N-12025 Load Forecast Report + Appendices - Redacted
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8 4.1 Historical Class Sales and Energy Data .................................................................... 16 9 4.2 Weather Data ..........................................................................................................
AI summary The document outlines sections analyzing historical energy sales, weather data, economic factors, end-use trends (including heat pumps, EVs, solar PV), and price data. It emphasizes load forecasting, renewable integration, and demand-side management as key themes in the regulatory proceeding.
1 LIST OF FIGURES 2 3 Figure 1: Historical and Predicted Annual Net System Requirement ...........................................8 4 Figure 2: Historical and Predicted Annual System Peak ......................................................
AI summary The document lists figures related to energy demand forecasting, system requirements, and climate trends, including historical data and predictions for net system requirement, system peak, heating and cooling degree days (HDD/CDD), and customer demographics.
.......... 26 17 Figure 15: Household Size ........................................................................................................ 27 18 Figure 16: Residential Economic Drivers ................................................
AI summary The text lists figures analyzing economic drivers, heat pump adoption, electric vehicle (EV) impact, and solar photovoltaic (PV) effects on energy demand. It includes forecasts for residential, commercial, and industrial sectors, as well as comparisons of heating technologies and EV load modeling.
8 30 Figure 72: 2024 Monthly class sums from AMI data vs System Generation ............................. 89 31 Figure 73: Example of time-varying EV effect detection in AMI data ....................................... 91 32 Figure 74: Syst...
AI summary The document outlines the 2025 Load Forecast Report, referencing figures analyzing AMI data, system generation, EV effects, and energy sensitivity. It includes attachments and appendices detailing residential/commercial demand models, forecast comparisons, and stakeholder presentations, with partial confidentiality noted.
energy forecasts derived from 26 the residential and commercial SAE models are then combined with an econometric-based 27 industrial forecast and customer specific forecasts for NS Power’s large customers to develop an 28 energy forecast f...
AI summary The 2025 Load Forecast Report indicates increased near-term Net System Requirement (NSR) due to changes in Renewable to Retail (RTR) sales, with mid- to long-term growth reduced by lower EV sales, higher RTR and behind-the-meter solar adoption, and Demand Side Management (DSM) initiatives. Annual NSR is projected to decrease by 0.2% between 2025–2035, while peak demand remains stable near-term despite electrification trends.
1 2.0 INTRODUCTION 2 3 NS Power develops an annual forecast of energy sales and peak demand requirements which assess 4 the effects of end-use and economic factors on the future power system load and load shape. The 5 forecast is a foundat...
AI summary NS Power's 2024 Load Forecast Report was reviewed by the NSUARB through a paper hearing process. Intervenors including the Consumer Advocate and EfficiencyOne provided input, with the Board encouraging NS Power to improve forecast accuracy by evaluating residential model variables. The Board acknowledged NS Power's commitment to continuous improvement.
l system monthly energy and monthly demand data is 23 derived from system hourly load data for the period January 2015 to December 2024. Large 24 customer peak demand is forecast separately. 25 DATE: June 27, 2025 Page 16 of 94 REDACTED (C...
AI summary The document discusses the derivation of system load data from 2015–2024 hourly measurements and the use of Heating Degree Days (HDD) and Cooling Degree Days (CDD) to model weather impacts on energy demand. It notes a declining trend in annual HDD due to climate change, affecting long-term load forecasting.
trends, as shown in Figure 19 6. 20 DATE: June 27, 2025 Page 17 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 6: Historic Annual HDD 2 3 4 To reflect the general warming trend as seen in the...
AI summary The 2025 Load Forecast Report analyzes historical Heating Degree Day (HDD) and Cooling Degree Day (CDD) trends using regression analysis of 30 years of data with a 10-year moving average. HDD is projected to decrease by ~16/year, while CDD is expected to increase by ~1.4/year, reflecting long-term climate warming trends.
2025 Load Forecast Report Redacted 1 Figure 11: Rolling 10 Year Average of Annual Max Trend 2 3 4 The Climate Atlas of Canada 7 provides estimates for annual HDD as well as number of “Winter 5 Days” that are -15°C or colder under different...
AI summary The 2025 Load Forecast Report references the Climate Atlas of Canada, projecting declining Heating Degree Days (HDD) and Winter Days in Halifax by 2050 under current and reduced emissions scenarios, alongside rising Cooling Degree Days (CDD) and Summer Days. These trends align with forecasts of increased cooling demand but minimal impact on summer peak loads.
1 9.0 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 indu...
AI summary The Net System Requirement (NSR) for 2024 was 11,326, with the largest variances attributed to weather and a single large customer. Forecasts indicate a slight annual decline in NSR from 2025 to 2035, driven by new customers and EV adoption, offset by solar, DSM, and RTR initiatives.
N-4NSPI (NSEB) RIR 1 to 24
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2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) includes NSPI's responses to NSEB information requests. The document outlines NSPI's position on load forecasting methodologies and energy usage patterns relevant to regulatory proceedings.
me in even lower 29 due to factors detailed in Section 10.1 of the report. Since peak demand is driven largely 30 by weather – predominantly temperature and wind – NS Power remains confident in its Date Filed: August 19, 2025 NSPI (NSEB) I...
AI summary NSPI asserts confidence in its weather-based peak forecasting approach for the 2025 Load Forecast Report, citing historical weather data from previous system peaks as the foundation for its methodology. Peak demand is attributed primarily to weather factors like temperature and wind.
ns are used to forecast 4 residential customers and that new customer growth “is expected to remain positive but 5 decrease over time, as population growth slows from the expected peak in 2024.” 6 7 (a) Why does NS Power consider that popu...
AI summary NS Power cites the Conference Board of Canada (CBoC) for population forecasts, noting population growth peaked in 2024 due to slowing growth post-2024. It explains that Statistics Canada's data lacks forecast details and updates less frequently, hence CBoC data was used. Manufacturing employment forecasts rely on CBoC's 2024 data due to discrepancies in Statistics Canada's modeling.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 (b) NS Power is not aware of a reduction in manufacturing employment in the province of the 2 scale that the Statistics Canada data impl...
AI summary NSPI disputes Statistics Canada's implication of significant manufacturing employment declines between 2023-2024, citing historical growth rates (2.2% pre-pandemic, 4.2% 2021-2023) and noting the 2024 forecast aligns with +0.7% annual growth. The CBoC's 2025 forecast showed -0.3% annual change due to 2024's drop, but NSPI states it cannot adjust input data provided by CBoC.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) includes NSPI's responses to NSEB information requests. The document outlines NSPI's position on load forecasting methodologies and energy usage patterns relevant to regulatory proceedings.
1 2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 Page 69 of the application states that Medium Industrial sales will “decline in 2026 and 2027 4 as load migrates to...
AI summary NSPI explains that the 2026 Medium Industrial load forecast increased from 39 GWh (2024) to 84 GWh due to updated data from Renewall, the Licensed Retail Supplier. It clarifies that the 34 GWh load migration to RTR by 2027 is in addition to the 84 GWh figure.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) includes NSPI's responses to NSEB information requests. The document outlines NSPI's position on load forecasting methodologies and energy usage patterns relevant to regulatory proceedings.
1 Request IR-22: 2 3 Figure 76 provides a comparison of the 2022 Evergreen Integrated Resource Plan 4 (IRP) Scenarios to the 2024 and 2025 load forecast. 5 6 (a) Has the Evergreen IRP E1 – Current Policy and Trends scenario been updated to...
AI summary The Nova Scotia Energy Board (NSEB) questions whether the Evergreen IRP E1 scenario reflects updated electrification policies and if NS Power's IRP and load forecasts align. NS Power responds that the E1 scenario is based on the 2022 forecast but the 2025 forecast incorporates current policies. Forecasts are deemed aligned despite small differences in firm peak (28 MW) by 2035, though energy forecasts show greater variation.
From an energy perspective, there is a greater variation between the two forecasts in both 30 the near term and in 2035, but the direction of variation varies. In 2025, the energy Date Filed: August 19, 2025 NSPI (NSEB) IR-22 Page 1 of 2 2...
AI summary The 2025 Load Forecast Report compares energy requirements between the 2025 forecast and the Evergreen IRP Scenario E1, noting differences in 2025 and 2035. The updated forecast is within historical variance ranges for NS Power's long-term predictions.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 Appendix D, page 6 states that, for the sensitivity of energy sales, weather is the strongest 4 variable but “in the...
AI summary NSPI explains that economic factors like income, GDP, and employment compound over time, influencing energy sales similarly to weather impacts. Higher household income and industrial growth increase energy demand, with economic sensitivity matching weather effects in long-term forecasts.
N-5NSPI (SBA) RIR 1 to 13
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on page 42 of 27 the report. Any current programs are expected to remain in place, and growth is assumed 28 to continue at approximately 15 percent increase in installs per year. Date Filed: August 19, 2025 NSPI (SBA) IR-6 Page 1 of 1 2025...
AI summary NSPI maintains current programs and anticipates a 15% annual growth in installations. The 2025 Load Forecast Report (NSEB M12349) is referenced, with NSPI providing responses to SBA Information Requests.
Washer EV Washer EV Year Heat Misc Light Vent Refrig Office EWHeat Cooking EV Total 2025 Commercial End Use Peak Shares 2025 218.73 83.67 129.01 26.42 42.54 32.27 5.70 9.16 1.70 549.2 2034 Commercial End Use Peak Shares 2034 267.56 85.22 1...
AI summary The table compares 2025 and 2034 commercial end-use peak energy shares, showing Heat as the largest category (39.8% to 42.8%), with EV demand rising from 0.3% to 6.5%. Total commercial peak demand increases from 549.2 to 624.9, reflecting growing energy use trends.
2025 Commercial End Use Peak Shares 2034 Commercial End Use Peak Shares 1.7% 0.9% 1.0% 0.3% 1.5% Heat Heat 5.7% 6.5% 5.9% Misc Misc 7.7% Light Light 4.8% Vent 6.7% Vent 39.8% 42.8%
AI summary The text presents comparative data on commercial end-use peak energy shares for 2025 and 2034, showing shifts in energy consumption categories like heat, lighting, ventilation, and miscellaneous uses. Light and ventilation usage increases, while heat and miscellaneous uses decrease slightly.
N-8Evidence - Synapse
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9.................................................................................5 1.5. Recommendations from the Previous Forecast Review .....................................................6 2. ENERGY FORECAST .............................
AI summary The document outlines sections of an energy forecast review, covering residential, commercial, industrial, and municipal sectors, with a focus on demand-side management (DSM) effects. It includes subsections on forecasting methodologies, electrification trends, and peak demand analysis, particularly addressing electric vehicles and AMI data.
The 2025 forecast predicts a 242 gigawatt-hour (GWh) decrease—2.1 percent—in the net system requirement (NSR) from 2025 to 2035. NS Power lays out the components of this change in Figure 59 of its report, as shown in Table 1. The “Model” g...
AI summary The 2025 forecast predicts a 2.1% (242 GWh) decrease in net system requirement (NSR) from 2025 to 2035, driven by electrification, EV growth, and new customers, offset by rooftop solar, DSM, and RTR sales. Hybrid heating adjustments further reduce NSR.
made to arrive at the firm peak. Figure 2. Firm peak demand Source: Synapse, from Figure 2 in the 2025 Load Forecast and Synapse IR-20 Att 01 EO. Table 2. 2025 Peak contribution components (MW) Res. Modeled heat C&I Large Firm Inter. Syste...
AI summary The document discusses Nova Scotia Power's 2025 and 2035 load forecasts, including peak demand contributions by sector and DSM impacts. It highlights forecast accuracy, noting under-forecasting for longer lead times, and provides sectoral energy use projections, with municipal loads increasing while others decline.
view Table 3 shows the forecast energy use by sector. Overall, municipal and other load is the only sector with a projected increase. The remaining sectors show modest decreases between 2025 and 2035. Table 3. Sector energy requirements (G...
AI summary Table 3 forecasts sectoral energy use, showing municipal/other load growth while other sectors decline. DSM is projected to reduce 2035 load by 575 GWh (5%), with Synapse noting forecast assumptions about RTR and solar adoption. The analysis questions specific forecast components and suggests improvements.
in the peak model has shifted to a rolling 10-year average, which is - 14.2°C, in order to reduce year-to-year temperature fluctuations.9 These data should be analyzed and updated on a regular basis. Recommendations and considerations The...
AI summary The text discusses adjusting temperature models to a 10-year average, recommends analyzing trade tariffs' impact on economic forecasts using Conference Board of Canada (CBoC) data, and highlights the residential sector's load reduction due to Demand Side Management (DSM) programs.
ry change drivers for XOther are water heating (increased electric heater saturation), reductions in lighting use, and decreased television use. The net effect is to decrease XOther by 1.3 percent. 13 There are many factors driving the SAE...
AI summary The text discusses factors influencing residential energy use, including water heating and reduced lighting, leading to a 1.3% decrease in XOther. It outlines the SAE model's breakdown of residential energy use (48% heating, 5% cooling, 50% other) and NSPI's methodology for forecasting residential load using the model, incorporating adjustments like new customer load and DSM impacts.
’s forecast will yield accurate projections. Recommendations and Considerations NSPI should monitor the accuracy of its projections of housing completions, and consider changes to this methodology. Price elasticity NSPI demonstrated that t...
AI summary NSPI should monitor housing completion projections and adjust methodology. Price elasticity of -0.15 aligns with SAE models. NSPI revised COVID-19 work-from-home modeling, removing the variable from General Service models while retaining binary shift variables. Synapse supports NSPI's approach to phase out the separate COVID-19 variable.
100378Board Decision Letter
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the unexplained variance between forecast and actual NSR for the residential sector; and, • report on the findings of the continued investigation into the 2023 unexplained variance. In addition to the Board’s directives to adjust inputs an...
AI summary The document discusses NS Power's revisions to forecast models, including adjustments to employment data and housing completions, as well as the impact of cloud cover on energy demand forecasts. It addresses the Board's directives to improve forecasting accuracy and NS Power's response to the 2023 unexplained variance in residential sector NSR.
-4- increasing on a per EV basis more so than the previous Reports. This forecast is worth revisiting for the 2026 Load Forecast Report to confirm accuracy. In the 2023 decision, the Board did not agree with NS Power’s attribution of the C...
AI summary The document discusses load forecast variances, noting the Board's rejection of NS Power's pandemic and heat pump attribution for 2022 discrepancies, while acknowledging heat pumps as a partial factor. It highlights reduced unexplained variance in 2024 and projects future demand declines due to renewable retail shifts and solar adoption, with General and Medium Industrial demand expected to fall by 7.3% and 1.4% respectively between 2025-2035.
outcome was forecast for Medium Industrial customers. Overall, between 2025 and 2035, General demand sales will decrease 7.3% and Medium Industrial demand sales? will have an average decline of 1.4%. The CA did not find fault in the applic...
AI summary The document outlines forecasted demand declines for General and Medium Industrial customers between 2025-2035, with recommendations from the CA for improved class-specific peak load forecasting and verification of RtR sales data. The SBA challenges NS Power's arbitrary 2% annual electricity price increase projection, urging the use of measured factors for long-term sales forecasts.
d the adoption of technologies affecting future load. The Board expects that these adjustments will be closely monitored in subsequent forecasts to ensure their continued accuracy and appropriateness. NS Power is directed to continue compa...
AI summary The Board directs NS Power to monitor economic forecasts, compare data sources, and track battery storage price trends. It agrees that some intervenor suggestions are better suited for the next Integrated Resource Plan. The Independent Energy System Operator (IESO) has initiated a stakeholder engagement process.
98689NSEB (NSPI) IR 1 to 24 - Redacted
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o adjust the forecast to avoid zero HDD? 25 b) When is the unadjusted model projecting to reach zero HDD? 26 27 Request IR-4: 28 Page 24 of the application explains that housing completions are used to forecast residential 29 customers and...
AI summary The text raises questions about adjusting HDD forecasts to avoid zero values, the timeline for unadjusted HDD projections, NS Power's population growth assumptions peaking in 2024, and the source of population data in Figure 13, including potential deviations from Statistics Canada's forecast.
urce for Change in Population presented in Figure 13. 33 i. If Statistics Canada’s population forecast table 17-10-0009-01 was not used, 34 please explain why.
AI summary The text requests clarification on the use of Statistics Canada’s population forecast table (17-10-0009-01) for modeling population changes, asking for an explanation if it was not utilized. The focus is on data sourcing for demographic projections.
Document: 322941 Date Filed: 07/24/25 Page 2 1 Request IR-5: 2 Page 27 of the application explains that the manufacturing employment forecast is based on the 3 2024 Conference Board forecast because the 2025 forecast shows a significant de...
AI summary The document outlines requests for clarification regarding NS Power's use of employment forecasts, adjustments to energy estimates, and validation of E3's forecasting models. Questions focus on the accuracy of data sources, alignment with current employment trends, and methodological consistency in energy modeling.
to those estimates. 28 a) Please describe the adjustments under the Change in Energy Forecast (GWh) and the 29 adjustments made under the Change in Peak Forecast (MW) for 2030. 30 b) Please explain the changes from the 2024 Load Forecast R...
AI summary The text requests explanations regarding adjustments to energy and peak forecasts for 2030, changes in NS Power's HP Peak load forecasts for 2030 and 2035, and how these updates improve forecast accuracy in both short- and long-term planning.
Document: 322941 Date Filed: 07/24/25 Page 4 1 Understanding that some of the new housing units will replace existing housing and some new 2 units may be used for tourism, there appears to be a divergence between the population growth 3 ex...
AI summary The text includes requests for clarification on NS Power's load forecasting assumptions, including building efficiency forecasts, industrial load migration discrepancies, municipal load service arrangements, and a reference to the Atlantic Economic Council (AEC). Questions focus on data calibration timelines, load migration projections, and third-party service confirmations.
98721Synapse (NSPI) IR-1 to IR-29
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Date Filed: 07/28/2025 Synapse (NSPI) Page 1 of 12 1 Request IR-1: 2 Report Tables and Graphs 3 a. Please provide in electronic spreadsheet format all tables and graphs with identification of 4 the data source(s) that appear in the load fo...
AI summary The document outlines regulatory requests for detailed load forecast data, explanations of system peak volatility (2022-2025), and the 2024 peak decline. It also asks NSPI to document modifications to heating intensity models and evaluate alternative approaches to reduce residential forecast variances, referencing Board direction in M11689.
variances in the residential NSR? Please address the reasonableness of any other 24 approaches to reducing this variance and please explain why any alternative approaches 25 weren’t ultimately adopted. 26 Request IR-4: 27 Billed versus Acc...
AI summary The text raises questions about residential NSR variances, the reasonableness of approaches to reduce them, and the methodology for forecasting energy sales using differing data periods. It also inquires about the implications of using shorter data periods for Medium Industrial forecasts and the accuracy of cloud cover data from NASA's SWin for Halifax.
ud Cover (Section 4.2.1, p. 23) 10 a. Does NSPI have any concern about how accurately the SWin data from NASA, reflecting 11 conditions in the Halifax area, effectively represents cloud cover across NSPI’s service 12 territory? Please expl...
AI summary The document contains questions directed at NSPI regarding the accuracy of NASA's SWin data for cloud cover modeling, alternative data sources, and methodological approaches in load forecasting. It also asks about NSPI's validation of housing completions and employment forecasts, referencing Board Decision M11689 and the Conference Board of Canada (CBoC) projections.
ion differs from residential PV net metering. 25 e. Please confirm that the information in Figure 33 is not incorporated into the forecast and 26 that it is a sensitivity analysis. 27 f. Please describe what the Residential Share (%) varia...
AI summary The text outlines a series of requests (IR-18 to IR-22) directed at Synapse (NSPI), seeking detailed data, calculations, and explanations related to residential sector forecasts, renewable-to-retail (RTR) energy magnitude changes, net system requirements, and sensitivity analysis. Requests focus on clarifying variables, source data, and methodological assumptions in the report.
100378Board Decision Letter
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December 19, 2025 [email protected] Jennifer Ross Director Regulatory Planning & Compliance Nova Scotia Power Inc. PO Box 910 Halifax NS, B3J 3S8 Dear Ms. Ross: M12349 – Nova Scotia Power Inc. 2025 Load Forecast Report In accordance...
AI summary Nova Scotia Power Inc. (NS Power) submitted its 2025 Load Forecast Report after extensions granted by the Nova Scotia Energy Board. The proceeding includes interventions from groups like the Consumer Advocate and Energy Storage Canada, with Synapse Energy Economics reviewing the filing. Evidence and rebuttals were submitted by multiple parties.
the unexplained variance between forecast and actual NSR for the residential sector; and, • report on the findings of the continued investigation into the 2023 unexplained variance. In addition to the Board’s directives to adjust inputs an...
AI summary NS Power revised residential customer forecasts and employment data due to inaccuracies in Conference Board of Canada projections and policy changes like electric vehicle adoption and RtR market impacts. The Board directed adjustments to NSR variance analysis and model assumptions, noting reliance on Statistics Canada's Labour Force Survey data. Cloud cover data testing showed minimal forecast improvement, leading to its exclusion.
rea data from satellite observations. Through testing, NS Power found adding this data improves the forecast, but since the improvement was insignificant the dataset was not added to the final Report. NS Power’s Heat Pump forecast was revi...
AI summary NS Power revised forecasts for heat pumps and EVs based on installer feedback, incentive changes, and AMI data analysis. Satellite data improvements were deemed insignificant, and EV forecasts were reduced by 40,000 units by 2035. AMI data revealed a 54% load increase per EV, prompting a call to revisit the 2026 Load Forecast Report.
ted annually., Therefore, according to NS Power, it was appropriate to revise the EV forecast in response to the end of government incentives. -7- Findings NS Power’s rebuttal stated, “Each annual Load Forecast report reflects continuous i...
AI summary The Board agrees with NS Power's improved forecasting methodologies and intervenor input. It directs NS Power to implement agreed recommendations, monitor housing completions, and investigate residential sales variance. NS Power's model adjustments respond to government policy changes impacting electrification and technology adoption.