Topic:"Energy Efficiency Budgets" in M12349

0.7 2 3 The major Canadian banks provide short term (1-2 year) forecast for some of the key economic 4 indicators, and these are provided in Figure 19. The Conference Board of Canada forecast is in 5 line with the forecasts from the banks...

AI summary The document discusses economic forecasts provided by major Canadian banks and the Conference Board of Canada, noting alignment except for housing starts. Adjustments to housing completions bring forecasts in line with bank predictions.

Page 71 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 energy under the Wholesale Market Backup/Top-Up (BUTU) Tariff in cases where their third- 2 party supply is unavailable or interrupted. Their lo...

AI summary The 2025 Load Forecast Report discusses the inclusion of municipal electric utilities' peak demand in the load forecast due to NS Power's requirement to provide backup capacity. It also addresses system losses and unbilled sales, with system losses expected to remain between 6.0 and 7.0 percent over the next decade.

Forecast 2028 133 36 2,274 2,443 0.8 -14 Forecast 2029 134 39 2,285 2,458 0.6 -14 Forecast 2030 132 39 2,303 2,474 0.7 -14 Forecast 2031 132 39 2,332 2,503 1.2 -14 Forecast 2032 133 38 2,364 2,535 1.3 -13 Forecast 2033 133 38 2,402 2,573 1...

AI summary This section provides details on the residential average use SAE model used in the 2025 NS Power Load Forecast. The model incorporates variables for heating, cooling, and other uses, along with factors such as energy efficiency savings and short-term utilization.

IDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 4 of 34 OtherIndex is defined as: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 �𝑆𝑆𝑆𝑆𝑆𝑆𝑦𝑦 /𝐸𝐸𝐸𝐸𝐸𝐸𝑦𝑦 � 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑦𝑦,𝑚𝑚 = � 𝐸𝐸𝐸𝐸15 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 × 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚 𝑇𝑇...

AI summary The document defines the OtherIndex term as a function of saturation, efficiency, and calibration weights for various end-uses, and explains that the AvgEESavings term captures past DSM savings for the residential class, with a regression coefficient indicating that DSM activity reduces load.

d regression coefficient, b4, assesses the portion of embedded DSM activity that is already included in the billed sales information. The negative sign of b4 indicates that DSM activity reduces load. The COVID variable is a binary that sta...

AI summary The text discusses the use of regression coefficients and binary variables in modeling load impacts, including the effect of DSM activities and the influence of the COVID-19 pandemic on residential load. It also mentions the inclusion of binary shift variables to improve model fit and address anomalies in billing data.

riables, 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).

AI summary The analysis indicates that residuals exhibit slight autocorrelation, which occurs when a model fails to account for small factors that influence the dependent variable, sales.

Variable Coefficient StdErr T-Stat P-Value MStructRes.WtXHeat 0.926 0.018 51.423 0.00% MStructRes.WtXCool 1.147 0.185 6.205 0.00% MStructRes.WtXOther 0.925 0.032 29.357 0.00% MSales.AvgEESavingsProfiled -0.414 0.161 -2.577 1.14% MBin.Jan 64...

AI summary The text presents a statistical model summary with coefficients, standard errors, t-statistics, and p-values for various variables in a residential load forecasting model. The table includes variables such as heating, cooling, other usage, energy efficiency savings, and monthly bins, as well as a variable related to the Covid-2020 stepped update.

(703) (412) Change 5.8% 7.6% 7.9% -11.7% -2.2% -3.8% -5.0% -1.6% to load Res Sales = Existing Customer + New Customer + EV + Solar + RTR + Hybrid + DSM Existing customer load is calculated as Res Average Use (10,475 kWh/customer in 2025, 1...

AI summary The text discusses the calculation of residential load, including existing customer load, new customer load, and the impact of factors like EVs, solar, and demand-side management. It provides data on residential average use and its components, such as heating, cooling, and other uses, along with statistical models and variables used in the analysis.

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 provides a residential load forecast model for 2025 and 2035, incorporating adjustments for EVs, solar, RTR, and DSM. The model calculates load based on average use per customer and customer count, with projections showing increases in energy use despite some reductions from efficiency programs.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 6 of 9 Figure C5: Firm Peak Forecast Accuracy Firm Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecas...

AI summary The document presents a table showing the accuracy of firm peak load forecasts issued in various years from 2014 to 2023, comparing forecasted values for each year up to 2024. The data illustrates how forecast accuracy has changed over time.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 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, hydrogen facilities, and batteries also showing significant influence.

ad, timing pushed out Residential COVID variable removed from forecast COVID Variable timeframe, commercial variable removed entirely 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 6 of 19 Average C...

AI summary The document discusses updates to residential load forecasting, including the removal of the residential COVID variable and the adjustment of average consumption estimates for new customers based on AMI and customer segmentation data. The forecast also incorporates the Conference Board of Canada’s housing completion forecasts, adjusted for historical underestimation.

the Report.52 The forecast rates of growth in electrification appear to be generally consistent with those in the previous year’s forecast, though this year’s projections are slightly lower overall. 2.6. The Municipal Sector The municipal...

AI summary The municipal sector's energy load is projected to increase slightly over time, though its forecasted energy use is questioned due to assumptions about third-party service. DSM savings remain modest, with residential savings accounting for a small percentage of total load, and historical effects influencing model results.

Date Filed: 07/28/2025 Synapse (NSPI) Page 9 of 12 1 ii. Has NS Power considered incorporating hybrid systems as a separate end-use or 2 intensity variable in the SAE model? If not, explain why. 3 iii. What steps would be required to enabl...

AI summary The document contains eight questions directed at NS Power (NSPI) regarding hybrid heating systems modeling, heat pump peak load impacts, forecast discrepancies, and methodology. Key topics include hybrid system integration in SAE models, temperature thresholds for heat pump performance, and NSPI's use of E3's analysis versus its own estimates.