Topic:"Demand Side Management" in M12861

4.5.8 Commercial and Industrial Growth............................................................................................ 55 19 4.6 Price Data ..........................................................................................

AI summary The text outlines sections of a regulatory proceeding document covering topics such as commercial and industrial growth, price data, demand-side management, renewable energy integration, and sector-specific analyses (residential, commercial, industrial/municipal). It includes a date (May 15, 2026) and pagination details.

.......................................................................... 81 16 Figure 58: Forecast Components .................................................................................................. 82 17 Figure 59: Average pea...

AI summary The text lists figures related to forecasting components, peak demand analysis, regression models, and demand response. It includes visual representations of forecasted vs. actual peaks, temperature records, peak normalization models, and end-use demand breakdowns, likely supporting regulatory analysis of energy system reliability and efficiency measures.

1 1. 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 Nova Scotia Power (NS Power) is required to submit annual 10-year energy and demand forecasts to the Nova Scotia Energy Board (NSEB). The Independent Electrical System Operator Nova Scotia (IESO-NS), established under the Energy Reform (2024) Act, will oversee electricity demand forecasting. The 2026 Load Forecast considers factors like weather, economic indicators, and energy efficiency programs, acknowledging inherent uncertainties.

tcomes. In electricity 26 forecasting, much of this uncertainty is due to the impact of variations in weather, energy 27 efficiency program activities, the health of the economy, government policy, the impact of 28 electrification, changes...

AI summary NS Power uses Statistically Adjusted End-Use (SAE) models to forecast load, projecting increased Net System Requirement (NSR) due to lower solar generation estimates and higher Electric Vehicle (EV) penetration. Growth is driven by new customers, heating, and EVs, offset by solar, Demand Side Management (DSM), and Renewable to Retail (RTR) initiatives.

M) and Renewable to Retail (RTR) migration offsetting sales. 14 Annual historic and forecast NSR are shown below in Figure 1. 15 16 Figure 1: Historical and Predicted Annual Net System Requirement Net System Requirement 15,000 14,000 13,00...

AI summary NS Power forecasts increasing system peak demand from 2026 to 2036, driven by customer growth, electrification, and EV adoption, offset by DSM and DR programs. The 2026 forecast adjusts for a record peak in January 2026, with the 2025-2026 forecast difference narrowing by 2036.

1 • The economic data used in the medium industrial class, including evaluation of 2 manufacturing employment, has been updated. Please refer to Section 4.4. 3 • The impacts of hybrid heat pumps have been modelled and included in the under...

AI summary Updates to the 2026 Load Forecast include revised economic data, hybrid heat pump modeling, and solar installation updates. NS Power engaged stakeholders, discussing changes like heat pump impacts, EV demand, and renewable-to-retail effects, with references to technical sections.

28 • Peak model updates. 29 DATE: May 15, 2026 Page 14 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Following the stakeholder session, the 2026 Load Forecast was updated to reflect the Demand Side...

AI summary The 2026 Load Forecast Report was updated following a stakeholder session, incorporating EfficiencyOne’s 2027-2031 Plan Application and NS Power’s 2026/2027 rate changes from their GRA Compliance Filing. These adjustments reflect revised demand-side management and demand response figures.

2026 Load Forecast Report REDACTED 1 Figure 6: Comparison of Peaks Using Actual and Estimated Large Customer Load 2 3 4 Additionally, the interruptible portion of the large customer load required estimation in order to 5 calculate the firm...

AI summary The report discusses the estimation of interruptible load for large customers to calculate firm peak load, using a historical average of 43.8% during previous system peaks. It also mentions the use of Heating Degree Days (HDD) and Cooling Degree Days (CDD) to account for temperature impacts on electric sales, with a reference temperature of 18°C.

development in the long term, it does not necessarily correlate 4 with new customer additions in the near term. 5 6 Figure 15: Yearly Change in Customers, Population, and Housing Completions 7 8 9 Comparing actual customer additions with t...

AI summary The document discusses the discrepancy between Signal49's forecast of customer additions and actual data, noting that Signal49 underestimated additions by 20% from 2020 to 2024 but was closer in 2025. NS Power adjusted the forecast for housing completions, reducing the projected decline from 24% to 10% annually, acknowledging future housing developments and slower population growth.

1 Household Size decreased steadily from 2000 to 2016 as population growth stagnated while new 2 housing continued to increase. From 2016 to 2022, the average household size was steady around 3 2.2, but between 2022 and 2024 household size...

AI summary The text discusses changes in household size over time and the evolution of forecasting models for commercial and industrial sectors, including updates to the medium industrial model based on data from Signal49 and Statistics Canada. The models now use a weighted variable incorporating manufacturing GDP and employment, improving statistical significance.

Page 32 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED

AI summary The 2026 Load Forecast Report provides an analysis of projected electricity demand in Nova Scotia for the year 2026. It includes detailed forecasts based on various factors such as heating and cooling degree days, as well as the impact of energy efficiency programs and demand-side management initiatives.

0 56 44 71 3,232 87 283 2036 170,636 55 45 73 3,317 89 290 2 3 4.5.2 Hybrid Heating 4 Residential Hybrid Heating 5 6 Residential hybrid heating assumptions have been updated for the 2026 Load Forecast, consistent 7 with the Board’s 2025 Lo...

AI summary The 2026 Load Forecast Report updates residential hybrid heating assumptions, aligning with the 2025 Load Forecast Decision. The report includes a regression model that incorporates potential energy and peak reductions from hybrid programs, modeled by NS Power and used by a Department of Energy-led working group with Net Zero Atlantic and other stakeholders.

Page 37 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 The modelling uses a baseline residential electric‑heating load shape derived from AMI data for 2 the year 2022. Total household consumption wa...

AI summary The 2026 Load Forecast Report uses AMI data to model residential electric-heating load shapes, focusing on heat pumps and hybrid heating scenarios. It evaluates the impact of switching from electric to non-electric heating during peak events and low temperatures, scaling results based on participation assumptions and program implementation timelines.

2028, which is the hybrid heating working group’s current target for program implementation. The 17 development of any associated incentives is part of the ongoing work on a hybrid program. 18 DATE: May 15, 2026 Page 38 of 105 REDACTED (CO...

AI summary The 2026 Load Forecast Report discusses the modeling of residential electric heating load shapes and the potential impacts of a hybrid heating program, which aims to reduce peak and energy demand. The report highlights that peak reductions depend on participation levels and hybrid event trigger scenarios, and that the overall impact may not reach the target program value.

1 Figure 24: Range of Modelled Hybrid Peak and Energy Reductions Peak Reduction Energy Reduction (MW) (GWh) Year Min Max Min Max 2028 -2 -18 -3 -23 2029 -4 -36 -5 -47 2030 -7 -54 -8 -70 2031 -9 -71 -10 -92 2032 -11 -87 -12 -114 2033 -13 -1...

AI summary The document discusses the 2026 Load Forecast, which adopts updated modelling results showing a peak reduction of −48 MW and an energy reduction of 83 GWh by 2036. The forecast incorporates residential hybrid heating as a distinct end-use and splits the NAE heat-pump category into hybrid and non-hybrid segments based on a 50% participation rate.

1 comparison of energy and peak values predicted by the SAE models with and without the 2 residential hybrid heating representation showed that the hybrid-reduction did not fully match the 3 model results discussed above, because of differ...

AI summary The text discusses the adjustment needed in residential peak and energy forecasts due to differences in modeling approaches between the SAE models and NS Power’s own modeling. The impact of hybrid heating programs is now estimated using actual customer heat use values from AMI data, rather than estimates from E3 as in past forecasts.

heating 16 scenario Year Energy Adjustment (GWh) 2028 -7 2032 -35 2036 -75 17 18 4.5.3 Water Heaters 19 NS Power anticipates that some customers who convert their oil heating systems to heat pumps 20 will also convert their hot water suppl...

AI summary NS Power projects that the adoption of heat pumps and electric water heaters will increase, leading to a decrease in energy demand by 2036. However, the growth rate is slightly lower than previously forecasted due to fewer heat pump installations. NS Power is collaborating with E1 on a demand response program involving direct control of water heaters.

1 to achieve benefits to the system (see Section 10.4). Figure 27 shows the expected changes in 2 electric water heater saturation and overall intensity over the forecast period. 3 4 Consistent with the Board’s direction in the 2025 Load F...

AI summary The document discusses the modeling of heat pump water heaters as a separate end use in Nova Scotia, their current and projected saturation rates, and factors influencing their growth, including U.S. efficiency standards and a market transformation pilot by E1. Efficiency improvements are modeled with heat pump water heaters at 40% of standard electric water heaters.

water heaters, their modelled intensity is 21 set to 40 percent of standard electric water heaters, based on estimates provided by E1. 22 Heat 22 pump electric water heater saturation and overall intensity over the forecast period are incl...

AI summary The document discusses the modelled intensity of heat pump electric water heaters, set at 40 percent of standard electric water heaters based on estimates from E1. References are made to various reports and plans related to demand-side management and load forecasting.

1,653 24 1,677 2036 82 3 85 1,661 26 1,687 2 3 4.5.4 Electric Vehicles (EVs) 4 EV sales were higher than forecast in 2025, despite the conclusion of provincial light-duty vehicle 5 EV rebates in May 2025, and the temporary pausing of feder...

AI summary EV sales in Nova Scotia exceeded 2025 forecasts despite the end of provincial rebates and temporary federal incentives. As of 2025, there were approximately 9,600 EVs in the province, leading to an updated 2026 load forecast. The ramp-up in EV adoption is expected to be less steep due to changes in policy, including the cancellation of the EV Availability Standard and a revised federal ZEV mandate.

Page 44 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Consequently, by 2036 the gap between the current EV sales forecast and the 2025 EV forecast is 2 much smaller than in the early 2030s. 3 4 A c...

AI summary The 2026 Load Forecast Report indicates that Nova Scotia is projected to have nearly 250,000 EVs on the road by 2036, compared to 225,000 in the 2025 forecast. This update reflects stronger EV sales in 2025, but the gap between the 2026 and 2025 forecasts narrows later in the forecast period due to differing sales targets.

https://ecologyaction.ca/sites/default/files/2023-05/RegionalZEVAdoptionOptions_Dunsky_March2023.pdf, 24 prepared by Dunksy Energy+Climate Advisors for the Ecology Action Center DATE: May 15, 2026 Page 45 of 105 REDACTED (CONFIDENTIAL INFO...

AI summary The 2026 Load Forecast Report discusses the impact of electric vehicles (EVs) on residential energy sales and peak demand based on analysis of customer-level AMI data. It estimates a per-customer load increase of 3820 kWh per year and a coincident peak impact of 0.39 kW for at-home charging. Commercial and MDV/HDV charging impacts are estimated using E3's EV Load Shaping Tool.

1 Figure 29: 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 30 provides the estimated energy and peak impacts that correspond to...

AI summary The text provides figures analyzing the impact of electric vehicles (EVs) on energy and peak load forecasts, including average kilowatt usage per vehicle type and projected cumulative energy and peak load impacts for the years 2026 to 2029.

1 Figure 31: PV Impact to Energy (cumulative) Total Solar Total Load Year New Installs Load (GWh) Peak (MW) Installs (GWh) 2026 2,680 -31 0 15,767 -169 2027 5,622 -66 0 18,709 -204 2028 8,864 -105 0 21,951 -243 2029 12,439 -148 0 25,526 -2...

AI summary The text discusses the impact of solar photovoltaic (PV) installations on energy load and peak demand, noting that while solar generation reduces overall customer consumption, a significant portion of energy is still supplied by the utility. Additionally, net metering customers exhibit higher peak demands compared to non-net metering customers during both summer and winter periods.

(CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 4.5.6 New Technologies 2 The 2026 Load Forecast does not assume a significant amount of distributed solar/battery storage 3 combinations or storage only deployments. T...

AI summary The 2026 Load Forecast Report discusses the limited adoption of residential battery storage due to high costs compared to gas generators, with potential future changes as technology improves. It also notes the early stage of Vehicle-to-Grid (V2G) technology in Canada, with limited commercial availability.

GM Ultium-based vehicles and the Ford F-150, Kia EV9, and Tesla Cybertruck) 19 or have announced that they will provide elements of V2G capability in the near future. 20 21 4.5.7 Intensities 22 Figure 34 provides an estimate of the resulti...

AI summary The text discusses the potential for vehicle-to-grid (V2G) capability in various electric vehicles, including GM Ultium-based vehicles, the Ford F-150, Kia EV9, and Tesla Cybertruck. It also references a figure that estimates residential end-use intensities as inputs to variables used in forecasting.

Page 52 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 The intensity trends are similar to those in prior forecasts. The use of heat pumps for space heating 2 is forecast to increase steadily throug...

AI summary The 2026 Load Forecast Report discusses trends in residential and commercial energy use, noting an increase in heat pump adoption, a decline in electric baseboard heating, and a rise in electric water heating and EV usage. Commercial end-use intensities are forecast on a per square metre basis, with various categories outlined.

2026 Load Forecast Report REDACTED 1 Figure 39: Historical and projected real electricity prices (real dollars per kWh) 2 3 4 Prices impact the class sales through imposed price elasticities. The SAE models are estimated 5 using a price el...

AI summary The 2026 Load Forecast Report discusses the impact of electricity prices on sales, using a price elasticity of -0.15. It also outlines the role of Demand Side Management (DSM) in the forecast, citing specific DSM plans and applications currently under review by the NSEB.

1 In 2022, E1’s mandate was expanded to include strategic electrification through amendments to 2 section79A(b)(iv) of the Public Utilities Act (PUA). Strategic electrification is now included in the 3 DSM definition, as follows: 4 5 (b) "...

AI summary The document discusses the expansion of E1's mandate to include strategic electrification under the Public Utilities Act, and the absence of such initiatives in the proposed 2027-2031 DSM Plan. E1 will conduct research and report annually, and NS Power will use findings to inform load forecasts. It also addresses the challenge of double counting DSM savings in forecast models.

1 model as a load modifying variable and allow the model to determine what level of DSM is already 2 included in other variables. Assuming future DSM activities are similar to historic activities, the 3 coefficient applied to historic DSM...

AI summary The text discusses a methodology for incorporating historical Demand-Side Management (DSM) into forecasting models to improve accuracy. It explains that DSM is treated as a load-modifying variable and highlights the impact of including it in regression models for residential and commercial/industrial classes.

ed to 20 be included in the forecast. 21 22 For the Commercial and Industrial classes, a combined model was created to identify the level of 23 DSM already captured by other variables. DSM impacts are not provided for Commercial and 24 Ind...

AI summary The document discusses the inclusion of Demand-Side Management (DSM) in the 2026 load forecast for Commercial and Industrial classes. A combined model was used to reduce uncertainty in allocating historical DSM savings, with a DSM variable coefficient of -0.539, indicating a 54% adjustment to future load forecasts based on DSM amounts. The model has a high adjusted R-squared value of 0.85 and a low MAPE of 2.68, indicating a strong fit.

1 Figure 40: Annual Forecast Residential DSM Savings (incremental) Year Forecast Forecast Forecast DSM DSM DSM DSM Residential Commercial Industrial captured by captured by Adjustment Adjustment DSM DSM savings DSM savings Residential Comm...

AI summary The table presents annual forecasts of residential and commercial/industrial DSM savings from 2026 to 2036, including captured savings and adjustments with coefficients. The data shows incremental savings by year and sector, highlighting the impact of DSM programs on energy efficiency.

5 66.0 42.5 7.5 30.8 23.0 35.2 26.9 2036 62.9 43.4 7.7 29.4 23.5 33.5 27.5 2 3 The methodology used to determine the DSM coefficient only works for levels of DSM that have 4 been relatively consistent throughout the historical data set and...

AI summary The DSM coefficient methodology is effective for consistent historical DSM levels but may need revision if future forecasts change significantly. NS Power notes that DSM amounts in E1’s 2027-2031 DSM Plan Application are lower than historical values and will reassess the approach once the plan is finalized.

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.

Page 62 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 home as several large employers in the province implemented back-to-office policies. It is 2 expected that residential sales will continue to i...

AI summary The 2026 Load Forecast Report predicts that residential electricity sales will increase through 2026 due to factors such as back-to-office policies, new customers, and electric heating, before stabilizing by the end of the decade. Solar, DSM, and RTR are expected to reduce load, but these effects will be offset by new customer and EV load by 2031.

Page 63 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 The forecast for new construction in 2025 was 7,726 units, while actual customer growth was 2 7,111. Nova Scotia’s population continues to incr...

AI summary The 2026 Load Forecast Report discusses population growth, housing projections, and residential energy consumption assumptions. It notes lower population growth forecasts due to reduced federal immigration targets and outlines expected housing units and energy use per household, factoring in building efficiency and house size trends.

1 Figure 45: Residential Sales Components by Year Total DSM Regression Hybrid New Solar EV RTR DSM Total Res. captured Model Adjust. Year Output Cust. (GWh) Impact Impact Sales Adjust. Sales DSM by end (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) 3...

AI summary The table presents residential sales components by year, including total output, DSM, and other factors such as new customers, solar, EV, RTR, and adjustments. It shows projected changes in energy sales and DSM impacts from 2026 to 2034.

5,393 -222 -103 2033 5,258 342 -17 -276 302 -49 -152 5,408 -286 -133 2034 5,261 368 -17 -320 406 -49 -185 5,464 -348 -162 2035 5,269 390 -17 -361 529 -49 -216 5,545 -407 -190 2036 5,288 411 -16 -394 653 -49 -246 5,646 -464 -217 2 3 Figure...

AI summary The text discusses the use of regression models and load forecasting methodologies, referencing specific figures and appendices. It highlights the inclusion of heat pump and electric baseboard loads in the Regression Model Output column and notes that the numbers are illustrative, not accounting for DSM impacts.

by the DSM coefficient). DATE: May 15, 2026 Page 66 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 46: Illustrative Contribution of Specific End Uses Year HP Heat HP Cool Baseboard Heat Water...

AI summary The 2026 Load Forecast Report provides an illustrative breakdown of energy consumption by end use, including heat pump heating, heat pump cooling, baseboard heating, and water heating, across the years 2026 to 2036.

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.

ecast period due predominantly to less solar 22 generation, greater EV load, and reduced DSM in the period 2027 to 2031. Individual class 23 components are discussed in greater detail below. 24 DATE: May 15, 2026 Page 68 of 105 REDACTED (C...

AI summary The 2026 Load Forecast Report discusses load growth in the Small General Service class, noting a 2.0% annual increase due to heating electrification, commercial EV uptake, and other factors. Solar generation and DSM are moderating this growth, while changes in EIA input efficiencies and lower solar output are contributing to a higher forecast compared to 2025.

Page 69 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 48: Historical and Forecast Annual Small General Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the ch...

AI summary The 2026 Load Forecast Report indicates a sharp decrease in General class load initially due to sales shifting to the RTR market, followed by a stabilization period. Load is expected to recover by 2036, with differences from the 2025 forecast attributed to reduced solar load and increased EV load.

ses for this are reduced solar load (-160 GWh in 2036 vs -266 3 GWh) and increased EV load (310 GWh in 2036 vs 301 GWh). 4 5 Figure 49: Historical and Forecast Annual General Demand Sales 6 7 8 9 Please refer to Appendix B for tables with...

AI summary The 2026 Load Forecast Report discusses changes in electricity demand, noting a decrease in solar load and an increase in EV load. The Large General class forecast is based on customer surveys and historical data, with input from NS Power personnel and key customers.

Page 71 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 absence of survey or publicly available information, load levels are forecast to be flat before the 2 impact of any DSM activities. 3 4 For the...

AI summary The 2026 Load Forecast Report discusses load growth projections, noting that without DSM activities, load levels are expected to remain flat. Growth is anticipated from institutional and government facilities, but overall load is forecast to decrease by 2036 due to DSM and migration to the RTR market.

Page 75 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 53: Historical and Forecast Annual Medium Industrial Sales 2 3 4 7.3 Other Industrial Rate Classes 5 6 Other Industrial rate classes inc...

AI summary The 2026 Load Forecast Report discusses the forecasting methods used for Other Industrial rate classes, including Large Industrial and Generation Replacement. Customer surveys and historical data are used to predict load, with some variance due to a major customer's reduced consumption in 2025. Load migration to the RTR market is also mentioned.

1 9. NET SYSTEM REQUIREMENT 2 3 The NSR is the energy required to supply the sum of residential, commercial, and industrial 4 electricity sales, plus the associated system losses, within the province of Nova Scotia. Loads 5 served by indus...

AI summary The Net System Requirement (NSR) in Nova Scotia is calculated based on residential, commercial, and industrial electricity sales, plus system losses. The 2025 NSR was slightly lower than forecast due to colder weather and a large customer variance. From 2026 to 2036, NSR is expected to grow at 0.4% annually, driven by new customers, heating, and EV adoption, partially offset by solar, DSM, and RTR initiatives.

growth 18 driven by new customers, space heating and EV adoption, and with solar, DSM and RTR migration 19 offsetting sales. Annual NSR is shown below in Figure 57. Forecast NSR values and the 20 contribution to NSR from the different sect...

AI summary The document discusses the growth in Net System Requirement (NSR) driven by new customers, space heating, and EV adoption, with solar, DSM, and RTR migration offsetting sales. The forecast NSR is higher than the 2025 forecast due to decreased solar generation and increased EV penetration.

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.

1 10. PEAK DEMAND 2 3 The total system peak is defined as the highest single hourly average demand experienced in a 4 year. It includes both firm and interruptible loads. Due to the weather-sensitive load component 5 in Nova Scotia, the to...

AI summary The document discusses the definition and calculation of total system peak demand in Nova Scotia, noting that the peak occurs between December and February due to weather-sensitive loads. The 2026 peak was the highest recorded at 2459 MW, occurring during a prolonged cold spell rather than extreme low temperatures.

ssion, stakeholders requested that 13 NS Power carry out a high-level sensitivity analysis using 36- and 48-hour lag temperatures in 14 place of the 24-hour value, to evaluate whether these lag temperatures impacted the peak prediction 15...

AI summary Stakeholders requested NS Power to conduct a sensitivity analysis using 36- and 48-hour lag temperatures instead of the 24-hour value. NS Power performed the analysis and found that these lag temperatures did not improve model fit or peak prediction accuracy.

Page 84 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 60: Comparison of modelled 2026 peaks Using peak model from Using improved 2025 Load Forecast, with peak model from updated input data (...

AI summary The 2026 Load Forecast Report updates the peak model used in prior forecasts, adjusting coefficients for peak normalization based on improved input data and weather variables. The report includes a comparison of modelled peaks and actual peaks, with detailed model details provided in subsequent figures.

-2.86370325 0.353258208 -8.106544 5.6E-16 -3.55613436 -2.17127215 -3.55613436 -2.17127215 2 Weekdays 41.4298405 2.545256524 16.277275 4.8E-59 36.4408134 46.4188677 36.4408134 46.4188677 3 4 Figure 62: Peak Normalization Regression Coeffici...

AI summary The document discusses peak normalization regression coefficients, including the impact of weekdays, wind speed, and temperature on electricity demand peaks. It outlines how large customer contributions to the system peak are calculated using historical load factors and forecasts.

REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 67: Weather-Normalized Firm Peak (including DR) 2 3 4 Figure 68 below shows the breakdown of the peak forecast by the various components. 5 6 Figure 68: Peak Contribution Components (MW)...

AI summary The 2026 Load Forecast Report provides a detailed breakdown of peak demand contributions by various components, including residential, EV, demand response, and DSM programs, with projections for 2026 and 2036.

1 contribution to peak is expected to be partially mitigated via utility managed charging. The firm 2 peak assuming the current non-coincident residential EV peak value of 0.5kW/vehicle and that 3 commercial charging does not include peak...

AI summary The text discusses the impact of electric vehicle (EV) charging and hybrid heating on peak demand, noting that utility-managed charging and DR programs are expected to mitigate some of the growth in peak demand. It also references the 2026 Load Forecast and the inclusion of DR programs from the 2022 Evergreen IRP, with future DR amounts based on achievable potential.

was used in the 2022 Evergreen IRP. NS Power's IRP Action Plan 23 targeted 75 MW of capacity for DR deployment by 2025, but this estimate has been moved back 24 to 2033 in the Load Forecast to align with the current program development and...

AI summary The 2022 Evergreen IRP initially targeted 75 MW of demand response (DR) capacity by 2025, but this has been revised to 2033 due to program development timelines and expected ramp-up of E1’s DR portfolio and CPP. The forecast does not include specific peak mitigation for home EV charging, as it is based on actual behavior.

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.

6 71 34 2033 38 30 6 74 36 2034 38 32 6 76 37 2035 38 33 6 77 37 2036 38 34 6 78 37 13 14 A pilot project was completed in 2021 and 2022 in conjunction with E1 to investigate direct load 15 control through water heater controls. 201 water...

AI summary A pilot project involving 201 water heater controllers was conducted in 2021 and 2022 in collaboration with E1 to explore direct load control. The project aimed to demonstrate the benefits of utility-managed load shift events. A reference is made to the 2023 Evergreen Integrated Resource Plan and a 2026 Load Forecast Report, both related to capacity and load studies.

1 Report, 41 indicated that the average available DR capacity per controller during the utility winter 2 peak period is 385 W. Results indicated that capacity tends to be lower during the last hour of a 3 four-hour event. Results also indi...

AI summary The document discusses the performance of demand response (DR) programs, including the Eco Shift program, and the challenges faced during the 2023/2024 season due to defective controllers. It highlights the resumption of installations in 2024 and the capacity achieved during the 2024/2025 season. A two-phase pilot project for commercial and industrial load control is also mentioned.

re still being evaluated. 21 22 NS Power also worked with E1 on a two-phase pilot project to investigate automatic and manual 23 control of various loads for commercial and industrial (C&I) customers, ultimately becoming E1’s 24 Smart Syne...

AI summary NS Power collaborated with E1 on a two-phase pilot project to explore automatic and manual load control for C&I customers, leading to the Smart Synergy BNI Demand Response program. Customer recruitment was scaled during 2023 for the 2023/2024 winter season, adding 67 customers. Results from that season were reported in E1’s 2024 DSM Programs Evaluation Report.

1 available DR capacity at the generator of 8 MW for the 76 participating C&I customers. In the 2 2024/2025 season, DR capacity at the generator was evaluated at 5.941 MW over 158 customers 3 total versus a target of 10.726 MW (45 percent...

AI summary The document discusses the performance of demand response (DR) programs in Nova Scotia, noting that available DR capacity fell short of targets in the 2024/2025 season. It also addresses the impact of solar energy on system peak demand, highlighting the influence of weather and timing on solar production during peak periods.

ariation in weather conditions at time of system peak, as well as the time of 5 peak occurrence. 6 7 Figure 74: Comparison of annual sales and coincident peak for non-large customer classes 8 9 10 Empirical evidence suggests the assumed re...

AI summary The document discusses the relationship between peak load and sales for residential and non-residential customer classes. It highlights that the correlation between energy use and peak load is strong for residential customers but weak for non-residential classes, leading to potential overestimation of coincident peak growth when using a bottom-up scaling approach.

Page 101 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 11. SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, including 4 economics, wea...

AI summary The 2026 Load Forecast Report discusses the uncertainty in sales and peak forecasts, influenced by factors such as economics, weather, distributed generation, electricity rates, and DSM. A P10/P90 probability analysis using Monte Carlo simulations is employed to estimate the probable distribution of future load, showing a range of approximately 488-584 GWh over a 10-year period.

Figure 76 shows the peak forecast, which includes the latest adjustments (wind, 12-hour and 24- 2 hour temperature averages) to the peak end-use model. 3 4 Figure 76: System Peak Sensitivity 5 6 7 This analysis provides a potential range o...

AI summary The text discusses the 2025 Load Forecast and its comparison with the Evergreen IRP cases, highlighting sensitivity to temperature and economics. It notes the lower estimate for load served through the RTR market in the 2022 Load Forecast and the slow ramp-up of EV and heat pump uptake to meet net zero emissions by 2050.

2,105 -3.6% 236 80.6% 765 11,424 -1.3% 2028 5,335 0.5% 3,003 -0.2% 2,161 2.7% 258 9.3% 768 11,525 0.9% 2029 5,321 -0.3% 3,014 0.4% 2,194 1.5% 258 0.0% 769 11,556 0.3% 2030 5,327 0.1% 3,031 0.6% 2,182 -0.5% 258 0.0% 770 11,568 0.1% 2031 5,3...

AI summary The document provides a table of load forecast values for various years, including percentages of change, with data spanning from 2028 to 2036. It is part of Appendix A of the 2026 Load Forecast Report, focusing on coincident peak demand forecasts for NS Power.

3 of 3 Appendix A – Forecast Values Table A2: Coincident Peak Demand - 2026 NS Power Forecast Peak Forecast

AI summary The document presents a table titled 'Table A2: Coincident Peak Demand - 2026 NS Power Forecast Peak Forecast' which provides forecast values for peak demand in Nova Scotia for the year 2026.

Interruptible Demand Firm Net Temp at 12hr Lag 24hr Lag Contribution Response Contribution Growth System Peak Temp Temp Year to Peak (reduction in to Peak Notes Peak Firm Peak only, (%) (MW) MW) (MW) (deg C) (deg C) (MW) (deg C) - December...

AI summary The table presents data on interruptible demand and firm peak contributions to system peak in Nova Scotia from 2016 to 2018. It includes metrics such as net growth, temperature at peak, and temperature lags for each year, with notes indicating specific dates and conditions.

February 6 weekday morning (min lighting load) 2026 130 8 2,346 2,484 9.6 -15 -15 Forecast 2027 129 11 2,356 2,497 1.5 -14 -12 Forecast 2028 135 14 2,377 2,526 1.2 -14 -12 Forecast 2029 142 17 2,390 2,549 0.9 -14 -12 Forecast 2030 141 20 2...

AI summary The document presents a load forecast report for the years 2026 to 2036, including details on lighting load, demand, and other metrics. It is part of an appendix to the 2026 Load Forecast Report and contains confidential information.

rUse × OtherIndex Where OtherUse = f(Seasonal Use Pattern, Household Income, Household Size, and Price) OtherIndex = g(Other Appliance Saturation and Efficiency Trends) OtherUse is calculated as 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻...

AI summary The document provides a formula for calculating OtherUse and OtherIndex, which are used in the 2026 Load Forecast Report. OtherUse is calculated based on seasonal use patterns, household income, household size, and price, while OtherIndex considers appliance saturation and efficiency trends.

Type for a particular year y, EffyType is the efficiency of an end-use of given Type for a particular year y, and EI15Type is a reference year (2020) calibration weight per end-use Type. The factors: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 �𝑆𝑆𝑆𝑆𝑆𝑆𝑦𝑦 /𝐸...

AI summary The document describes the calculation of efficiency factors and the impact of DSM programs on residential load forecasting. It includes the use of a binary variable to account for the effects of the COVID-19 pandemic on load patterns, which was later removed from the forecast period.

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.

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 text describes a load forecast model that uses price elasticities and binary variables to account for various factors such as monthly variations, pandemic-related billing issues, and hurricane impacts. An ARMA process was added to improve the model's accuracy.

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.

cients to calculate the overall impact. For example, the contribution of Heating is calculated as [(Heat2036-Heat2026) x HeatUse x Coeff x Scaling]/WtXHeat2026 Small General Input Variables – XCool Cooling CoolUse Coefficient Scaling Total...

AI summary The text presents formulas and tables for calculating energy use contributions from heating, cooling, and other categories, including variables like heat use, coefficients, scaling factors, and changes in load between 2026 and 2036.

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.

% 12.7% -6.6% -6.4% -1.2% to load Gen Sales = Sales + Model alignment (2025 actuals vs forecast) + RTR + EV Load + Solar Load + Hybrid + DSM General Demand Sales –Regression XHeat XCool XOther Binaries ARMA Sales (GWh) 2026 559,305 124,865...

AI summary The text presents load forecast data and input variables for general demand, including heat, cooling, and other factors, with comparisons between 2026 and 2036. It discusses the calculation methodology, including multiplicative factors and growth rates for intensities.

(CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 21 of 34 General Demand Input Variables – XCool Intensity Econ + Regression Structural Cooling CoolUse Coefficient Scaling Total Variable Factor Xcool 2026 363,06...

AI summary The text presents demand input variables for XCool and XOther, including intensity factors, scaling factors, and load forecasts for 2026 and 2036. It outlines the calculation methodology for XCool and XOther based on various demand components and coefficients.

Prob (Jarque-Bera) 0.3238 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 27 of 34 Medium Industrial Model Fit REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 28 o...

AI summary The text presents a statistical model used for forecasting load demand, specifically focusing on the Medium Industrial Model Fit and a Combined Model for Commercial and Industrial DSM Coefficient. It includes variables, coefficients, standard errors, t-statistics, and p-values associated with the model, as well as binary variables added to address billing issues and improve model fit.

s with the billing. Binaries were also added for February 2018 and for the years 2021- 2024 to improve model fit. Variable Coefficient StdErr T-Stat P-Value MSales.EESavingsProfiled -0.539 0.170 -3.169 0.20% MStructGen.WtXCool 0.457 0.046 9...

AI summary The text presents a statistical model analyzing variables affecting energy sales, including DSM savings, structural generation weights, and binary variables for specific time periods. The coefficients and their significance levels are provided, indicating the impact of each variable on historical sales trends.

herm and GSOtherm are the non-weather dependent portion of the sales model (including embedded DSM). For example, the energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coeffici...

AI summary The document explains how the non-weather dependent portion of the sales model, including embedded DSM, is calculated using regression coefficients. It also describes the normalization of load requirements on an average MW basis and introduces variables like PkWindVarm, which represents the average daily windspeed on monthly peak days.

0.0308 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix B Page 34 of 34 Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. A...

AI summary The document discusses the peak model fit in the 2026 Load Forecast Report, noting that the model aligns well with historical data. It explains that while an explicit peak DSM variable could not be included due to insignificant parameters, the indirect effects of energy-related DSM from historical data are carried over into the peak model.

2256 2291 2024 2408 Actual System Peak: 2,111 2,018 2,073 2,060 2,050 1,968 2,216 2,455 2,088 2,267 Percent Error 2015 -3.8% 0.9% -1.8% -1.6% -1.7% 1.5% -10.4% -19.6% -4.6% -12.7% 2016 5.6% 4.0% 5.3% 6.3% 11.2% -0.8% -10.3% 5.6% -2.6% 2017...

AI summary The text presents a table with actual system peak values and percent errors from 2015 to 2024, illustrating variations in forecasting accuracy over time. The data shows fluctuations in percent error, indicating the challenges in predicting system peak demand.

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 to model variability in load forecasting, incorporating historical data and the impact of heat pumps. It outlines the production of 10,000 forecast points and the use of Normal distributions to derive probabilistic forecasts and sensitivity diagrams.

. From these annual forecast distributions, the various probabilistic forecasts can be obtained as well as sensitivity diagrams that show the relative impact of the variables in each year. In Figure D4 the probabilities of system peak fore...

AI summary The text discusses probabilistic forecasts and sensitivity diagrams for system peak demand, highlighting the impact of variables on peak demand forecasts. It explains the asymmetry in peak forecast distributions due to the use of the MAX function on monthly heating degree day data.

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.

drogen facilities could all have a significant impact on energy and EVs, hydrogen facilities and batteries could have a significant impact on peak. Figure D8 shows the relative impact of these items. Figure D8: Relative Impact of Inputs 20...

AI summary The text discusses the potential impact of various energy-related factors, including demand-side management (DSM), solar PV, electric vehicles (EVs), hydrogen production, and battery storage, on energy and peak demand in 2026 and 2036. It highlights the relative contributions of these factors to energy and peak demand under different scenarios.

Year d forecast forecast +364 2035 -875 -511 (42%) 8 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix E Page 9 of 21 Residential hybrid heating (1) • NS Power modelled the system impact of potential hybrid h...

AI summary The document discusses NS Power's modeling of residential hybrid heating programs using AMI data, considering various trigger scenarios and participation rates. The 2026 Load Forecast retains E3's peak-mitigation estimate but adjusts the energy reduction estimate to the median modelled outcome of -85 GWh by 2036.

Using updated peak model 2025 Load Forecast, with from this Forecast (MW) updated input data (MW) 2026 Forecasted Peak 2,442 2,487 Interruptible -42 -42 Weather (-14.9oC 12hr lag avg, +38 +38 -15.1oC 24hr lag avg) Wind (14.1 km/h daily avg...

AI summary The 2026 Load Forecast Report updates the 2025 forecast using new input data, including recent installations of heat pumps, EVs, and solar, as well as policy and incentive changes. A hybrid impact model based on AMI data was incorporated, and heat pump water heaters were introduced as a separate end-use category in the residential model.

ons incorporated into 2026 Load Forecast. Heat Pump Water Heaters Introduced as a separate End-Use in Residential model. RTR Delayed migration, changed class distribution. Peak Model Updated based on analysis of 2026 system peak (25th Janu...

AI summary The text discusses updates to the 2026 Load Forecast, including the incorporation of heat pump water heaters as a new end-use category in the residential model, changes to the RTR migration, and an updated peak model based on the 2026 system peak analysis. These updates are part of the 2026 Load Forecast Report, with attachments filed electronically.

Muni Forecasts Apr-09 to Mar-10 Domestic Commercial Industrial Losses Total 74.6 97.2 20.6 7.7 200.10 GWh 37.3% 48.6% 10.3% 3.8% 100% old1 37.3% 48.6% 10.3% 3.8% 100% old2 38.5% 47.5% 8.0% 6.0% 100% TOTAL Total NS SECTOR TOTALS: MUNICIPAL...

AI summary The text presents electricity consumption forecasts for the period April 2009 to March 2010, including domestic, commercial, industrial, and losses data. It provides percentages and total consumption in gigawatt-hours (GWh) for different sectors and includes historical comparisons.

Losses + Change Summary without YEAR INDUSTRIAL Municipal BUTU EBS Total Sales in Unbilled Requirement Loads Domestic Commercial Industrial Losses Total Growth PTP Forecast OATT including Muni Domestic Commercial Industrial Losses ΔGWh bef...

AI summary The table presents data on energy sales, losses, and related metrics across multiple years, including industrial, municipal, and total sales figures, along with unbilled requirements and growth rates. The data spans from 1991 to 1996 and includes metrics such as domestic, commercial, and industrial loads, losses, and total growth.

,966 1,354 13,296 3,052 29,961 553,222 10,354.23 14,600 4,100 2033 509,966 1,219 14,514 2,747 32,708 557,188 10,310.27 14,600 4,100 2034 509,966 1,097 15,612 2,472 35,180 560,757 10,316.49 14,600 4,100 2035 509,966 987 16,599 2,225 37,405...

AI summary The text contains a table with numerical data and a reference to a redacted 2026 Load Forecast Report. It mentions structural changes, new home forecasts, and the impact of programs like RTR and DSM, but no specific arguments or entities are discussed.

Total EV Incremental EV ENERGY load Load Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 2016 2017 0 2018 0 2019 0 2020 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 2021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 2022 0.0...

AI summary The document presents a table showing the total and incremental energy load for electric vehicles (EVs) from 2016 to 2027. The data indicates that EV load starts to increase significantly in 2026, with a total of 8.0 units, and continues to grow in 2027, reaching 19.3 units. The table provides monthly breakdowns of the load for each year.

Cumulative Incremental PEAK Peak Peak Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 2016 2017 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2019 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0...

AI summary The text presents a table showing cumulative and incremental peak demand values for each month from 2016 to 2028. The table is largely empty except for the years 2026 and 2027, where specific numerical values are provided, indicating changes in peak demand over time.

Forecast no No DSM Previous Year Actuals DSM Launch with Launch Small General BCurrent For Forecast 2001 2002 104,447.37 2003 109,056.34 109.06 109.06 2004 164,678.82 164.68 164.68 2005 232,201.41 232.20 232.20 2006 232,600.73 232.60 232.6...

AI summary The text presents a table showing electricity usage forecasts and actuals from 2001 to 2021, comparing scenarios with and without demand-side management (DSM). The table highlights the impact of DSM on energy consumption over time, with specific figures for each year.

General Demand No DSM Before DSM Forecast no with with Year Actuals DSM Launch Launch Growth Current Forecast Previous Forecast 2001 2002 2,349,606.41 2,349.6 2003 2,417,229.17 2,417.2 2004 2,426,350.54 2,426.4 2005 2,381,272.38 2,381.3 2,...

AI summary The text presents a table comparing actual demand, forecast demand with and without DSM (Demand Side Management), and growth figures from 2001 to 2020. The data shows fluctuations in demand over time and the impact of DSM on demand forecasting.

Small Industrial No DSM Before DSM with with Current Previous Year Actuals Forecast Launch Launch Growth Forecast Forecast 2001 2002 2003 2004 2005 241,108.65 241.11 241.11 2006 239,922.38 239.92 239.92 2007 248,104.98 248.10 248.10 2008 2...

AI summary The text presents a table with historical and forecasted data for industrial energy usage, comparing scenarios with and without demand-side management (DSM) initiatives. The data spans from 2001 to 2020, showing actuals, forecasts, and growth metrics.

Medium Industrial No DSM Before DSM with with Current Year Actuals Forecast Launch Launch Growth Forecast Previous Forecast 2001 2002 2003 2004 2005 557,069.84 557.07 2006 567,300.33 567.30 2007 567,981.14 567.98 567.98 2008 539,433.16 539...

AI summary This table provides a historical comparison of industrial electricity usage, including actuals, forecasts, and growth metrics from 2001 to 2025. It outlines data before and after the launch of DSM (Demand Side Management) initiatives, highlighting changes in consumption trends over time.

Year DLC CPP BNI Curtailm Grand Total with ELCCJan Feb Dec Estimated ELCC 2021 0.0 0.0 0.0 0.0 0.48 2022 0.0 1.0 0.0 0.5 0.5 0.5 0.5 2023 0.2 1.0 2.8 1.9 1.9 1.9 1.9 2024 2.9 2.0 7.1 5.8 5.8 5.8 5.8 2025 7.1 3.2 10.7 10.1 10.1 10.1 10.1 20...

AI summary The text presents a table with data spanning from 2021 to 2036, showing values for DLC, CPP, BNI Curtailm, and Estimated ELCC. The data reflects changes over time, with increasing values in most years. The table is partially redacted, indicating some information may be confidential.

2036 38.0 34 6 37 37.3 37.3 37.3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2026 Load Forecast Report Attachment 4 Page 26 of 80 Year 2036 PHP 2050 DSMYear 2026 Forecast With Out DSM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De...

AI summary The text presents a load forecast report with data for the year 2036, including various categories such as residential, ETS, Res CPP, and Res TOU, with monthly and total values provided. The data is part of an attachment from a 2026 Load Forecast Report.

REDACTED 2026 Load Forecast Report Attachment 4 Page 27 of 80 Forecast With DSM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Residential 563.7 510.2 475.9 352.5 280.0 228.7 257.2 248.5 225.9 279.2 359.2 519.8 4,301 ETS 49.6 46.9 4...

AI summary This table presents a forecast of residential load demand for 2026, including various programs such as ETS, Res CPP, and Res TOU, with monthly and total figures provided. The data includes the forecast with demand-side management (DSM) and highlights the total residential load demand for the year.

- - 85,143 85,143 76,255 Jun-30 2030 81,540 - - - 36,455 12,160 - 57,245 - - 164,206 164,206 221,451 Jul-30 2030 509,256 - - - 1,343 285,000 - 792,913 - - 775,341 775,341 1,568,254 Aug-30 2030 3,510 - - - 3,683,260 - 3,686,770 - - - - 3,68...

AI summary The document contains a table with numerical data and a redacted section from a 2026 Load Forecast Report, which includes a reference to a DSM Demand Year of 2026. The data appears to be related to financial figures and dates, but the content is partially redacted.

REDACTED 2026 Load Forecast Report Attachment 4 Page 64 of 80 Year 2036 PHP 2050 DSM DemandYear 2026 31 28 31 30 31 30 31 31 30 31 30 31 Forecast With DSM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Peak Residential 1036.2 1143.6 1081....

AI summary This table presents a load forecast for the year 2036, including forecasted demand for residential, small general, general demand, and large general sectors across different months, with peak demand values provided. The data includes DSM (Demand Side Management) demand and ETS (Electric Transmission Service) demand for the year 2026.

ION REMOVED) REDACTED 2026 Load Forecast Report Attachment 4 Page 65 of 80 Forecast With DSM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Peak Residential 994.6 1096.9 1036.4 755.4 521.7 525.7 481.9 530.3 458.4 611.9 719.0 1060.3 1,097...

AI summary The document presents a forecast of load demand with Demand Side Management (DSM) for residential, ETS, and commercial sectors across months and peak demand. It includes detailed load data for each category and overall totals.

1598.2 2263.7 2,307 System Peak 2183.4 2453.5 2376.3 1792.3 1459.4 1525.6 1466.3 1482.0 1402.4 1673.1 1814.0 2394.0 2,454 Peak End Use before DSM Accrued 2447.8 2352.8 2035.9 1569.9 1283.9 1200.5 1326.5 1390.8 1212.3 1287.2 1746.9 2125.0 2...

AI summary The text presents data on system peak and peak end use before DSM, including figures for various categories such as solar, EV, C&I growth, and others. It provides numerical values across different years and months, indicating trends and changes in energy usage.

85.7 71.1 80 GR & LF 0.2 1.7 0.4 -0.3 0.2 1.0 1.0 5.0 5.0 5.1 0.6 2.6 0 Municipal 43.0 51.5 47.5 29.0 25.0 20.1 22.5 21.4 22.6 29.0 32.8 40.5 43 System Peak 2886.4 2806.3 2529.1 2053.3 1643.4 1490.0 1626.7 1661.6 1548.3 1734.3 2212.2 2532....

AI summary The text presents a table with numerical data related to system peak, coincident interruptible, and firm peak values before and after Demand Side Management (DSM) interventions. It includes values for different years and highlights reductions in demand due to DSM efforts, particularly in the DR Reduction and Adjusted Firm Peak categories.

Engineering model profile based 2023 - 2025 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Residential 0.124 0.115 0.109 0.086 0.071 0.058 0.064 0.061 0.054 0.063 0.079 0.115 1.000 ETS 0.138 0.131 0.125 0.086 0.056 0.072 0.028 0.055...

AI summary The document presents an engineering model profile with monthly and total figures for various customer classes from 2023 to 2025, including residential, ETS, and industrial segments, providing a breakdown of usage distribution across different months and categories.

opUp 0.069 0.066 0.038 0.072 0.084 0.096 0.113 0.143 0.115 0.073 0.053 0.077 1.000 Loss Profile Transmission Loss pattern 12.9% 12.4% 11.6% 8.0% 6.7% 5.9% 6.2% 5.4% 5.2% 6.2% 9.2% 10.5% 100.0% Distribution Loss pattern 11.2% 10.0% 10.4% 8....

AI summary The text presents data on transmission and distribution loss patterns over time, as well as DSM allocations across different customer classes, highlighting the distribution of energy and demand-side management initiatives among residential, commercial, and industrial sectors.

0.0% 0.0% Municipal - Res 0.4% 0.4% Municipal - Com 0.6% 0.6% Municipal - Ind 0.6% 0.8% Unmetered 100.0% 100.0% DSM Adjustment to account for DSM already captured in forecast Energy Demand Residential 53% 53.3% ETS 53% 53.3% Small General...

AI summary The text presents a table showing the distribution of energy and demand across various sectors, with percentages indicating the DSM Adjustment to account for DSM already captured in the forecast. The table includes data for residential, commercial, industrial, and other sectors, with varying percentages for energy and demand.

Load Factors Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Residential from 2024 LS 76.83% 69.75% 62.13% 65.15% 71.96% 60.14% 71.24% 62.83% 67.95% 61.23% 69.55% 66.36% ETS from 2024 LS 124.59% 101.81% 87.14% 105.08% 76.60% 79.98% 84.69%...

AI summary The text presents load factor data for various customer segments in Nova Scotia from January to December 2024, indicating fluctuations in energy usage across residential, industrial, and other categories.

2952.5 2339.8 5,000 Historic Sales Previous Forecast Current Forecast 2021 10902.2 1.7% 10902.2 201620172018201920202021202220232024202520262027202820292030203120322033203420352036 2973.5 2480.3 2022 11133.9 2.1% 11133.9 3088.3 2480.2 Hist...

AI summary The text presents numerical data including sales figures, percentages, and forecasts spanning multiple years, with references to terms such as 'Historic NSR' and 'Current Forecast'. These figures appear to be related to energy sales and demand projections over time.

2100 1.1% 2.2% 1.0% W 1900 1700 1500 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 Historic Peak Previous Forecast Current Forecast 2026 actual REDACTED (CONFIDENTIAL INFORMATION R...

AI summary The document presents load forecast data, including historical peak values, previous and current forecasts, and a 2026 actual load value. The forecast spans from 2016 to 2036, with percentages and numerical values indicating load trends over time.

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.

2100 Peak Calcs W Modeled Peak (MWRes Peak (MW)EV (MW) DR (MW) Hybrid ModeC&I Growth Large Cust. DSM (MW) Firm Peak (MInter. Cust. System Peak (MW) Check 1900 2026 2,261 2 3 - 8 - 0 99 - 12 2346 130 2,484 2484 2027 2,274 4 7 - 11 - 1 104 -...

AI summary The text presents a table with modeled peak demand values for different years, including factors such as residential peak, electric vehicle demand, demand response, and system peak demand. The data outlines projected growth and demand management strategies for the years 2026 to 2030.

rm Peak Previous Forecast 2033 2,390 11 84 - 36 - 4 4 109 - 87 2471 145 2,652 2652 2034 2,411 12 110 - 37 - 4 4 110 - 99 2507 145 2,689 2689 Current Forecast 2026 actual 2035 2,433 13 141 - 37 - 5 4 110 - 111 2548 145 2,730 2730 2036 2,448...

AI summary The text presents a comparative analysis of peak demand forecasts for various years, highlighting differences between previous and current forecasts, along with variance calculations. It includes data on residential, commercial, industrial, and other demand categories, as well as losses and NSR (Net System Requirement) values for 2025.

5 Large Customer Actuals - 3 - 153 - 156 Other Variance - 41 23 - 5 1 - 14 - 36 2025 Actuals 5,292 3,161 2,098 150 768 11,469 -1% 1% 0% 1% -2% Res Comm Ind Other Losses NSR 2026 Forecast 5315 3165 2183 131 781 11575 2036 Forecast 5646 3213...

AI summary The text presents a comparison of actuals and forecasts for different customer categories and years, including metrics such as Res, Comm, Ind, Other, Losses, and NSR. It highlights variations and trends in the data across the years 2025 and 2036.

5315 3165 2183 131 781 11575 2036 Forecast 5646 3213 2168 259 810 12097 Model -2 275 27 2 78 381 New Customers 350 39 389 Solar -370 -171 -540 EV 645 373 1018 C&I Growth 14 20 34 Large Customer Projects 33 186 219 Hybrid model adjustm -16...

AI summary The text presents numerical data related to forecasted and modeled values for various categories, including energy usage, customer growth, solar and electric vehicle (EV) impacts, and demand-side management (DSM) metrics. The figures indicate changes and adjustments across different models and scenarios.

-406 -387 -147 -5 -74 -1018 DSM in models -179 -189 -17 -2 -25 -411

AI summary The text presents numerical data related to Demand Side Management (DSM) in models, with various figures listed across different categories. The numbers indicate potential costs or impacts associated with DSM initiatives.

DSM Res Comm Ind Captured by Re Captured by Adjustment Adjustment Com/Ind 2026 40.0 64.6 11.4 18.7 35.0 21.3 41.0 2027 26.1 55.8 38.8 12.2 43.6 13.9 51.0 2028 27.2 43.7 29.1 12.7 33.6 14.5 39.2 2029 27.1 28.9 21.1 12.7 23.1 14.4 27.0 2030...

AI summary The text presents a table with DSM-related data, including demand-side management (DSM) figures for residential, commercial, and industrial sectors from 2026 to 2036. The data includes metrics like captured by, adjustment, and other related parameters, but the content is partially redacted.

Sector Forecast Without DSM Sector Forecast With DSM Year Residential Commercial Industrial Other Losses Total Year Residential Commercial Industrial Other Losses Total 2003 3,940 3,003 4,031 184 851 12,009 2003 3,940 3,003 4,031 184 851 1...

AI summary The text presents sector electricity demand forecasts for Nova Scotia from 2003 to 2009, comparing scenarios with and without Demand Side Management (DSM). The data shows fluctuations in residential, commercial, industrial, and other sectors, with losses and total demand for each year. The percentages indicate growth or decline in demand for each sector.

MOVED) REDACTED 2026 Load Forecast Report Attachment 4 Page 77 of 80 Firm Peak End Use with DSM and DR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Peak 2026 2346.2 2262.8 1949.1 1534.6 1268.1 1197.0 1276.9 1312.5 1203.0 1295.8 1703.1 2...

AI summary The document presents a load forecast report with data on firm peak end use for the years 2026 to 2036, including demand-side management (DSM) and demand response (DR) considerations. It also includes a section with redacted information and a list of items, such as 'Yes 2000 Residential' and 'No 2001 ETS'.

1 Request IR-1: 26 heating systems for the coldest periods) and provincial policy on electrification and 27 decarbonization? Please explain your answer. 28 29 (h) Does NS Power agree that the forecast energy use reduction from its resident...

AI summary The document contains a request and response regarding NS Power's hybrid heating program, including the use of the Monte Carlo tool for sensitivity analysis and the impact of non-electric backup heating systems during cold periods. The response details three hybrid event scenarios and participation assumptions.

Methods - Heat use set to zero for hybrid events, which occur according to 3 scenarios: - 1. Top 88 NSR hours - 2. CPP events - 3. Hours when T < -10 o C - Hybrid impact = hybrid scenario load shape minus status quo.

AI summary The document outlines methods for analyzing hybrid events, where heat use is set to zero under specific scenarios, including top NSR hours, CPP events, and temperatures below -10°C. The hybrid impact is calculated as the difference between the hybrid scenario load shape and the status quo.

Results - Peak impact = ~100 200 MW reduction by 2035, depending on adoption. No impact for Scenario 2 (CPP-based). - This is because there was no CPP event at the time of system peak in 2022. - Based on results from Scenarios 1 & 3, impac...

AI summary The peak impact is estimated at a reduction of approximately 100 to 200 MW by 2035, depending on adoption, with no impact for Scenario 2 due to the absence of a CPP event during the 2022 system peak. The impact on NSR at peak in 2035 is estimated at ~5% or 10% based on Scenarios 1 & 3.

Request IR-3: Reference: Exhibit N-1, Section 10.4. (a) Please provide tables summarizing NS Power's forecasts for demand reduction and actual demand reduction due to Direct Load Control (DLC), Time Variable Pricing (TVP) and business, non...

AI summary The request asks NS Power to provide tables summarizing forecasts and actual demand reductions from Direct Load Control, Time Variable Pricing, and BNI Curtailment, including year-to-date values for 2026.

Load Forecast Report: 2021 2022 2023 2024 2025 Actual Demand Reduction Year 2021 2022 2023 2024 2025 2026 2027 n/a 2028 n/a 2029 n/a 2030 n/a 2031 n/a 2032 n/a 2033 n/a 2034 n/a 2035 n/a 1 (b) Please provide a brief explanation of the basi...

AI summary The text includes a table of load forecasts from 2021 to 2035 and a series of questions related to demand reduction, AMI technology, time-varying pricing (TVP), and peak demand events. The questions ask for explanations, confirmations, and details on the expected impact of these initiatives.

Business, Non-Profit and Institutional (BNI) Curtailment Load Forecast 2021 2022 2023 2024 2025 Actual Demand Report: Reduction (BNI) Year 2021 0 0.0 2022 1 0 0.0 2023 2 1 1 2.4 2024 4 2 2 0 8.0 2025 6 4 4 1 1 5.9 2026 7 6 6 2 2 13.0 2027...

AI summary The document presents a table showing load forecasts and actual demand reduction values for Business, Non-Profit and Institutional (BNI) curtailment from 2021 to 2035. The data is sourced from EfficiencyOne's 2025 DSM Programs Evaluation Reports and Q1 2026 Demand Side Management Report, with a note that 2026 values are tracked but not yet evaluated.

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: 2 3 Reference: NS Power 2026 Load Forecast Report, page 40, lines 3-11 4 5 Based on the 2026 Load Forecast report, E1 understands that the hybrid heating 6 participation rate is based on a 50 percent adopti...

AI summary The text requests clarification and explanation from NS Power regarding the assumptions and methodology used in the 2026 Load Forecast Report, particularly concerning hybrid heating participation rates, energy and peak demand reductions, and the use of AMI data for revised estimates.

(d) NS Power did not consider using per-participant energy and demand impacts from the AMI analysis and applying them to the revised participation estimate to estimate the total energy reduction and peak demand reduction for the purposes o...

AI summary NS Power did not use per-participant energy and demand impacts from the AMI analysis to estimate total energy and peak demand reductions in the 2026 Load Forecast. No hybrid heating program is currently proposed, and the forecast uses values representative of a range of scenarios. Hybrid heating is considered a demand side management activity when participants are incentivized.

1 Request IR-2: 2 3 E1 has used data from the Load Forecast (total peak reduction, total energy reduction, and 4 cumulative participants) to calculate full load hours, unitary demand reduction, and unitary 5 energy reduction in Table 1. 6...

AI summary E1 has used data from the Load Forecast, including total peak reduction, total energy reduction, and cumulative participants, to calculate full load hours, unitary demand reduction, and unitary energy reduction as presented in Table 1.

8 Reduction for Modelled Hybrid Heating in the 2026 Load Forecast. Column: A B C D E F Year Total Peak Reduction (MW) Total Energy Reduction (GWh) Hybrid HP Cumulative Participants Calculated Full Load Hours Unitary demand reduction (kW) U...

AI summary The chunk presents a table outlining projected reductions in peak load and energy usage due to hybrid heating systems from 2028 to 2036, including cumulative participants, calculated load hours, and unitary demand and energy reductions.

(a) Please explain why the modelled hybrid heating program has an average full-load hours reduction in electric space heating of 1,656, and whether this is consistent with the program's intended design? If the intended program design is no...

AI summary The text requests an explanation of the hybrid heating program's modelled average full-load hours reduction in electric space heating and asks about the program's intended design. It also inquires about the expected annual operating hours of hybrid heating systems and the assumptions used in load forecast modelling.

Response IR-2: (a) The "full load hours" metric is not used by NS Power in the hybrid heating modelling or the load forecast. The peak reduction is not achieved every hour of a modelled hybrid event; this is just the reduction at hour of s...

AI summary NS Power clarifies that 'full load hours' are not used in hybrid heating models or load forecasts. Peak reduction is only at the system peak hour, and demand reduction varies by heating intensity. The forecast assumes hybrid heating systems are operational when needed, with no single assumption set reflected in the load forecast. No existing hybrid heating program exists in Nova Scotia.

(c) Please refer to CA IR-1 part (f). Hybrid event trigger scenario Event hours 11 Department of Energy-funded hybrid heating working group led by Net Zero Atlantic (NZA) and 12 considered in the NZA Hybrid Heating Study. It is NS Power's...

AI summary The text discusses differences between the hybrid heating scenarios modeled in the Report and those in the NZA Hybrid Heating Study, including variations in participation rates and assumptions based on load forecasts. It also requests a detailed table of modelled scenarios with specific metrics and assumptions.

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.

Request IR-4: - Page 40 explains that NS Power's consultant E3, estimated a reduction of 200 GWh under a - hybrid scenario, but NS Power does not consider it to be attainable. Please elaborate on the - results of the modeling leading NS Po...

AI summary NS Power's modeling, based on actual heating load profiles from 20,000 customers, estimates a maximum attainable energy reduction of -83 GWh under a hybrid heating program by 2035, revising a previous estimate of -200 GWh due to more realistic assumptions.

participation assumptions. 1 Request IR-5: 2 3 Page 44 provides statistics on battery electric vehicles and plug-in hybrid electric vehicles in 4 Nova Scotia in 2025. Please provide the source of these data. 5 6 (a) How do these statistics...

AI summary The document discusses the source of statistics on battery electric vehicles and plug-in hybrids in Nova Scotia in 2025, noting that NS Power uses data from Statistics Canada and S&P Global Mobility instead of the Province's Electric Vehicle Registration Data due to concerns about accuracy and misclassification.

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: 1 (c) Condition (2) of the Critical Peak Event Proced...

AI summary The document discusses the factors not considered in the adjusted forecast and references Synapse IR-5 for housing estimates. It outlines the Critical Peak Pricing (CPP) procedure, including conditions for scheduling CPP events and criteria used by NSPI to make decisions.

- 3 undertaken. 1 Request IR-2: 28 remain location-specific and incremental infrastructure investment will be required as areas 29 become constrained. 1 Request IR-3: 2 3 Refer to M12861, Exhibit N-1, the Report, the 2027–2031 DSM Plan, an...

AI summary The document discusses the need for location-specific and incremental infrastructure investment as areas become constrained. It references the 2027–2031 DSM Plan and requests analyses and data to enable strategic electrification initiatives. NS Power provided data on the relative value of strategic electrification compared to energy efficiency.

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.

1 transportation. Accordingly, NS Power's view is that the avoided cost series of DSM is not 2 fully representative of the value SE brings to the system or where it drives costs. 3 4 As an initial step to support the evaluation of the cost...

AI summary NS Power argues that the avoided cost series of demand-side management (DSM) does not fully represent the value of strategic electrification (SE) or where it drives costs. They suggest developing a new cost series to reflect increased grid costs from additional load and propose collaboration with E1 to develop avoided cost calculations for DSM activities.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 1 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Average of large customer co 2013 6 92.0 5.0 2013 7 6.3 75.7 2013 8 14.2 16.4 2013 9 88.3 4.5 2013 10 228.0 0.0 2013 11...

AI summary The document presents a redacted load forecast report with data on average large customer consumption and heating/cooling degree days (HDD18/CDD18) from 2013 to 2018. It includes monthly values for energy use and climatic factors, but key details are redacted.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 12 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Year Cust Count Change in Res Customers Population Change in Population Housing Completions Starts Household Size Year...

AI summary The document presents a table with data on customer counts, population changes, housing completions, and household sizes from 2021 to 2023. It includes monthly breakdowns of household size for each year, indicating trends in demographic and housing data over time.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 34 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 2024 2025 billed 2025 accrued 2026 Residential WN (kWh) 21,157,507 33,365,662 -29,778,051 -5,732,680 -10,861,808 1...

AI summary The document presents a load forecast report for residential energy consumption from 2023 to 2036, including billed and accrued values, monthly breakdowns, and the impact of various factors such as solar, EVs, and DSM programs on total sales and energy usage.

Year Heat Misc Light Vent Refrig Office EWHeat Cooking EV Total 2026 Commercial End Use Peak Shares 2026 226.43 96.65 132.74 52.58 24.77 18.42 4.00 12.61 1.30 569.5 2035 Commercial End Use Peak Shares 2035 282.43 103.61 110.22 51.80 23.81...

AI summary The document presents a table showing the distribution of commercial end use peak shares for the years 2026 and 2035, with percentages and values for different categories such as Heat, Light, Vent, Refrig, and others.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 196 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Item 2026 Energy (GWh) 2026 Peak (MW) 2036 Energy (GWh) 2036 Peak (MW) Included in Forecast DSM (base case) -116 -19 -...

AI summary The document presents a 2026 Load Forecast Report, highlighting the impact of various factors on energy demand and peak load, including demand-side management, solar PV, electric vehicles, hydrogen production, battery storage, and price elasticity. The report includes multiple scenarios and confidence intervals for the years 2026 and 2036.

6 1 As can be seen, models perform very well for Residential class, but not for General Demand, even in class-tailored form. Restricting the training period to more recent years lead to minor model improvements, but using winter months onl...

AI summary The models perform well for residential class but poorly for General Demand, even with tailored training. Restricting training to recent years slightly improved models, but using only winter months did not. Poor performance of non-residential class models limited their use in estimating coincident peak demand.

2026 Load Forecast Report (NSEB M12861) NSPI Responses to Synapse Energy Economics Inc. Information Requests 1 Request IR-5: 1 the P10/P90 sensitivity provided in Section 11 of the Report. This represents 20 years of 2 economic fluctuation...

AI summary The 2026 Load Forecast Report (NSEB M12861) addresses Synapse Energy Economics Inc.'s information requests regarding sensitivity analysis, forecast accuracy, and assumptions about new technologies. It discusses uncertainties from trade tariffs, differences between forecasted and actual residential customer additions, and the assumption of distributed solar-plus-storage or storage-only deployments.

NON-CONFIDENTIAL 1 and collect heat pump and total house hourly load data, and measure heat-pump 17 heat pumps that replace electric heat sources and heat pumps that replace non 18 electric heat sources. 1 Request IR-8: 2 3 4 Residential H...

AI summary The document requests detailed documentation and analysis related to the residential hybrid heating program, including workpapers, reports, and cost impact assessments conducted by NS Power in collaboration with the Department of Energy and other stakeholders.

NON-CONFIDENTIAL 1 (d) Currently there is no federal EV mandate in Canada following the cancellation of the EV 2 Availability Standard in February 2026. The shift away from this policy toward tighter 3 greenhouse gas emissions (GHG) emissi...

AI summary The text discusses the absence of a federal EV mandate in Canada, noting the cancellation of the EV 2 Availability Standard in 2026 and the shift toward stricter GHG emissions standards. It mentions the 2026 Load Forecast, which assumes 55% ZEV sales as BEVs currently and projects 75% ZEV sales by 2035 with 80% being BEVs. An error in Figure 29 is highlighted, which incorrectly attributes LDV energy and peak values only to BEVs.

23 Vehicle Type Avg kWh/year Avg kW/vehicle on Peak BEV 4,202 0.6 PHEV 2,101 0.6 MDV 8,205 1.6 HDV 113,890 7.3 24

AI summary The text provides data on average annual electricity consumption (kWh/year) and peak power demand (kW/vehicle) for different vehicle types, including Battery Electric Vehicles (BEV), Plug-in Hybrid Electric Vehicles (PHEV), Medium Duty Vehicles (MDV), and Heavy Duty Vehicles (HDV).

II. Introduction and Summary - Q: Please summarize the topics you will address in your review of the 2026 Load Forecast Report. - A: My evidence reviews several technical or program-based adjustments to the base load forecast that I find d...

AI summary The review of the 2026 Load Forecast Report identifies several issues with technical and program-based adjustments, particularly the residential heating intensity forecast and forecasts for demand reduction programs. The reviewer recommends immediate corrections and improvements to forecasting practices, including revising forecast workbooks, rejecting the current hybrid heating forecast, and adjusting DLC, TVP, and BNI curtailment forecasts.

Q: What are your findings? A: NS Power has embedded a number of unexplained and unsupported assumptions in its residential load forecast. In addition to the hard-coded factors I mentioned above, there are unexplained factors in several oth...

AI summary The reviewer found that NS Power's residential load forecast contains unexplained and unsupported assumptions, with unclear sources for baseline data and outdated Itron-supplied EIA data. These issues were identified in multiple worksheets, and similar gaps may exist in other workbooks.

Q: Is NS Power's residential hybrid heating forecast reasonable? A: No. The energy-to-demand reduction ratio is far too high. NS Power forecasts that the maximum demand reduction per customer is 2.76 kW during the peak hour and, depending...

AI summary The residential hybrid heating forecast by NS Power is deemed unreasonable due to an overly high energy-to-demand reduction ratio. The forecast assumes a maximum demand reduction of 2.76 kW per customer and significant annual energy savings, but it is criticized for misinterpreting data from a 2022 Itron study and not accurately representing hybrid heating scenarios.

1 Table 3: Comparison of per Customer Hybrid Heating Impact Estimates Itron Study20 NS Power -10°C Hybrid Heating Model Forecast of Hybrid Heating Impacts22 Peak Reduction 1.5 kW 2.76 kW 2.76 kW 1.75 kW Energy Savings 4,407 kWh 7,294 kWh 5...

AI summary Table 3 compares per customer hybrid heating impact estimates from different studies and models, highlighting variations in peak reduction, energy savings, load factor, and minimum hours of operation across different scenarios.

V. Demand Reduction Forecasts - Q: Please summarize the demand reduction forecasts developed by NS Power. - A: NS Power forecasts demand reduction due to Direct Load Control (DLC), Time Variable Pricing (TVP) and business, non-profit and i...

AI summary NS Power forecasts demand reduction from DLC, TVP, and BNI programs, based on the 2022 Evergreen IRP and updated DSM plans. However, the DLC forecast is criticized for overestimating actual reductions over the past four years, with a recommendation to reduce the 2032 DLC forecast to 4 MW until evidence supports a higher forecast.

1 Table 4: Direct Load Control (DLC) Forecasts from 2021-2026 Reports (MW) Actual 2021 2022 2023 2024 2025 2026 Demand Reduction 2021 0 0.0 2022 4 0 0.0 2023 12 4 4 0.1 2024 24 12 12 0 0.1 2025 36 24 24 4 4 0.9 2026 39 36 36 12 12 5 2.7 20...

AI summary Table 4 presents forecasts for Direct Load Control (DLC) demand reduction from 2021 to 2036, showing increasing DLC capacity over time with some fluctuations in later years.

A: No, but in this case the forecast impacts are too low. As shown in [Table 6,](#page-17-0) NS Power has under-forecast actual BNI curtailment impacts. While the forecasts for the years 2026-2031 are reasonable, there is no evidence or pr...

AI summary The text discusses the under-forecasting of BNI curtailment impacts by NS Power and recommends increasing the forecast to 26 MW starting in 2032, with no evidence for further increases beyond that date.

1 Table 6: Business, Non-Profit and Industrial (BNI) Curtailment Program Forecasts from 2021- 2 2026 Reports (MW) Actual 2021 2022 2023 2024 2025 2026 Demand Reduction 2021 0 0.0 2022 1 0 0.0 2023 2 1 1 2.4 2024 4 2 2 0 8.0 2025 6 4 4 1 1...

AI summary Table 6 presents forecasts for the BNI Curtailment Program from 2021 to 2036, showing demand reduction in megawatts. The data outlines the expected participation and reduction levels over time. Section VI discusses the damping of heating and cooling trends.

Integrated Resource Plan," prepared for the Nova Scotia Consumer Advocate, NSUARB Matter No. M08059, with Paul Chernick, January 2021. - "Implementing All-Source Procurement in the Carolinas," prepared for Natural Resources Defense Council...

AI summary The text lists various reports and studies related to energy planning, procurement, and grid management, prepared for different organizations and regulatory bodies across North America. These reports include integrated resource plans, pricing formulas, interconnection scorecards, and analyses of energy demand and generation.

- "Making the Most of the Power Plant Market: Best Practices for All-Source Electric Generation Procurement," Indiana State Bar Association, Utility Law Section, Virtual Fall Seminar, September 2020. - "Resource Adequacy, Reserve Margin, &...

AI summary The document lists various presentations and seminars related to energy and utility topics, including power plant market practices, resource adequacy, real-time pricing, load forecasting, and energy transition challenges. These events were hosted by organizations such as the Indiana State Bar Association, The Energy Authority, and NERC.

hern Alliance for Clean Energy. Adequacy of consideration of energy efficiency in Georgia Power's 2010 integrated resource plan, including cost effectiveness, rate and bill impacts, and lost revenues. Georgia PSC Docket No. 31082, direct t...

AI summary The text outlines expert testimony provided by the Southern Alliance for Clean Energy in various regulatory proceedings, focusing on the adequacy of energy efficiency considerations in integrated resource plans and demand side management plans by utility companies in Georgia and South Carolina.

Synapse Energy Economics Inc. (Synapse) presents this evidence to document its review of the 2026 Load Forecast Report [1](#page-2-0) (Report) of Nova Scotia Power, Inc. (NS Power), and to offer recommendations for improvements. Synapse ha...

AI summary Synapse Energy Economics Inc. reviews NS Power's 2026 Load Forecast Report, noting higher net system requirements and peak levels compared to 2025. Key drivers include increased EV load, electric heating, and new customer growth. Changes in heating and cooling efficiency models, along with fewer heat pump installations, also influence the forecast. The peak model now includes a 24-hour lagged temperature variable.

1.1. Forecast Comparisons To begin, we compare this forecast with those of recent years. [Figure 1](#page-3-0) below shows both the actual historical energy requirements and recent forecasts. Since 2015, actual loads have been relatively f...

AI summary This section compares recent load forecasts with historical data, noting that actual loads have been flat since 2015. The 2026 forecast shows a lower projection due to reduced EV adoption and expansion of the RTR program, leading to a 4.5% increase in net system requirements from 2026 to 2036, driven by EV load, model growth, and new customer growth, partially offset by rooftop solar and DSM.

Res. Comm. Ind. Other Losses NSR (sum) 2026 Forecast (GWh) 5,315 3,165 2,183 131 781 11,575 Model -2 275 27 2 78 381 New Customers 350 39 389 Solar -370 -171 -540 EV 645 373 1018 Commercial & Industrial Electrification 14 20 34 Large Custo...

AI summary The document presents energy usage forecasts for 2026 and 2036, detailing residential, commercial, industrial, and other energy consumption, along with adjustments from various factors such as solar, EV, and DSM initiatives. It includes data on new customers, losses, and NSR, sourced from Figure 58 of the 2026 Load Forecast.

The historical trend for firm peak demand shows a general increase, as shown in Figure 2 below. We plotted the actual rather than the weather-normalized historical peaks to show the year-to- year variations. The 2026 firm peak demand incre...

AI summary The historical trend for firm peak demand shows a general increase, with a 244 MW (10.4%) rise by 2026, primarily driven by increased electrification. The modeled values from the SAE modeling and subsequent adjustments are detailed in Figures 65 of the Report.

Source: Synapse from Appendix A Table A2, 2025 Load Forecast and 2024 IR Response 1 Table 2. 2026 Peak contribution components (MW) Modeled Peak (MW) Res Heat (MW) EV (MW) DR (MW) Hybrid (MW) C&I Elect. (MW) Large Cust. (MW) DSM (MW) Firm...

AI summary The document presents a table showing the 2026 and 2036 peak contribution components in Nova Scotia, including modeled peak demand, residential heating, EV load, demand response, and DSM impacts. It also highlights the accuracy of NS Power's firm peak forecasts, noting inconsistencies in over- and under-forecasting over the past five years.

2. BOARD DIRECTIVES AND PRIOR SYNAPSE RECOMMENDATIONS In its Decision concerning NS Power's 2025 forecast, in Matter 12349, dated December 19, 2025, the Board issued several directives to NS Power for its 2026 forecast. In this Decision, t...

AI summary The Board issued directives to NS Power regarding its 2026 forecast, including implementing prior recommendations and monitoring various factors such as housing completions, forecast variances, and government policies on electrification. The Board also encouraged continued monitoring of battery storage price trends and noted some intervenor suggestions for future planning.

3.1. General Updates and Major Drivers While the forecast methodologies differ by class, a few updates and standing treatments affect the forecast broadly and are best addressed before turning to the individual sectors. NS Power continues...

AI summary NS Power uses a 20-year economic forecast from Signal49 Research and benchmarks it against major banks. RTR migration is reducing forecast energy demand, but peak demand remains unchanged. DSM programs continue to reduce load, with data drawn from current and pending agreements, and historical adjustments applied to avoid double-counting savings.

3.2. Residential Sector The residential class represents about 46 percent of total load and is forecast using the SAE model described above. The model expresses residential average use as the sum of three end-use intensity terms — heating...

AI summary The residential sector accounts for 46% of total load and is forecasted using the SAE model, which includes factors like heating, cooling, and other uses, along with embedded DSM savings and a COVID variable. Electricity price has a modest influence with a −0.15 elasticity.

Table 4. Residential load: post regression (GWh) Existing Customer Average Use from Regression Model (kWh/year) New Cust. Load EVs Solar RTR Hybrid Res. DSM Adjust. Res Sales Total Res. DSM (at meter) DSM captured by end uses 2026 10,376 6...

AI summary Table 4 presents residential load forecasts for 2026 and 2036, including factors such as new customer load, EVs, solar, RTR, and DSM adjustments. The data shows changes in load across various categories, with some factors increasing and others decreasing.

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.

3.4. Industrial Sector The industrial class represents about 20 percent of total load and is projected to decline modestly over the forecast period. Unlike the residential and commercial classes, it is not forecast with end-use models. NS...

AI summary The industrial sector accounts for about 20% of total load and is expected to decline modestly. NS Power uses econometric models and customer surveys to forecast load changes, with migration to RTR impacting load projections. Uncertainty remains due to potential changes in major customers' operations.

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.

4. PEAK FORECAST NS Power forecasts the system peak by first producing a modeled peak from historical data and economic and demographic projections, then applying a series of adjustments — residential heating, EVs, demand response, hybrid...

AI summary NS Power forecasts the system peak using historical data, economic projections, and adjustments for factors like heating, EVs, and DSM. The forecast shows a 1.1% annual growth in peak demand from 2026 to 2036. A revised model incorporating a 24-hour lagged temperature variable improved accuracy, reducing unexplained variance from 78 MW to 36 MW for the 2026 peak. However, the model's performance may depend on interactions between variables, suggesting the need for more sophisticated forecasting methods.

Table [Table 7](#page-12-0) shows the peak forecast decomposed into component parts. Table 7. 2026 Peak contribution components (MW) Modeled Peak (MW) Res Heat Peak (MW) EV (MW) DR (MW) Hybrid (MW) C&I Elect. (MW) Large Cust. (MW) DSM (MW)...

AI summary Table 7 provides a forecast of peak demand contributions for 2026, 2036, and 2035 (max non-coincident EV peak) in MW. It breaks down the contributions from various sectors and programs such as residential heat, EV, demand response, and DSM.

Recommendations While the inclusion of the 24-hour lagged temperature variable appears to be well supported, NS Power should consider the suitability of other modeling approaches that would reflect possible interactions between lagged temp...

AI summary The document recommends that NS Power consider alternative modeling approaches to better capture the non-linear impacts of long-duration cold on peak demand, beyond the current 24-hour lagged temperature variable.

5. DISCUSSION OF SPECIFIC END-USES AND SOLAR PV In this section, we provide more detailed discussion of specific topics within the load forecast. We focus on heat pumps, electric vehicles, solar generation, and demand side management as th...

AI summary This section discusses specific end-uses and solar PV, focusing on heat pumps, electric vehicles, solar generation, and demand side management as major drivers of change in the load forecast, based on prior Synapse recommendations.

Residential NS Power's residential heat pump forecast distinguishes three categories of heat pump households: (1) all-electric (AE) households, with heat pumps and no backup heating; (2) non-all-electric standard (NAE Standard) households,...

AI summary NS Power's residential heat pump forecast categorizes households into three groups and assumes a 50% participation rate for hybrid programs. However, the analysis suggests that this participation rate should be phased in over several years and extended to existing systems, citing EfficiencyOne's experience with lower participation rates in similar programs.

Commercial NS Power notes that, in response to the continued lack of provincial discussion of a commercial hybrid heating program, it delayed the modeled implementation date to 2028.[6](#page-15-1) We caution that it may be premature to as...

AI summary NS Power delayed the implementation of a commercial hybrid heating program until 2028 due to the lack of provincial discussion. The forecast includes a significant peak reduction but lacks clarity on participation rates and assumptions. A reassessment is recommended to ensure the trajectory is reasonable and supported by market conditions.

Efficiency assumptions and heating intensity methodology The 2026 Load Forecast does not clearly identify which efficiency metric underlies NS Power's heat pump assumptions. Based on our review of the forecast workpapers, we assume that NS...

AI summary The 2026 Load Forecast does not clearly specify the efficiency metric used by NS Power for heat pump assumptions, leading to potential inaccuracies. The analysis suggests that NS Power may be using HSPF instead of HSPF2 and relies on a calibration process involving historical data and the Itron study for heating intensity values.

ZEV sales forecast NS Power assumes that zero-emission vehicle (ZEV) sales will reach 75 percent by 2035, per the federal government's implied goal of 75 percent ZEV sales by 2035 (the federal EV Availability Standard was cancelled in Febr...

AI summary NS Power assumes ZEV sales will reach 75% by 2035 based on a top-down federal target, but this approach is criticized as flawed and not reflective of realistic deployment. A bottom-up forecast using reputable sources is recommended instead.

Peak charging loads NS Power states that it is not explicitly modeling managed charging for light-duty at-home charging.[15](#page-17-1) In doing so, NS Power is not accounting for any projected changes in managed charging penetration over...

AI summary NS Power is not explicitly modeling managed charging for light-duty at-home charging and uses a coarse assumption that BEV and PHEV peak impacts are the same, which is problematic. It is recommended that PHEV charging be removed from DCFC and workplace L2 peak modeling.

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.

Recommendations NS Power should replace its uniform 10-year DSM savings accumulation window with a vintage-based persistence model using measure-category lives and decay curves. NS Power should also estimate embedded energy and peak saving...

AI summary The document recommends that NS Power replace its current DSM savings accumulation window with a vintage-based persistence model and separately estimate embedded energy and peak savings using cumulative DSM demand savings as input to the peak forecast regression.

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.

M12861, N-9, Synapse Evidence, page 11. should apply a more moderate ramp rate. Meanwhile, NS Power should expand modeled eligibility to include all existing NAE systems, but with substantially lower participation rates. Finally, NS Power...

AI summary The text suggests that NS Power should adjust its ramp rate to be more moderate, expand eligibility for existing NAE systems with lower participation rates, and reassess the commercial hybrid-heating trajectory. It also recommends using explicit assumptions and presenting sensitivity cases for participation rates.

NS Power Response: At the time the hybrid heating assumptions were developed, NS Power relied on the best information reasonably available, including an analysis of AMI data for over 20,000 electrically heated NS Power customers and a stud...

AI summary NS Power explains that its hybrid heating assumptions were based on available AMI data and heat pump load studies. It acknowledges E1's DSM Potential Study and expects to use its findings in future load forecasts. NS Power argues against presenting specific hybrid heating participation scenarios, citing complexity and lack of clear benefit.

NS Power Response: NS Power agrees that modeling the impact of hybrid heat pumps should be included in the underlying residential intensity calculations and will update the calculations accordingly for 2026. Where possible [emphasis added]...

AI summary NS Power agrees to include hybrid heat pump modeling in residential intensity calculations for 2026 and will use AMI data where possible. They also agree to develop the capability to model hybrid commercial heating in future forecasts but not for 2026. NS Power disputes the claim that their hybrid heating forecast contradicts the Board's directive in the 2025 Load Forecast Report proceeding.

May 15, 2026 Ms. Crystal Henwood Regulatory Affairs Officer/Clerk Nova Scotia Energy Board 1601 Lower Water Street, 3rd Floor P.O. Box 1692, Unit “M” Halifax, NS B3J 3S3 Re: 2026 Load Forecast and 10 Year System Outlook Reports – IESO Nova...

AI summary The letter discusses the transition of responsibility for preparing the 2026 Load Forecast and 10 Year System Outlook (10YSO) reports from Nova Scotia Power Inc. (NS Power) to the newly established Independent Energy System Operator of Nova Scotia (IESO Nova Scotia), noting upcoming submission deadlines and implications for continued ownership of these filing requirements.

Matter No. M12861 NOVA SCOTIA ENERGY BOARD IN THE MATTER OF: The Public Utilities Act, R.S.N.S. 1989, c. 380 as amended – and – IN THE MATTER OF: Nova Scotia Power Incorporated’s 2026 Load Forecast Report NOTICE OF INTERVENTION TO: The Nov...

AI summary Port Hawkesbury Paper LP (PHP) seeks intervenor status in proceedings regarding Nova Scotia Power Inc.'s 2026 Load Forecast Report, citing its interest as a major power purchaser under the ELIADC Tariff. The notice outlines PHP's business operations and contact details for the proceeding.

Page 40 explains that NS Power's consultant E3, estimated a reduction of 200 GWh under a hybrid scenario, but NS Power does not consider it to be attainable. Please elaborate on the results of the modeling leading NS Power to draw this con...

AI summary Page 40 discusses NS Power's consultant E3 estimating a 200 GWh reduction under a hybrid scenario, but NS Power does not consider it attainable. The page requests an explanation of the modeling results leading to this conclusion.

Request IR-7: - Figure 69: Demand Response table shows a significant increase in Direct Load Control MW in 2032. On the page 92 the application states at lines 21-22, "For 2032 and beyond, the DR amounts revert to the achievable potential...

AI summary Request IR-7 questions NS Power's assumption of a significant increase in Direct Load Control MW by 2032, referencing the 2022 Evergreen IRP. It challenges why NS Power assumes this increase is realistic and whether a gradual approach was considered instead of meeting the IRP's target.

Request IR-3: - System Peak Lagged Temperature Variables - a. Please provide the results of the "iterative testing" that determined the weightings to apply to the 12-hour and 24-hour variables. - b. Please provide the results of the sensit...

AI summary Request IR-3 seeks detailed information on NS Power's modeling of temperature variables in relation to system peak demand, including iterative testing results, sensitivity analysis, and explanations for the impact of cold duration versus minimum temperature on peak demand, as well as alternative modeling approaches.

Request IR-4: - System Peak Bottom-up Approach - a. Please reconcile the statement that "NS Power currently forecasts system peak at the aggregate level….the system peak is disaggregated into rate class contributions based on historic load...

AI summary Request IR-4 asks NS Power to reconcile its system peak forecasting methodology, specifically the use of historic load factors and disaggregation into rate class contributions, with the formal peak model specification. The request also seeks an explanation of alternative approaches to class-level peak forecasting, including those that avoid or adjust for growth factors and historical peak reliance.

Request IR-6: - New Technologies (Section 4.5.6, p. 51) - a. Refer to Section 4.5.6 of the 2026 Load Forecast Report, which states that "[t]he 2026 Load Forecast does not assume a significant amount of distributed solar/battery storage com...

AI summary Request IR-6 seeks clarification on NS Power's assumptions regarding distributed solar-plus-storage, storage-only, and V2G technologies in the 2026 Load Forecast Report. It specifically asks about the inclusion of these resources in the forecast, projected battery cost declines, peak impact sensitivity analyses, and quantification of winter peak reduction from these technologies.

Request IR-8: Residential Hybrid Heating (Section 4.5.2, p. 37-42) - a. Refer to the following statements in the 2026 Load Forecast Report: "The 2026 residential SAE regression model has been updated to include peak and energy reductions r...

AI summary Request IR-8 focuses on residential hybrid heating analysis by NS Power, including data on load shapes, participation rates, modeling approaches, and documentation related to the 2026 Load Forecast Report. It asks for detailed explanations and supporting evidence regarding the impact of hybrid heating programs on energy and peak demand.

Request IR-9: - Commercial Hybrid Heating (Section 4.5.2, p. 42) - a. Refer to Figure 26 and the following statement on page 42: "As with previous load forecasts, the commercial energy values predicted from the commercial SAE model is high...

AI summary Request IR-9 seeks clarification on the discrepancy between the commercial SAE model and E3 hybrid scenario model for commercial hybrid heating, and asks for detailed explanations, supporting documentation, and assumptions used in the 2026 Load Forecast. It also inquires about the validation of E3's assumptions by NS Power.

Request IR-11: provided by E1." - Electric Vehicles (EVs) (Section 4.5.4, p. 44-47) - a. Please provide the source data and calculations behind Figures 28-30. - b. Please specify the share of EVs in the forecast which are light-duty, non-p...

AI summary Request IR-11 seeks detailed information on Nova Scotia Power's load forecast for electric vehicles, including data sources, methodology, assumptions, and how changes in mandates and incentives have influenced the forecast. It also asks about the inclusion of managed charging and the use of AMI data in future forecasts.

Request IR-15: - Demand Side Management (Section 4.7, p. 57-60) - a. Refer to the following statement on pages 58 to 59 of the NS Power 2026 Load Forecast Report: "to introduce cumulative historical DSM savings as reported by E1 to the reg...

AI summary Request IR-15 focuses on demand-side management (DSM) savings, asking for historical data, methodology, and assumptions used by E1 and NS Power in their 2026 Load Forecast Report. The request includes details on cumulative energy and peak load savings, savings decay, and the starting year for DSM savings estimates.

NON-CONFIDENTIAL INFORMATION REQUESTS TO NS POWER To: Nova Scotia Power Inc. Jessie Wallace Manager, Regulatory Strategy & Innovation Email: [email protected] From: EfficiencyOne c/o McInnes Cooper Responses Due: July 7, 2026 Copie...

AI summary EfficiencyOne requests NS Power to clarify assumptions and methodologies in the 2026 Load Forecast Report, including hybrid heating participation rates, energy/peak demand reduction estimates, and whether hybrid heating qualifies as demand-side management under the Public Utilities Act. Questions focus on data consistency, modeling approaches, and legal classification.

1 Request IR-02: - 2 E1 has used data from the Load Forecast (total peak reduction, total energy reduction, and - 3 cumulative participants) to calculate full load hours, unitary demand reduction, and unitary - 4 energy reduction in Table...

AI summary E1 has used data from the Load Forecast, including total peak reduction, total energy reduction, and cumulative participants, to calculate full load hours, unitary demand reduction, and unitary energy reduction in Table 1.

6 Table 1: Calculated Full Load Hours, Unitary Demand Reduction, and Unitary Energy Reduction for Modelled Hybrid 7 Heating in the 2026 Load Forecast. Column: A B C D E F Year Total Peak Reduction (MW) Total Energy Reduction (GWh) Hybrid H...

AI summary Table 1 presents calculated values for full load hours, unitary demand reduction, and unitary energy reduction for modelled hybrid 7 heating in the 2026 load forecast, showing negative demand and energy reductions increasing over time, with cumulative participants also rising.

5 - 9 (a) Please explain why the modelled hybrid heating program has an average full-load hours 10 reduction in electric space heating of 1,656, and whether this is consistent with the 11 program's intended design? If the intended program...

AI summary The text requests an explanation for the reduction in electric space heating full-load hours under the hybrid heating program and asks for assumptions regarding the operation hours of hybrid heating systems by NS Power, including how these assumptions are modeled.

1 (d) If hybrid heating does not achieve the forecast reductions, what is the quantified impact 2 on the Net System Requirement and system peak? Please provide the forecast with and 3 without hybrid heating for each year from 2028 to 2036....

AI summary The text requests an explanation of the average annual energy reduction from the modelled hybrid heating program and its impact on the Net System Requirement and system peak if the program does not achieve forecasted reductions. It also asks for comparisons of the 3,034 kWh reduction against residential consumption and heating usage, and whether NS Power agrees with the calculated values in Table 1.

12 Request IR-08: - 13 (a) Please confirm that E1's demand response (DR) programs are modelled as reductions to 14 firm peak only, and that they are excluded from the system peak forecast (as stated on 15 page 95 of the 2026 Load Forecast...

AI summary The request questions how E1's demand response (DR) programs are modeled as firm peak reductions versus hybrid heating's system peak reductions, querying rationale for differential treatment, reliability value attribution, and trigger event impacts on modeling decisions.

1 Request IR-09: 2 Reference: NS Power 2026 Load Forecast Report, page 42, lines 3 – 17 3 4 (a) What is the capacity reduction associated with commercial hybrid heating? 5 (b) If no capacity reduction has been modelled, does this imply the...

AI summary The text includes requests for information regarding the capacity reduction associated with commercial hybrid heating programs, the assumptions and methodologies used in the NS Power 2026 Load Forecast Report, and the potential risks and validation of post-model adjustments related to hybrid heating impacts.

20 Request IR-2: 22 Reference: Exhibit N-1, p. 41. - 24 (a) Please provide the system-coincident unmanaged peak impact per vehicle for 2025 in 25 the same format as the response in Exh. N-2, Matter M11108, CA RIR-3. - 27 (b) Please provide...

AI summary Request IR-2 seeks data on system-coincident unmanaged peak impact per vehicle for 2025 and assumptions about managed charging participation rates by vehicle type, referencing Exhibit N-1 and Matter M11108. The request aligns with load management and demand-side management themes.

31 Request IR-3: 33 Reference: Exhibit N-1, Section 10.4. 35 (a) Please provide tables summarizing NS Power's forecasts for demand reduction and 36 actual demand reduction due to Direct Load Control (DLC), Time Variable Pricing 37 (TVP) an...

AI summary The document requests NS Power to provide tables summarizing demand reduction forecasts and actual demand reduction for Direct Load Control, Time Variable Pricing, and BNI Curtailment, including year-to-date values for 2026.

Request IR-3: Refer to M12861, Exhibit N-1, the Report Section 4.4, Economic Information, page 29 of 105, Lines 9-21 and respond to the following: a) Please provide the calculation used to replace Signal49's average 24 percent year-over-ye...

AI summary Request IR-3 asks NS Power to explain the calculation used to replace Signal49's decline rate with their own assumption, identify developments totaling 40-50,000 units, and explain whether recent market conditions affecting housing completions were considered in their forecast.

Request IR-4: Refer to M12861, Exhibit N-1, the Report, Section 4.5.2, Hybrid Heating, page 40 of 105 and respond to the following: a) Please provide a detailed description of the customer configuration assumed to qualify as a hybrid-heati...

AI summary Request IR-4 asks for a detailed description of hybrid heating customer configurations, the basis for a 50% participation rate and 27,000 enrolled customers by 2036, and the criteria for NS Power-declared CPP events. It references specific sections and lines in M12861, Exhibit N-1, and the Report.

Request IR-8: Refer to M12861, Exhibit N-1, the Report Section 6.1, Small General Service, Page 69 of 105, Lines 9-11 and respond to the following: a) Please explain how DSM and RTR are expected to "moderate the rate of growth" in the Smal...

AI summary Request IR-8 asks for an explanation of how DSM and RTR are expected to moderate the rate of growth in the Small General Service forecast, including supporting assumptions and analysis, and whether RTR is primarily expected to affect larger commercial and industrial customers rather than Small General Service customers.

20 Request IR-2: 21 Refer to M12861, Exhibit N-1, the Report, Section 4.5.2, Hybrid Heating, Section 4.5.4, Electric 22 Vehicles, and respond to the following: 23 24 a) The forecast models hybrid heating at 50% participation (approximately...

AI summary The request asks whether the existing transmission and distribution system can accommodate higher penetration of hybrid heat pumps and managed EV charging, and whether such flexible loads would require less infrastructure reinforcement compared to unmanaged loads, citing E3 findings on peak reduction potential.

13 Request IR-5: 14 Refer to M12861, Exhibit N-1, the Report, the treatment of behind-the-meter solar and distributed 15 storage, and respond to the following: 16 17 a) The Report assumes no significant distributed storage and treats behin...

AI summary The request asks NS Power to assess whether flexible loads can shift demand to periods of high solar output and improve solar utilization, including reducing curtailment risk, in light of the Report's assumption that behind-the-meter solar is primarily a load reduction.

24 Request IR-6: 25 Refer to M12861, Exhibit N-1, the Report, Figure 31, and respond to the following: 26 27 a) Figure 31 assigns 0 MW of peak reduction to solar across all years, on the basis that the 28 system peak occurs after sunset. W...

AI summary The request questions the assumption in Figure 31 that solar contributes 0 MW of peak reduction, based on the system peak occurring after sunset. It asks if this assumption was carried forward from the 2025 Load Forecast and whether NS Power plans to revisit it with updated AMI and production data, including solar paired with storage.

July 21, 2026 VIA EMAIL Ms. Crystal Henwood Clerk of the Board Nova Scotia Energy Board 1601 Lower Water Street, 3rd Floor Halifax NS B3J 3S3 Dear Ms. Henwood: Re: M12861 - Nova Scotia J>ower Inc. (NSPI) - 2026 Load Forecast Report - REDAC...

AI summary The Small Business Advocate (SBA) reviewed NSPI's 2026 Load Forecast Report and acknowledges its thoroughness but raises concerns about the housing completions adjustment. The SBA notes that NSPI did not explicitly account for market conditions like development delays and financing constraints, despite incorporating a flat 10% annual decline in Signal49's forecast.