electricity from third-party providers. By 2020, some utilities sourced 100% of their energy from third parties, reducing municipal load. Starting in 2026, these utilities will rely more on in-province wind generation and the BUTU Tariff for backup.

Page 81 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 58: Forecast Components GWh Res Comm Ind Other Losses NSR 2026 Forecast 5315 3165 2183 131 781 11575 Model -2 275 27 2 78 381 New Custom...

AI summary The 2026 Load Forecast Report outlines various forecast components, including residential, commercial, industrial, and other load forecasts, along with adjustments for factors like solar, EV, and DSM. The report highlights the impact of DSM programs and other initiatives on load forecasts.

-25 -411 models 2 37 This corresponds to the portion of energy provided by NS Power as top-up under the Energy Balancing Service tariff (as outlined in Section 4.8). DATE: May 15, 2026 Page 82 of 105 REDACTED (CONFIDENTIAL INFORMATION REMO...

AI summary The document refers to a portion of energy provided by NS Power under the Energy Balancing Service tariff, as outlined in Section 4.8. It also mentions the 2026 Load Forecast Report, which has been redacted.

1 DR forecasts continue to use an effective load carrying capacity (ELCC) of 48 percent to account 2 for the contribution of DR in supporting (or in this case, reducing) the capacity needs on the system 3 to meet the reliability standard (...

AI summary The text discusses the use of Effective Load Carrying Capacity (ELCC) in Demand Response (DR) forecasts, noting that DR contributes 48% to system capacity needs. The ELCC study will reassess this value using data from NS Power’s and E1’s DR programs. Annual DR totals by program are provided in Figure 69, showing increasing participation over time.

t . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 1 of 34 Appendix B – Forecast Model Details 2026 NS Power Load Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendi...

AI summary The residential average use SAE model is based on end uses such as heating, cooling, and other uses, incorporating variables like short-term utilization, efficiency trends, and saturation. The model uses elasticity factors and a base year of 2020 for calculations.

ariables, the residuals show a slight autocorrelation, which happens when a model does not take into account relatively small drivers that should explain the dependent variable (in this case, sales). Variable Coefficient StdErr T-Stat P-Va...

AI summary The text presents a statistical analysis of a residential model, highlighting coefficients, standard errors, t-statistics, and p-values for various variables. The residuals indicate slight autocorrelation due to unaccounted small drivers affecting sales.

0.9% 14.4% 0.9% 6.2% 0.0% 30.1% to load XCool = (Central AC + HP Cool + Room AC) x CoolUseVariable x Coeff Residential Input Variables – XOther Intensities Econ + Reg Struct Water Cook Ref/Frz Wash/ TV Light Misc Other Coeff Total Heat Dry...

AI summary The document presents load forecast models for residential and commercial sectors, including variables like heating, cooling, and other uses, with projections for 2026 and 2036. It outlines formulas for calculating load based on intensity, price, and climatic factors.

sidential model (including heat pump programs inside the SAE model). Adjustments done outside the regression include estimates for other commercial and industrial growth programs, PV, EV, RTR and DSM. Historically the XHeat, XCool and XOth...

AI summary The document discusses the 2026 Load Forecast Report, focusing on the Small General Load model. It includes adjustments for EVs, Solar, RTR, and DSM, and outlines how load is calculated based on average use per customer and customer count. The model alignment variable accounts for differences between predicted and actual prior year load.

The model alignment variable represents the difference between the predicted prior year load and actual prior year load, to ensure the class level sales start from the same point as the prior year. Small General Average Use –Regression XHe...

AI summary The text discusses a model alignment variable used to compare predicted and actual prior year load, ensuring consistency in class level sales. It also presents a regression model for small general average use, detailing variables like XHeat, XCool, and XOther, along with their coefficients and scaling factors for the years 2026 and 2036.

Factor REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 16 of 34 General Service The General Service rate class model is estimated on a total monthly sales basis where total monthly billed sales is a fu...

AI summary The General Service rate class model estimates monthly billed sales based on heating, cooling, and other usage, incorporating factors like price elasticity, GDP, employment, HDD, CDD, and days in a month. Adjustments are made for specific events and post-pandemic usage shifts, with an ARMA process improving the model.

MOVED) 2026 Load Forecast Report Appendix B Page 19 of 34 General Service Model Fit General Demand 2026-2036 Reconciliation The general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total s...

AI summary The General Demand Load forecast for 2026-2036 is presented, showing adjustments for RTR, Hybrid, EV, Solar, and DSM. The forecast includes load from the model, regression alignment, and total load with DSM captured by end uses.

: 1. Once the deterministic SAE class regression models are completed, the regression coefficients are exported into the Monte Carlo tool, called Oracle Crystal Ball (MS Excel add-on). 2. The Monte Carlo process assumes that the regression...

AI summary The text describes a forecasting process using deterministic SAE regression models and Monte Carlo simulations with Oracle Crystal Ball. Historical weather and economic data are used to generate probabilistic forecasts, with variations treated as normal distributions. The process involves running 10,000 trials to assess how forecast predictions respond to input variations.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2026 Load Forecast Report Appendix D Page 6 of 8 Figure D5: Relative Sensitivity of Peak In terms of the sensitivity of the energy sales forecast to the various input variables, Figures...

AI summary The document discusses the sensitivity of energy sales forecasts to various input variables, highlighting that weather has the strongest near-term impact while economics becomes equally important in the long term. Demand-side management (DSM) has the largest impact on both energy and peak demand, with solar, EVs, and hydrogen facilities also having significant effects.

ation, changed class distribution. Peak Model Updated based on analysis of 2026 system peak (25th January). Specific directives from the 2025 Load Forecast 5 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix...

AI summary The 2026 Load Forecast Report notes a significant reduction in heat pump installations, primarily due to the discontinuation of financial incentives such as the Oil to Heat Pump Affordability (OHPA) program and Home Heating System Rebates in 2025. The forecast predicts a decrease of ~4,000 installations per year compared to the 2025 Load Forecast, with corresponding declines in heat pump heating and cooling demand by 2035.

Year Month Pred 2023 7 1204.3 2023 8 1159.3 2023 9 1083.0 2023 10 1179.2 2023 11 1560.4 2023 12 1803.5 2024 1 1786.2 2024 2 1904.9 2024 3 1786.4 2024 4 1458.4 2024 5 1172.8 2024 6 1167.4 2024 7 1252.1 2024 8 1277.1 2024 9 1141.0 2024 10 12...

AI summary The text presents a table of monthly predicted values (Pred) for various years from 2023 to 2028. The values show fluctuations over time, with peaks in the months of December and troughs in the months of May and June. These figures may represent forecasts for energy demand, production, or other metrics relevant to the regulatory proceeding.

0 676,251 250,546 32,905,454 Aug-20 2020 Aug 31 1,620,552 886,397 660,618 3,380,496 1,194,720 3,082,560 1,563,984 4,250,748 955,782 1,771,848 3,498,560 1,444,560 647,136 2,719,248 2,283,480 1,331,928 - 498,384 680,940 729,486 - 466,110 32,...

AI summary The document contains a table with financial data and a redacted section from a 2026 Load Forecast Report Attachment 4, Page 36 of 80. The table includes figures related to various financial metrics and operational data for different months in 2020.

LG LI LG LI Current Forecast Current ForecasPrevious Forecast Previous Forecast 2010 416.1 3162.7 2011 414.9 2769.8 2012 410.8 1417.3 2013 404.1 1863.6 2014 393.5 1799.0 Other Industrial Sales Large General Sales 2015 414.5 1723.2 420 2016...

AI summary The text presents historical data on Large General Sales and Other Industrial Sales from 2010 to 2021, showing trends over time with numerical values for each year. It includes a visual representation with a graph indicating changes in sales volume.

356.3 1511.8 Historic Sales Previous Forecast Current Forecast Current Forecast Firm Weather Normalized Peak 2031 356.9 1493.9 355.6 1509.6 2700 2032 355.4 1488.6 354.9 1507.5 2033 354.1 1483.6 354.4 1505.6 2500 2034 352.1 1478.8 353.1 150...

AI summary The text presents historical sales, previous and current forecasts for various years, including peak load calculations and weather-normalized peak values, with data spanning from 2031 to 2036 and a peak value of 2700.

Codes and Residential Commercial Residential Commercia Total Residential Residential Commercial Commercial Industrial Industrial LED Standards Total Total Loss Loss Residential Cummulativ Commercial l Industrial Industrial Incrementa Total...

AI summary The text presents a table with data on residential and commercial energy usage, including incremental and cumulative values, percentages, and loss metrics over several years, starting from 2008 to 2019. The data appears to track energy efficiency measures, such as LED standards, and their impact on energy consumption and losses.

1 Request IR-1: 22 provincial policy on electrification and decarbonization. 23 24 (h) No, NS Power does not agree. Reducing the forecast energy use reduction from a potential 25 residential hybrid heating program on this basis implies tha...

AI summary NS Power disagrees with reducing the forecast energy use reduction from a potential residential hybrid heating program, arguing that supplemental non-heat pump electric heating consumes significant energy annually. They also state that a Monte Carlo analysis is not feasible due to lack of historical data and that current hybrid heating study scenarios are appropriate for understanding program impacts.

(h) Please identify additional data and analysis that NS Power may require to fully understand the performance of TVP rates and achieve NS Power's goals for this program. Response IR-3: (a) Please refer to the tables below. Please that val...

AI summary The response refers to tables containing data values before the application of effective load carrying capacity (ELCC), which NS Power may need to analyze the performance of TVP rates and achieve program goals.

M12875, E-1, E1 Q1 2026 Demand Side Management Report, May 25, 2026, pages 19-21. 1 For TVP actual demand reduction, the actual demand response is calculated by multiplying 2 the average impact per customer for morning and evening peak per...

AI summary The document discusses the calculation of actual demand reduction for TVP, the application of ELCC, and the estimated benefits of TVP from NS Power's AMI Application. It also references past approvals and evaluations of TVP tariffs and the lack of explicit reporting requirements for TVP savings from M08349.

NON-CONFIDENTIAL 1 Request IR-1: 4 forecast percentage change are solar generation (cumulative solar generation totals -51 5 GWh in 2027 versus -24 GWh in 2026) and DSM (-32 GWh in 2027 versus -19 GWh in 6 2026). However, growth in Renewab...

AI summary The document discusses changes in Net System Requirement (NSR) forecasts from 2026 to 2036, highlighting factors such as increased EV load, new residential load, reduced solar generation, and reduced demand-side management (DSM). Growth in Renewable to Retail (RTR) migration is also noted as a major driver of change.

2 (c) The factors listed were not considered explicitly in the adjusted forecast. Please refer to 3 Synapse IR-5 for additional information on the new housing estimates. 1 Request IR-4: 5 reduce or shift consumption during those periods. B...

AI summary The text references specific requests and responses in a regulatory proceeding, including inquiries about energy consumption adjustments and EV charging assumptions. It directs readers to external documents such as Synapse IR-5 and M12861 for detailed information.

1 2 11 NS Power recommended examining additional cases that electrify these off‑peak, non‑winter hours. Historical hourly usage patterns indicate there are opportunities in heat pump heating, resistive electric water heating, and EV chargi...

AI summary NS Power suggests examining additional electrification cases during off-peak, non-winter hours, highlighting opportunities in heat pump heating, electric water heating, and EV charging. They recommend avoiding the inversion of the avoided cost series for energy efficiency and demand response, as the load profiles differ significantly from those of SE measures involving fossil fuel heating conversions.

Non-large customer classes Year Peak (MW) annual sales (GWh) 2016 1,911 7,696 2017 1,857 7,804 2018 1,896 8,048 2019 1,844 8,173 2020 1,850 7,989 2021 1,784 7,957 2022 1,968 8,304 2023 2,302 8,387 2024 1,908 8,530 2025 2,084 8,655

AI summary The table presents data on non-large customer classes, showing peak demand in MW and annual sales in GWh from 2016 to 2025. The data indicates fluctuations in both peak demand and annual sales over the years, with a notable increase in peak demand in 2023.

hanges. First, NS Power increased the heating intensity enhancement for the years 2016-2021, resulting in an average enhancement of 2.6% per year, substantially raising the baseline heating intensity. [E](#page-6-1)xhibit N-1(i), 2023 LFR...

AI summary NS Power made several changes to heating intensity factors over the years, including increasing the heating intensity enhancement and trend factor, and later reducing them in 2026. The changes in 2026 were not well explained, and the methodology used became unclear due to the way values were pasted into spreadsheets without explanation.

SUMMARY OF PROFESSIONAL EXPERIENCE - 2023– Present Vice President, Grid Strategies, LLC . Provides research, technical assistance, and expert testimony on electric- and gas-utility planning, economics, and regulation. Reviews electric util...

AI summary The individual has extensive experience in utility planning, regulation, and energy policy, spanning roles in research, regulatory policy, and advocacy. They have provided expert testimony, designed energy efficiency and electrification programs, and worked on renewable energy and market data initiatives.

rid Nova Scotia Project on behalf of the Nova Scotia Consumer Advocate. Cost classification, decommissioning costs, justification for software vendor selection, and suggested changes to project scope. Nova Scotia UARB Matter No. M09499, di...

AI summary The text outlines various regulatory matters where Paul Chernick provided expert testimony on behalf of the Nova Scotia Consumer Advocate and Small Business Utility Advocates. Topics include capital expenditures, decommissioning, cost classifications, load forecasting, and evaluation of electric vehicle charging programs.

Relative to the 2025 forecast, growth in this year's residential forecast is flatter. In the 2025 forecast, modeled average use for existing customers rose by 5.8 percent over the forecast period, driven largely by heat pump electrificatio...

AI summary The residential electricity forecast for this year shows flatter growth compared to the 2025 forecast, with reduced heat pump adoption and the winding down of the Oil to Heat Pump Affordability program. NS Power adjusted its housing completion forecasts based on Signal49's data, but the adjustment is viewed as somewhat arbitrary.

Table 5. Small general load: post regression (GWh) Load from Regression Model (GWh/year) Model Alignment EVs Solar RTR SG. DSM Adj. SG. Sales (with DSM) Total SG. DSM (at meter) DSM captured by end uses 2026 407 13 3 (1) (0) (4) 418 (7) (3...

AI summary Table 5 presents the projected small general load post-regression for 2026 and 2036, including factors like EVs, solar, RTR, and DSM adjustments. It shows the load from the regression model, model alignment, and changes in load over time.

Table 6. General demand load: post regression (GWh) Load from Regression Model (GWh/year) Model Alignment RTR Hybrid Impact EV Solar GD. DSM Adj. GD Sales (with DSM) Total GD. DSM (at meter) DSM captured by end- uses 2026 2,362 (13) (10) -...

AI summary Table 6 presents a forecast of general demand load post-regression for 2026 and 2036, showing changes in load from various factors such as RTR, EV, Solar, and DSM adjustments. The data highlights the impact of these factors on load changes over time.

3.5. Municipal Sector The municipal class comprises municipal electric utilities that purchase wholesale electricity from NS Power and distribute it within their own territories. It is small and is not modeled through the SAE or econometri...

AI summary The municipal class consists of municipal electric utilities that purchase wholesale electricity from NS Power. Starting in 2026, these utilities will serve most of their demand directly through their own wind facility, reducing bundled municipal load from 120 GWh to 45 GWh, while backup and top-up purchases increase. NS Power will still provide backup capacity and reserve margin.

Recommendations NS Power should construct its reference-case EV forecast primarily in a bottom-up fashion based on realistic inputs from a reputable source, reserving the federal-target trajectory for a high case. NS Power should model man...

AI summary The document recommends that NS Power construct its EV forecast using a bottom-up approach with realistic inputs, model managed charging based on participation rates, and exclude PHEV charging from DCFC and workplace L2 peak modeling due to its limited impact on peak demand.

Savings persistence The cumulative savings as of the test year is the DSM input variable in NS Power's energy regression model.[21](#page-19-1) NS Power uses a rolling 10-year accumulation period as a proxy for measure expiration, rather t...

AI summary The document discusses NS Power's use of a rolling 10-year accumulation period in its energy regression model to estimate savings persistence for demand-side management (DSM) programs. It suggests replacing this approach with a vintage-based persistence model that uses measure-category lives and decay curves for more accurate savings estimation.

Peak savings NS Power assumes that the proportion of DSM savings already embedded in the energy forecast also applies to peak savings.[23](#page-19-3) Energy and peak impacts, however, may be captured differently in the historical data and...

AI summary NS Power assumes that the proportion of DSM savings embedded in energy forecasts also applies to peak savings. However, energy and peak impacts may be captured differently in historical data and model variables, leading to different levels of embedded savings. It is recommended that NS Power estimate embedded energy and peak savings separately for more accurate peak demand forecasting.

6. RECOMMENDATIONS - 1. NS Power should continue to monitor the impact of trade policy and consider explicitly incorporating tariff impacts into its future forecast if they are expected to have a material impact on load growth. - 2. Concer...

AI summary The recommendations focus on improving NS Power's forecasting methods for load growth, hybrid heating participation, heat pump efficiency metrics, EV charging, solar installations, and DSM savings accumulation. Emphasis is placed on using more accurate modeling approaches, incorporating updated data, and clarifying assumptions to enhance forecast reliability.

Request IR-5: - Economic Information (Section 4.4, p. 28-34) - a. Why did NS Power not consider the impact of trade tariffs in its load forecast? - b. Please explain in detail how NS Power understands the potential impacts of trade tariffs...

AI summary Request IR-5 seeks clarification on NS Power's load forecast, focusing on the impact of trade tariffs on economic assumptions, the accuracy of customer addition projections, and the basis for changes in housing completion forecasts. The questions aim to understand the quantitative reasoning behind NS Power's assumptions and forecasts.