Topic:"Energy Efficiency Resource Assessment Model" in M12349

1 1.0 EXECUTIVE SUMMARY 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules, 4 Nova Scotia Power Incorporated (NS Power, the Company) is required to provide the Nova Scotia 5 Energy Board (NSEB...

AI summary NS Power is required by the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules to submit an annual 10-year load forecast to the NSEB. The 2025 Load Forecast covers 2025-2035, incorporating factors like sales history, weather, economic indicators, and SAE models for residential and commercial forecasts, along with econometric-based industrial forecasts.

Page 15 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.0 DISCUSSION OF MAJOR INPUTS 2 3 4.1 Historical Class Sales and Energy Data 4 5 The Load Forecast is developed using NS Power’s “billed” sales...

AI summary NS Power's 2025 Load Forecast Report explains the use of 'billed' sales data over 'accrued' sales due to billing delays, with a transition to AMI-based accrued sales pending sufficient historical data. Residential/commercial forecasts use 2015-2024 billed sales, while industrial forecasts use extended periods for improved model fit. Peak demand data is derived from hourly load data.

EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 15: Household Size 2 3 4 Household Size decreased steadily from 2000 to 2016 as population growth stagnated while new 5 housing continued to increase. F...

AI summary The 2025 Load Forecast Report discusses trends in household size and economic factors influencing load forecasting. Household size decreased from 2000 to 2016 and stabilized until 2022, with a slight increase followed by a decline as population growth slows. Economic models use GDP and employment data, with specific considerations for manufacturing employment forecasts based on the 2024 CBoC forecast.

Page 28 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary The 2025 Load Forecast Report has been redacted, with confidential information removed. The document provides an analysis of expected electricity demand in Nova Scotia for the year 2025.

1 Figure 19: Economic Forecast Comparison GDP Employment Housing Starts 2025 (%) 2026 (%) 2025 (%) 2026 (%) 2025 2026 Conference Board 1.3 1.5 0.9 0.0 4851 4453 of Canada BMO 11 1.7 1.3 0.8 1.0 12000 10000 RBC 12 1.5 1.3 2.8 2.4 7000 7000...

AI summary The text discusses the use of end-use data from NRCan and the US Energy Information Agency (EIA) in the SAE model to predict energy consumption trends. The model combines historical saturation trends with survey data and adjusts projections to align with actual billing data from NS Power.

f the study objectives. NS Power understands that the current estimated 23 timeline for completion of the study is Q3 2025.17 24 25 To date, NS Power has participated in meetings with Net Zero Atlantic and provincial staff in a 26 working...

AI summary The document discusses the timeline for a study related to Net Zero Atlantic, with NS Power participating in working group meetings. The study is expected to be completed in Q3 2025. The text also references the 2025 ACE Plan and load forecast reports, including comparisons of residential and commercial electric heating saturation models.

2 series was adjusted to meet the E3 series by around 2040 as shown in Figure 22, resulting in a 3 slightly lower trajectory. 4 5 Figure 22: Commercial Space Heating Saturation Comparison 6 7 For the 2024 forecast, energy and peak values p...

AI summary The document discusses the adjustment of energy and peak values in the 2024 forecast, comparing SAE models with E3 hybrid scenario models. Heat pump energy and peak usage are compared between NS Power and E3 models, showing significant differences in energy and peak forecasts by 2030 and 2035.

Page 35 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary This document is a redacted page from the 2025 Load Forecast Report, which contains confidential information that has been removed. The report likely outlines projections and analysis related to electricity demand in Nova Scotia for the year 2025.

s the difference between years for the control group. Using this same method, coincident 26 peak EV effect was taken as the maximum Y2 – Y1 kW difference for hour-ending 8 pm. 27 28 Aggregated across a whole year, the EV effect represents...

AI summary The document discusses the load impact of electric vehicle (EV) charging, estimating an annual increase of 3820 kWh per customer from at-home charging and a coincident peak impact of 0.39 kW. The analysis uses data from E3’s EV Load Shaping Tool to model EV load shapes and includes the impact of both at-home and public charging on load and peak demand.

1 Figure 28: EV Mileage Assumptions and Load/Peak Modeling Results Vehicle Avg kW/vehicle Avg kWh/year Type on Peak LDV 4,202 0.6 MDV 8,205 1.6 HDV 113,890 7.3 2 3 Figure 29 provides the estimated energy and peak impacts that correspond to...

AI summary The text discusses the impact of electric vehicles (EVs) on energy and peak load forecasts, presenting data on average energy consumption and peak load contributions for different vehicle types (LDV, MDV, HDV) and providing cumulative forecasts from 2025 to 2029 under varying assumptions.

no forecast reduction in NS Power peak demand, as solar generation occurs at times non-coincident 21 with NS Power’s system peak (refer to Section 10 for further discussion on peak impacts). 22 20 The Smart Grid Nova Scotia solar garden in...

AI summary The text discusses the lack of forecast reduction in NS Power peak demand due to solar generation occurring at non-coincident times with system peak. It also references the Smart Grid Nova Scotia solar garden in Amherst and a 2024 Net Metering Report by NS Power.

1 4.5.1 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the use of 4 electricity in Nova Scotia, and the forecast takes the projected energy and demand savings into 5 account. Betwe...

AI summary The document discusses the role of Demand Side Management (DSM) in Nova Scotia's electricity use and forecasting, noting that DSM savings are incorporated into regression models. It highlights the challenge of double-counting DSM impacts and describes an approach to address this by introducing historical DSM savings as a load-modifying variable in the model.

pplier licence issued by the NSUARB included a condition that no sales can occur before the effective date prescribed by the Governor in Council. In addition, it was a condition of the license DATE: June 27, 2025 Page 55 of 94 REDACTED (CO...

AI summary The document discusses the 2025 Load Forecast Report, which outlines energy production and load distribution for the RTR market. It includes a forecast of 500 GWh of wind energy production by 2027 and details the load distribution across customer classes, with NS Power providing top-up energy under the Energy Balancing Service tariff.

1 In prior forecasts single-family homes were assumed to use approximately 16,000 kWh per year 2 on average, while multi-unit homes were assumed to use 4,860 kWh per year on average. Using 3 AMI data, a study was conducted to compare elect...

AI summary The text compares electricity consumption between single-family homes (SFU) and multi-unit residential buildings (MURB), noting SFUs use about three times more electricity. Newer buildings, regardless of type, show lower consumption due to energy-efficient technologies and materials, while older buildings have higher consumption due to outdated systems.

1 be attributed to factors such as larger living spaces and a greater concentration of electric space 2 and water heating compared to older buildings. Despite improvements in energy efficiency, these 3 factors can lead to an overall rise i...

AI summary The text discusses factors influencing electricity demand in new single-family units (SFUs) and multi-unit residential buildings (MURBs), noting that while MURB consumption decreased by 27%, SFU consumption increased by 19%. It also highlights the use of a structural index incorporating building shell efficiency and projected house size changes to forecast residential electricity use.

Page 62 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 6.0 COMMERCIAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General rate 4 classes. Like the resident...

AI summary The Commercial SAE model forecasts electricity use for the Small General and General rate classes based on factors like heating, cooling, GDP, employment, and monthly HDD and CDD. The model incorporates annual end-use intensity projections and adjusts for sector-specific employment data. The impact of the COVID-19 pandemic on commercial sales is reflected in historical data, and the pandemic variable has been removed from the 2025 forecast.

g to the RTR 4 market (-165 GWh per year), and higher solar generation will reduce sales by a further 237 GWh 5 by 2035. 6 7 Figure 50: Historical and Forecast Annual General Demand Sales 8 9 10 Please refer to Appendix B for tables with a...

AI summary The 2025 Load Forecast Report discusses the impact of Renewable to Retail (RTR) market participation and solar generation on electricity demand, projecting a decrease in sales by 7.3% between 2025 and 2035. Large General Service class forecasts are based on customer surveys and historical data, with growth expected from institutional facilities, particularly hospital expansions.

) 2025 Load Forecast Report Redacted 1 Figure 56: Historical and Forecast Annual Other Industrial Sales 2 3 4 7.4 Municipal 5 6 The Municipal class comprises municipal electric utilities that purchase wholesale electricity from 7 NS Power...

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 other providers through OATT, leading to a reduction in municipal load due to increased third-party energy purchases.

Page 73 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary The document is a redacted section of the 2025 Load Forecast Report, which contains confidential information. It is part of a regulatory proceeding in Nova Scotia, likely related to energy planning and forecasting.

een moved back to 2028 to align with the current program development and expected ramp- 24 up for both DLC and CPP. DR forecasts continue to use an effective load carrying capacity 25 (ELCC) of 48 percent to account for the fact that the f...

AI summary The document discusses the alignment of program development timelines with the 2028 timeframe, the use of effective load carrying capacity (ELCC) for demand response (DR) forecasts, and the distribution of a draft study scope document for the next ELCC study by NS Power in February 2025.

benefit of utility-managed load 11 shift events. Results from the pilot, as presented in E1’s 2023 Demand Response Program Final 12 Report, 32 indicated that the average available DR capacity per controller during the utility winter 13 pea...

AI summary The document discusses a pilot project involving utility-managed load and demand response (DR) capacity, highlighting average DR capacity during peak periods and the impact of controller removal due to quality concerns. The project was paused for the 2023/2024 season, but new controllers are being installed in 2024. References to the Effective Load Carrying Capacity (ELCC) Study and a DSM Programs Evaluation Report are included.

of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary The 2025 Load Forecast Report provides an analysis of projected electricity demand for the year 2025. The report is redacted, indicating that confidential information has been removed.

1 contributions, and finally DSM. As discussed in Section 4.4, the EV contribution to peak is 2 expected to be partially mitigated via utility managed charging. The firm peak assuming the 3 current non-coincident residential EV peak value...

AI summary The document discusses the impact of EVs and space heating on peak demand, estimating a 60 MW increase from EVs and a 46 MW reduction from space heating by 2035. It also analyzes the 2024 system peak, which occurred at 8am on February 21, with a recorded peak of 2,088 MW and a firm peak of 2,001 MW, influenced by factors like interruptible load, weather, wind, and unexplained variances.

Page 84 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary The 2025 Load Forecast Report has been redacted, indicating that confidential information has been removed from the document. The report likely contains details on projected electricity demand for the year 2025.

1 10.2 Solar Impact to Peak 2 3 The estimate for solar production at system peak demand was updated for 2025 based on an 4 average of the factors used in the 2024 forecast and recalculated averages based on 2024 data. 5 Hourly data from si...

AI summary The document updates the estimate for solar production at system peak demand for 2025, based on data from six community solar farms. It highlights the varying coincidence factors across months, with winter months showing 0% and summer months ranging from 24% to 31%. These factors are influenced by weather patterns and system peak timing, and will need to be monitored in future forecasts.

INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 70: Commercial End-Use Peak Shares 2 3 4 5 As with the residential class, there is a significant increase in peak contribution from EVs, 6 increasing from 0.3 percent in 2025...

AI summary The 2025 Load Forecast Report highlights a growing contribution of electric vehicles (EVs) and electric heating to peak demand in the commercial sector, increasing from 0.3% to 6.2% for EVs and from 39.8% to 42.8% for electric heating between 2025 and 2034. Other end uses, such as lighting, see a decline in peak contribution.

Page 89 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Grid NS project, or data obtained from other regions of the world where driving characteristics 2 and public charging infrastructure may differ...

AI summary The text discusses the use of AMI data in analyzing EV charging patterns and SFU/MURB consumption, highlighting the importance of data granularity and integration with external datasets. It also outlines future research directions, such as assessing the impact of electric heating on load shapes and refining forecasting models.

g the 10-year period, which is explained mainly by 21 the impact of weather variation (HDD) and economic impact in the long term. The black line and 22 points represent actual system totals. 23 DATE: June 27, 2025 Page 92 of 94 REDACTED (C...

AI summary The 2025 Load Forecast Report discusses system energy and peak demand sensitivity over a 10-year period, influenced by weather variations (HDD) and economic factors. It presents scenarios using P10/P90 ranges and includes adjustments to the peak end-use model based on wind and temperature data.

Page 93 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted

AI summary The 2025 Load Forecast Report provides an analysis of projected electricity demand for the year 2025. This report is redacted and contains confidential information that has been removed.

1 This analysis provides a potential range of outcomes for the 2025 Load Forecast. Energy is most 2 sensitive to economics over the long term, while peak is most sensitive to temperature. In addition, 3 peak is more variable overall. The i...

AI summary The document discusses the 2025 Load Forecast and compares it with the 2024 Load Forecast and the Evergreen IRP cases. It highlights the sensitivity of energy to economics and peak load to temperature, while noting that the Evergreen IRP scenarios assume a slower adoption of EVs and heat pumps, with lower RTR market load estimates compared to the current forecast.

Appendix A – Forecast Values 1.1 Table A1: Energy Requirement – 2025 NS Power Forecast Energy Forecast

AI summary Appendix A presents Table A1, which outlines the 2025 energy requirement forecast by NS Power. The table provides projected energy needs, serving as a basis for planning and regulatory considerations.

Residential Commercial Industrial Municipal Total Year Sector Growth Sector Growth Sector Growth and Other Growth Losses Energy Growth GWh % GWh % GWh % GWh % GWh GWh % 2015 4,504 2.3 3,251 0.9 2,456 -2.6 197 -0.2 691 11,099 0.6 2016 4,264...

AI summary The table shows energy consumption trends across residential, commercial, industrial, and municipal sectors from 2015 to 2027, with varying growth rates and fluctuations, including significant declines in some years and increases in others.

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.

tilization (Use) and a variable that captures changes in end-use efficiency and saturation trends (Index). The heating variable is calculated as: XHeat = HeatUse × HeatIndex HeatUse is defined as: 𝐻𝐻𝐻𝐻𝐻𝐻𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻...

AI summary The text describes the calculation of heating and cooling variables used in load forecasting. Heating variable (XHeat) is calculated using factors like Heating Degree Days, household size, employment compensation, and electricity price. Cooling variable (XCool) follows a similar structure but applies to cooling demand. Both variables incorporate efficiency and saturation indices to model usage trends.

×� � ×� � 𝐶𝐶𝐶𝐶𝐶𝐶15 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻15 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅15 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃15 Where CDDy,m is the Cooling Degree Day for a given month m of the year y, HHSize is the average household size, ResEcon is Employment Compensation divided by House Hold popula...

AI summary The text describes the calculation of energy use variables, including CoolIndex and OtherIndex, using factors such as household size, employment compensation, electricity price, and appliance efficiency. These variables are used in load forecasting and are based on a baseline year of 2015.

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.

0.00% REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 6 of 34 Residential Model Statistics Model Statistics Iterations 1 Adjusted Observations 120 Deg. of Freedom for Error 105 R-Squared 0.990 Adjusted...

AI summary The document presents statistical model outputs for residential load forecasting, including metrics like R-squared, AIC, BIC, and MAPE, as well as model fit and reconciliation data for the years 2025-2035.

hange -20.3% 33.3% 3.4% -0.2% -7.2% 0.0% 7.7% to load XHeat = (Efurn + HP Heat + Secondary Heat + Furnace Fans) x HeatUseVariable x Coeff Note that because these factors are multiplicative, the growth rate for the intensities is multiplied...

AI summary The text discusses residential load forecasting inputs, specifically XHeat and XCool, which are calculated using various intensities, coefficients, and variables. These calculations involve factors like heating and cooling use, appliance efficiencies, and other variables to estimate load changes from 2025 to 2035.

1.1% 25.2% 9.2% 6.1% 0.0% 58.4% 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 outlines the methodology used for forecasting load in the residential and small general service commercial sectors. It details the calculation of XCool and XOther, which are derived from various end-use intensities, prices, and climatic factors such as HDD and CDD, multiplied by coefficients and other variables.

ice (Pricem), monthly HDD and CDD and a variable accounting for the number of days in a given month: XHeatm = EIheat × Pricem -.15× SmlGenVarm× HDDm XCoolm = EIcool × Pricem -.15× SmlGenVarm× CDDm XOtherm = EIother × Pricem-.15 × SmlGenVar...

AI summary The document presents a model for forecasting monthly electricity use, incorporating price elasticities, heating and cooling degree days, and adjustments for various external factors such as the pandemic, hurricanes, and shifts in usage post-COVID. An ARMA process was added to improve the model's accuracy.

ated as Small Gen Average Use (14,020 kWh/customer in 2025, 14,505 kWh/customer in 2035) x number of customers (26,981 in 2025, increasing to 29,804 in 2035). Small General Average Use –Regression XHeat XCool XOther Binaries ARMA Res Avera...

AI summary The document provides a forecast of small general average use for electricity customers in Nova Scotia from 2025 to 2035, breaking down usage into components such as heating, cooling, and other uses. It includes regression models and input variables for forecasting load, with specific calculations for XHeat and XCool.

2025 Load Forecast Report Appendix B Page 15 of 34 Heating is calculated as [(Heat2035-Heat2025) x HeatUse x Coeff x Scaling]/WtXHeat2025 Small General Input Variables – XCool Cooling CoolUse Coefficient Scaling Total Variable Factor Xcool...

AI summary The text presents formulas and tables related to heating and cooling load calculations for a 2025 load forecast report, including variables such as HeatUse, Coeff, Scaling Factor, and CoolUseVariable. It also details changes in load from 2025 to 2035 for various components.

tor REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 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 funct...

AI summary The General Service rate class model estimates monthly billed sales based on heating, cooling, and other use variables, incorporating price elasticity, GDP, employment, HDD, CDD, and various binary shift variables to improve model accuracy. An ARMA process is also added to the model.

IDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 17 of 34 Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.751 0.029 25.743 0.00% MStructGen.WtXCool 0.719 0.059 12.200 0.00% MStructGen.WtXOther 1.069 0...

AI summary This section presents statistical data from the 2025 Load Forecast Report, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. These statistics help assess the significance and reliability of the model used for forecasting load demand.

NFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 22 of 34 Industrial Econometric Model Details Small Industrial model SmlInd_Salesm = MBin.Janm + MBin.Febm + MBin.Marm + MBin.Aprm + MBin.Maym + MBin.Junm + MBin.Julm + MBin.Aug...

AI summary The Small Industrial model uses a binary variable for October 2022 to account for billing delays after Hurricane Fiona, with the equation incorporating monthly binary variables and a coefficient for economic indicators.

A binary variable was added for October 2022 to account for billing delays after hurricane Fiona. Variable Coefficient StdErr T-Stat P-Value MBin.Jan 21712.759 1366.359 15.891 0.00% MBin.Feb 18642.698 1368.355 13.624 0.00% MBin.Mar 19340.7...

AI summary A binary variable was introduced in October 2022 to account for billing delays caused by Hurricane Fiona. The variable, MBin.Oct22, has a coefficient of 4830.446 and a statistically significant p-value of 0.00%. The table provides statistical data on various monthly billing variables and their coefficients.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 25 of 34 Medium Industrial Model MedInd_Salesm = MBin.Janm + MBin.Febm + MBin.Marm + MBin.Aprm + MBin.Maym + MBin.Junm + MBin.Julm + MBin.Augm + MBin.Sep...

AI summary The Medium Industrial Model uses a combination of monthly binary variables and an economic indicator for manufacturing employment to forecast sales. A binary variable for 2016 and later years was added to improve the model fit, reflecting a shift from declining to flat sales trends. The model uses historical data from 2006 to 2024.

oned from declining to flat. As discussed in Section 4.0, a historic period of 2006–2024 was used to improve model fit. Variable Coefficient StdErr T-Stat P-Value MBin.Jan 20853.444 1440.731 14.474 0.00% MBin.Feb 20342.822 1439.895 14.128...

AI summary The text presents statistical data from a load forecasting model, focusing on coefficients and their significance levels for various variables, including monthly bins and economic indicators, over a historical period of 2006–2024. The data shows strong statistical significance with very low p-values.

MOVED) 2025 Load Forecast Report Appendix B Page 26 of 34 Medium Industrial Model Statistics Model Statistics Iterations 1 Adjusted Observations 228 Deg. of Freedom for Error 214 R-Squared 0.706 Adjusted R-Squared 0.688 AIC 15.021 BIC 15.2...

AI summary This section presents statistical details of the Medium Industrial Model used in the 2025 Load Forecast Report. It includes model statistics such as R-squared, adjusted R-squared, AIC, BIC, and other relevant metrics. Some values are marked as #NA, and the report contains redacted confidential information.

le, the energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coefficients found after running the sales model. ResOther can be written as: ResOtherm =ResSalesm- b1×ResXHeatm-b2×Re...

AI summary The energy sales model separates residential energy sales into weather-dependent and non-weather-dependent components, with the latter including past DSM activity. Non-weather variables are normalized to an average MW load basis, and binaries are included to account for billing issues and improve model fit.

0.287 0.104 2.752 0.71% REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 33 of 34 Peak Model Statistics Model Statistics Iterations 13 Adjusted Observations 120 Deg. of Freedom for Error 99 R-Squared 0....

AI summary This section presents statistical details of a peak load forecasting model, including metrics such as R-squared, mean absolute percentage error, and other statistical indicators that evaluate the model's performance and accuracy in forecasting peak load demand.

2035 2057 2023 2105 Actual Firm Peak: 1,861 2,014 1,951 1,993 1,949 1,954 1,875 2,061 2,397 2,001 Percent Error 2014 4.2% -4.1% -1.8% -4.1% -2.2% -2.3% 1.3% -8.0% -21.0% -5.4% 2015 -6.0% -2.8% -4.8% -3.1% -3.9% -0.8% -10.4% -23.4% -7.3% 20...

AI summary The text presents historical data on actual firm peak and percent error for various years, likely related to energy demand forecasting. The data spans from 2014 to 2023, with values showing fluctuations over time. The document is part of a load forecast report, and some content has been redacted due to confidentiality.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 8 of 9 Figure C6: System Peak Forecast Accuracy System Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast For...

AI summary The document presents a table showing the accuracy of system peak forecasts issued in various years from 2014 to 2023, comparing forecasted values for each year up to 2024. The data indicates trends in forecast accuracy over time.

rmal distributed weight, meaning that after 10,000 trials, a histogram of the variable will have an average and standard deviation that coincides with the distribution of the last 20 years. 6. Incorporating variability in the individual en...

AI summary The document discusses the use of Monte Carlo simulations and statistical analysis to model variability in energy and peak load forecasts. Historical data challenges and the inclusion of heat pumps in models are noted. Normal distributions are used to generate probabilistic forecasts and sensitivity diagrams.

112 176 Industrial 59 128 Total 246 421 10 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 11 of 19 COVID Variables • For 2025 the COVID variables have been updated in both the Residential and Commerci...

AI summary The document discusses updates to the 2025 Load Forecast Report, including changes to the COVID variables in residential and commercial models, and reasons for lower-than-forecast residential sales in 2024, such as warmer weather, slower EV sales, and increased behind-the-meter solar production.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to CA Information Requests NON-CONFIDENTIAL

AI summary The document is a non-confidential portion of the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to information requests by the Commissioner of the Environment and Sustainable Resource Development (CA). Key themes include load forecasting and regulatory compliance processes.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-6: 2 3 Page 31 discusses that the end-use energy estimates are adjusted so that the intensities are 4 consistent with NRCan c...

AI summary NSPI explains adjustments to end-use energy estimates in the 2025 Load Forecast Report, scaling intensities to match 2015 billed totals. It reevaluated EV data using AMI data but notes heat pump intensity data is unavailable for comparison with E3 forecasts.

(c) The shipyard is not one of the industrial customers surveyed as they are not in the large 30 customer class, but NS Power is in contact with them and their proposed expansion is Date Filed: August 19, 2025 NSPI (NSEB) IR-20 Page 1 of 2...

AI summary NSPI is collaborating with Irving Shipbuilding on a load impact study for a shipyard expansion. NSPI explains its use of 'achievable potential' in demand response forecasts, citing ongoing program development and future data updates.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 Appendix D, page 6 states that, for the sensitivity of energy sales, weather is the strongest 4 variable but “in the...

AI summary NSPI explains that economic factors like income, GDP, and employment compound over time, influencing energy sales similarly to weather impacts. Higher household income and industrial growth increase energy demand, with economic sensitivity matching weather effects in long-term forecasts.

15% 75% † Temperature at Peak (Peak HDDs) † Monthly HDD † Economics Wind at Peak † Monthly HDD † Monthly CDD † Economics REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-23 Attachment 1 Page 2 of 3 Weather N...

AI summary The document includes a Load Forecast Report for 2025, with data on temperature, heating degree days (HDD), cooling degree days (CDD), and wind at peak. It references historical HDD data from Weather Canada (Shearwater).

cast Report Synapse IR-23 Attachment 1 Page 3 of 3 Historical Monthly CDD 15 from Weather Canada (Shearwater) Data Month 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 January February M...

AI summary The document contains a table with historical monthly cooling degree day (CDD) data from 2005 to 2024 and references the 2025 Load Forecast Report (NSEB M12349), along with NSPI's responses to Synapse Information Requests. The data is sourced from Weather Canada (Shearwater).

2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (c) Refer to page 33 of the report. Please provide the full scope of work for “The Path to 2 2030” regarding the Province’s work to s...

AI summary The document contains requests from the NSEB to NSPI regarding the 2025 Load Forecast Report, specifically concerning the 'Path to 2030' hybrid scenario study, the development of stock forecasts for residential and commercial hybrid heating systems, and the heat pump saturation rate in 2024. The NSEB is seeking detailed explanations, supporting evidence, and modeling documentation.

1 (v) For the hybrid heating scenario, how did E3 model switching behavior 2 between electric and fossil backup heat? What temperature threshold, if any, 3 was assumed for backup system use? 4 5 (vi) Please explain how NSPI estimates its H...

AI summary The text consists of a series of questions directed at Nova Scotia Power Inc. (NSPI) regarding its modeling of heat pump behavior, estimation of heating intensities, and the discrepancy between energy and peak forecasts. The questions focus on assumptions, data sources, and validation of models related to hybrid heating systems and residential energy use.

residential. 25 26 (ii) Please refer to 2025 Load Forecast Report Attachment 01, Shares tab. 27 28 (iii) The E3 study was completed in 2022 and based on data from 2021. 1 https://www.ethree.com/wp-content/uploads/2023/12/E3_NS-Power_Electr...

AI summary The document references the 2025 Load Forecast Report and mentions an E3 study completed in 2022 based on 2021 data. It also includes a link to an electrification report and refers to NSPI's responses to Synapse Information Requests under NSEB M12349.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) Please refer to Figure 24 of the report. 2 3 (f) 4 (i) For Figure 21, please refer to Attachment 1, HP Stock tab. For Figure 22,...

AI summary NSPI provides responses to Synapse Information Requests regarding the 2025 Load Forecast Report, referencing specific figures and attachments. The report discusses the use of the E3 model for forecasting hybrid heating systems and heat pump impacts, citing the 'Current Trends Hybrid' scenario and peak temperature assumptions.

1 (vii) Please refer to NSEB IR-8. 2 3 (viii) The difference is based on the incremental increase in heat pumps between 2025 4 and the corresponding reference year, not the absolute number. In Attachment 1 5 (HP Stock tab) the difference i...

AI summary The text discusses the calculation of heating intensity for year x using a formula based on the base year 2015, incorporating Unit Energy Consumption (UEC) values from Natural Resources Canada and adjusting for actual customer usage. It also references the difference in heat pump stock between 2025 and the reference year and mentions no adjustment was made to the E3 numbers due to similarity in percentages.

𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒2015 19 20 This is calculated for all the heating components (electric baseboards, heat pumps, 21 secondary heat and furnace fans). 22 23 (ii) In Figure 24, the terms “% Install Non-Elec. Heat” and “% Install Elec. Heat...

AI summary The text discusses the calculation of heating components, including electric baseboards, heat pumps, and secondary heat, and references the percentage of heat pumps installed as replacements for non-electric or electric heating sources. It also mentions the 2025 Load Forecast Report and NSPI's responses to Synapse Information Requests.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (b) Please see the table below. The values for 2025 are from E1’s supply agreement, while the 2 values from 2026-2035 are from the ba...

AI summary The 2025 Load Forecast Report provides energy and demand projections from 2025 to 2035, sourced from E1’s supply agreement and the E1 Potential Study. The base case aligns with current DSM levels and is used in the IRP forecast, with no changes to the forecast for Annual DSM Savings in the 2025 LFR compared to the 2024 LFR except for the addition of a 2035 forecast value and updated regression model coefficients.

ng energy use and, to a lesser degree, peak loads. Specific effects appear in Figures 40 and 59 of the Report. Overall, NSPI projects that DSM will reduce the 2035 load by 575 GWh, or about 5 percent. It should be noted, however, that the...

AI summary NSPI projects DSM will reduce 2035 load by 575 GWh (5%), but modeled savings are adjusted due to historical embedded effects from prior DSM programs. Residential and commercial sectors require 58.6% and 43% adjustment factors, respectively, to avoid double-counting. Synapse acknowledges NSPI’s approach to subtract incremental savings above historical norms.

tial SAE model in some detail to better understand the drivers behind the forecast. 9 2025 Load Forecast, page 20. 10 2025 Load Forecast, page 61. 11 2025 Load Forecast, Appendix B, pages 1-8. Synapse Energy Economics, Inc. Evidence Regard...

AI summary The document details the SAE model's XHeat, XCool, and XOther variables, explaining their components and forecasted changes. XHeat is projected to increase 7.7% due to electric heating and heat pumps, XCool will rise 58.4% from heat pump cooling adoption, and XOther will decrease 1.3% from reduced lighting and TV use. Cites pages 20, 61, and Appendix B of the 2025 Load Forecast.

These adjustments are applied annually and presented in Figure 46 of the 2025 Load Forecast report as “Hybrid Adjust.” Table 6. NSPI’s analysis of the impacts of hybrid heating on heat pump (HP) load E3 HP Change in NS Power Change in NS P...

AI summary The document discusses annual adjustments to load forecasts, highlighting discrepancies between NSPI's and E3's assumptions about hybrid heating saturation rates and efficiency. NSPI's 2025 Load Forecast shows significant energy and peak load changes, while Synapse critiques the validity of comparing models with differing assumptions.

ommendations in previous years. It represents an important step toward improving the accuracy and transparency of NSPI’s load forecasting by grounding assumptions in observed customer usage patterns. Recommendations and considerations For...

AI summary The text recommends improving NSPI’s load forecasting by adjusting heat pump models with scaling factors based on E3 scenarios, developing explicit hybrid heating modeling, and validating assumptions using AMI data. It emphasizes long-term modeling improvements and supports NSPI’s commitment to analyzing AMI data for accuracy and transparency.

cific inputs for this variable for 2020–2024 in the residential model could be better supported, Synapse agrees with NSPI’s overall approach in phasing out reliance on the separate COVID-19 variable. 2.3. Commercial Sector The commercial s...

AI summary Synapse agrees with NSPI on phasing out the COVID-19 variable in residential load forecasts. Commercial sector load declines due to RTR adoption, lower EV forecasts, and higher solar generation. Industrial forecasts use historical data and surveys, with subsectors showing mixed trends, including flat load for large industrial customers.

lly derived, however, there is necessarily some associated uncertainty. Overall, we feel that this type of approach and the proposed magnitudes of the adjustments are appropriate for this forecast. Synapse Energy Economics, Inc. Evidence R...

AI summary The 2025 peak load forecast shows a slight reduction in growth rate compared to the 2024 forecast, primarily due to lower EV adoption growth. The actual 2024 peak was significantly lower than forecasted, leading to a larger anticipated increase in 2025. Adjustments for weather, wind, interruptible load, and timing explain most of the variance between forecast and actual peak loads.

modeled peak resulting from these issues in the approach to assessing water heater and heat pump energy and peak effects, since NS Power uses an end-use approach to formulating its peak forecast. 57 3.3. Demand-side Management To develop i...

AI summary The document discusses NS Power's approach to forecasting peak load reductions from demand-side management (DSM) programs, including the use of an effective load carrying capacity (ELCC) of 48% and the adjustment of DSM targets from 2025 to 2028. It also references the Board's directive to update the Capacity Value Study for demand response and the potential challenges in incorporating the next ELCC study into the 2026 Load Forecast.

ends to focus on demand response in the next ELCC study. 62 In responses to discovery, NS Power clarified that it may not be possible to incorporate the next ELCC study into the 2026 Load Forecast. 63 We appreciate that NS Power is elevati...

AI summary The text discusses concerns regarding Nova Scotia Power's (NS Power) use of the Energy Load Contribution Credit (ELCC) factor for demand response (DR) in its load forecasts. Synapse Energy Economics Inc. (Synapse) raises concerns about the outdated basis of NS Power's ELCC assumptions and the lack of consideration for interactive effects and future electrification impacts. Recommendations are made for NSPI to conduct a portfolio ELCC analysis and consider more demand response programs.

Document: 322941 Date Filed: 07/24/25 Page 5 1 a) The AEC report notes that investment in housing is up 17% and identifies growth nodes 2 in the Halifax Regional Municipality that will be developed over the Load Forecast period. 3 Does NS...

AI summary The text includes questions from intervenors to NS Power regarding capacity to address demand increases in Halifax, consideration of infrastructure projects in load forecasts, updates from industrial customers, and the methodology for using 'achievable potential' in peak demand estimates. It also references the Evergreen Integrated Resource Plan (IRP) and requests data comparisons for demand response programs.

2025 M12349 NOVA SCOTIA ENERGY BOARD IN THE MATTER OF: The Public Utilities Act - and - IN THE MATTER OF: Nova Scotia Power Incorporated (“NS Power”) 10 - Year Energy and Demand Forecast (2025 Load Forecast Report) NON-CONFIDENTIAL INFORMA...

AI summary Nova Scotia Energy Board is requesting non-confidential information from Nova Scotia Power Inc. regarding their 2025 Load Forecast Report under the Public Utilities Act. Synapse Energy Economics Inc. is acting as Board Counsel Consultant, with responses due by August 19, 2025.

ii. Number of customers for HP, Hybrid, Resistance, Wood, and Fossil Fuel heating 27 systems as indicated in Figure 17. 28 iii. Number of customers using heat pumps as the sole technology 29 iv. Number of customers using heat pumps as prim...

AI summary The text requests data on residential heating technology adoption (heat pumps, oil, gas, etc.) and new customer projections from 2024-2050. It also seeks details on the Province’s 'Path to 2030' hybrid scenario study, including draft materials and scope of work.

vide the full scope of work for “The Path to 4 2030” regarding the Province’s work to study the hybrid scenario. Please provide all draft 5 study materials, if they are publicly available. 6 d. Refer to Figures 20 and 21 and hybrid heating...

AI summary The text contains requests from a regulatory body to NSPI regarding the 'Path to 2030' study, including detailed explanations of E3's hybrid heating forecasts, supporting evidence for heat pump saturation rates, and discrepancies in data. It also asks for NSPI's own heating stock saturation forecasts.

ase provide NSPI’s own forecast of space heating stock saturation from 2024 22 to 2050 for both heat pumps and electric resistance heating, corresponding to 23 Figure 20. 24 f. Refer to Figures 21 and 22. Please provide for all the forecas...

AI summary The proceeding requests NSPI to provide forecasts of space heating stock saturation for heat pumps and electric resistance heating from 2024 to 2050, commercial floor area data, and explanations for excluding hybrid heat pump systems in the SAE framework. It also seeks clarification on adjustments to energy and peak forecasts for hybrid heating.

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.

accurate, it could result in an overstated industrial load forecast. The SBA believes that the selection of inputs to the forecast model must be done carefully and transparently. Commercial and Industrial Electrification Forecasts In respo...

AI summary The SBA argues that NSPI's load forecast model for commercial and industrial electrification requires careful, transparent input selection to avoid overstating industrial load. While NSPI attributes changes between 2024 and 2025 forecasts to removing 2024 data, the SBA requests full transparency on all changes.

(as will federal investment tax credits where applicable). And, scale, workforce experience, and competition within the Nova Scotia market will further drive down installation costs. 6. It is an object of the NSIESO to “[p]rior to, or as p...

AI summary The NSIESO is tasked with conducting a DER Potential Assessment to inform the 2026 Load Forecast Report and procurement activities, emphasizing DER deployment to alleviate grid constraints. Fuel-switching to in-province electricity aligns with the province’s energy policy, including the Clean Power Plan’s focus on electrifying heating and transportation.

-4- increasing on a per EV basis more so than the previous Reports. This forecast is worth revisiting for the 2026 Load Forecast Report to confirm accuracy. In the 2023 decision, the Board did not agree with NS Power’s attribution of the C...

AI summary The document discusses load forecast variances, the Board's rejection of NS Power's pandemic and heat pump explanations for 2022 NSR discrepancies, and directives to re-evaluate unexplained variances. It notes reduced unexplained variance in 2024, declining demand projections due to RtR adoption and solar growth, and a 7.3% general demand decline by 2035.