N-12025 Load Forecast Report + Appendices - Redacted
40 passages
REDACTED (CONFIDENTIAL INFORMATION REMOVED) Nova Scotia Energy Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended 2025 Load Forecast Report NS Power June 27, 2025 REDACTED REDACTED (CONFIDENTIAL INFORMATION R...
AI summary The 2025 Load Forecast Report by NS Power, prepared under the Public Utilities Act, outlines stakeholder consultations, forecasting methodologies, and key input analyses. It addresses load management strategies and infrastructure planning considerations for Nova Scotia's energy sector.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 31 7.3 Other Industrial Rate Classes .................................................................................... 69 32 7.4 Municipal .....................
AI summary The 2025 Load Forecast Report outlines system requirements, peak demand analysis, solar impact assessments, and sensitivity studies. It includes sections on industrial rate classes, municipal demand, system losses, and comparisons with the Evergreen IRP, focusing on forecasting methodologies and integrated resource planning.
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.
............................................. 74 16 Figure 58: Historical and Forecast Annual NSR ........................................................................ 75 17 Figure 59: Forecast Components ..................................
AI summary The text lists figures related to energy demand forecasting, demand response programs, peak load analysis, and the impact of electric vehicles. Topics include system reliability, load management, and integration of renewable energy sources through advanced metering infrastructure.
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.
1 1.0 EXECUTIVE SUMMARY 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules, 4 Nova Scotia Power Incorporated (NS Power, the Company) is required to provide the Nova Scotia 5 Energy Board (NSEB...
AI summary NS Power is required by the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules to submit an annual 10-year load forecast to the NSEB. The 2025 Load Forecast covers 2025-2035, incorporating factors like sales history, weather, economic indicators, and SAE models for residential and commercial forecasts, along with econometric-based industrial forecasts.
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.
27 of time, if alternative inputs make the residential model more robust, 28 considering the following: 29 • given the Provincial Government’s population growth targets, 30 changes to building permitting and funding such as the Housing 1 N...
AI summary The document references the NSUARB Hearing Order and a 2024 Load Forecast Report, considering population growth targets and housing policy changes to improve residential energy modeling. It cites regulatory decisions and reports related to load forecasting and infrastructure planning.
1 Accelerator, re-evaluate the use of housing completions for the near- 2 term; 3 • continue to provide a comparison of the short-term economic inputs 4 provided by the Conference Board of Canada to ensure the data is 5 close to what is us...
AI summary The NSUARB directs NS Power to revise load forecasting models, align EV adoption rates with Statistics Canada data, test TVP elasticity, and address CMHC housing data limitations. Adjustments to 10-Year Forecast Classes and updates to the Capacity Value Study for demand response are also mandated.
response before the next IRP 26 analysis; and, 27 • continue to investigate the unexplained variance between forecast and 28 actual NSR for the residential sector. 29 30 In accordance with the Board’s direction, NS Power revised and enhanc...
AI summary NS Power revised the 2025 Load Forecast by adjusting residential customer projections using historical data from the Conference Board of Canada and comparing economic indicators with major banks' forecasts, as directed by the NSUARB. The analysis also investigates unexplained variance between forecasted and actual Net System Requirement (NSR) in the residential sector.
Page 12 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 • EV adoption rates have been adjusted to align with recent trends and the elimination of 2 federal and provincial rebates. Please refer to Sect...
AI summary NS Power's 2025 Load Forecast Report outlines updates to EV adoption rates, work-from-home trends, temperature impacts, and the Capacity Value Study. Stakeholder consultations with NSUARB, CA, SBA, IG, E1, and EE addressed residential load estimates, solar integration, RTR impacts, and forecast variances.
94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 3.0 FORECASTING APPROACH 2 3 NS Power continues to use a set of SAE models for the Residential 4 and Commercial 5 rate classes, 4 an econometric model for...
AI summary NS Power uses SAE models for residential and commercial classes, econometric models for small/medium industrial, and customer surveys/historical data for large industrial. The SAE model combines econometric and end-use methods, incorporating efficiency, population, economic factors, and weather. Long-term growth is driven by economics and structural changes captured via SAE.
and 20 growth. Structural changes are captured in the residential forecast model through the SAE model 21 specifications. Figure 4 shows the general forecast approach used in the SAE models. 22 4 References to the Residential class include...
AI summary The document discusses residential load forecasting using the SAE model, which incorporates structural changes through specified residential, commercial, and industrial class definitions. It references a 2025 Load Forecast Report and includes a general forecast approach illustrated in Figure 4.
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.
Page 19 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 These trends are reduced over time (approximately 40 years) such that the average annual HDD is 2 not forced to 0 and the average annual CDD doe...
AI summary The 2025 Load Forecast Report discusses evolving HDD and CDD trends, showing reduced winter heating loads and increased summer cooling loads by 2035. Leap years cause annual fluctuations, while the temperature input model uses a rolling 10-year average to stabilize forecasts, updating from -14.4°C to -14.2°C for peak temperature inputs.
14.2°C. This will reduce year-to-year DATE: June 27, 2025 Page 20 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 fluctuations in the peak temperature input, and will also better reflect the slowly wa...
AI summary The 2025 Load Forecast Report adjusts winter peak temperature inputs using a trend similar to Heating Degree Day (HDD) calculations, reflecting a 0.13°C annual increase in minimum temperatures. Summer peak temperatures remain unchanged, relying on a 10-year average. Wind speed inputs use a 10-year average of 18.3 km/h, with less impact on peak models.
-0.15 % Peak R2 0.985 0.985 0.000 MAPE 2.36 % 2.33 % -0.03 % 22 8 GES DISC Dataset: MERRA-2 tavg1_2d_lfo_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Forcings V5.12.4 (M2T1NXLFO 5.12.4) DATE: June 27, 2025 Page 23 o...
AI summary The text contains statistical data from a load forecast report, including metrics like R2 and MAPE, though much of the content is redacted. The dataset references MERRA-2, a meteorological model, and the document is dated June 27, 2025.
16, 17 and 18 summarize the economic DATE: June 27, 2025 Page 27 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The document is a redacted version of the 2025 Load Forecast Report, which provides an overview of load forecasting methodologies and assumptions used for the year 2025. Key sections summarize the economic factors influencing load forecasts.
1 drivers, on an annual basis, used in the 2025 Load Forecast. For financial measures, the variables 2 have been adjusted to constant dollars, eliminating the inflation effects from the series. 3 4 Figure 16: Residential Economic Drivers N...
AI summary The text presents residential economic drivers, such as new construction and household compensation, from 2015 to 2034, used in the 2025 Load Forecast. The variables are adjusted to constant dollars to eliminate inflation effects.
r the Ecology Action Center DATE: June 27, 2025 Page 38 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The 2025 Load Forecast Report has been submitted, though specific details have been redacted due to confidentiality. The report likely contains data and analysis related to electricity demand forecasting for the year 2025.
Page 38 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The document presents a redacted section of the 2025 Load Forecast Report, which is part of a regulatory proceeding. Key details are obscured due to confidentiality, but the report likely discusses electricity demand forecasting for the year 2025.
Page 53 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The 2025 Load Forecast Report provides an analysis of projected electricity demand for the year 2025. Key aspects include forecasting methodologies, assumptions, and potential impacts on the electricity system. The report is redacted, indicating that confidential information has been removed.
of the forecast DSM amounts. 22 The adjusted R-squared for the model is 0.84 while the MAPE is 2.69, indicating a good fit overall. 23 Figure 40 shows the DSM levels (at the generator) incorporated into the forecast. 24 DATE: June 27, 2025...
AI summary The text discusses the 2025 Load Forecast Report, highlighting a statistical model with an adjusted R-squared of 0.84 and a MAPE of 2.69, indicating a strong fit. It also references Figure 40, which shows DSM levels incorporated into the forecast.
0.901 0.962 154.0 1.001 2029 0.895 0.959 154.3 0.999 2030 0.888 0.957 154.5 0.998 2031 0.881 0.954 154.6 0.996 2032 0.874 0.951 154.7 0.994 2033 0.868 0.949 154.8 0.993 2034 0.862 0.948 154.9 0.992 2035 0.857 0.947 155.0 0.991 20 DATE: Jun...
AI summary The document presents a redacted 2025 Load Forecast Report, which includes numerical data and dates, but the content has been confidentially removed. The report likely discusses load forecasting methodologies and projections for electricity demand.
ns at several hospital sites in the province. The forecast for 8 customer growth related to new projects/expansions is provided in Figure 51. 9 10 Figure 51: Large General Annual Growth (GWh) 11 Year 2025 2026 2027 2028 2025 Forecast 3 3 4...
AI summary The document discusses energy load forecasts, including customer growth projections and the impact of demand-side management (DSM) on overall energy consumption. It mentions a forecasted decrease in large general annual sales by 12 GWh by 2035 due to DSM efforts surpassing projected growth.
Page 73 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The document is a redacted section of the 2025 Load Forecast Report, which contains confidential information. It is part of a regulatory proceeding in Nova Scotia, likely related to energy planning and forecasting.
(CONFIDENTIAL INFORMATION REMOVED) 2025 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 2025 315,67...
AI summary The document presents demand input variables for cooling (XCool) and other uses (XOther) in the 2025 Load Forecast Report. It includes intensity values, coefficients, scaling factors, and calculated totals for both 2025 and 2035. The variables are used in econometric models to forecast load demand.
FIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 23 of 34 Small Industrial Model Statistics Model Statistics Iterations 1 Adjusted Observations 120 Deg. of Freedom for 106 Error R-Squared 0.812 Adjusted R-Squared 0....
AI summary The text presents statistical model summaries for small and medium industrial load forecasts in the 2025 Load Forecast Report. It includes metrics such as R-squared, AIC, BIC, and other statistical indicators for model evaluation.
mVarsNew.Heat_Var 1.432 0.075 19.017 0.00% mVarsNew.Cool_Var 0.865 0.160 5.397 0.00% mVarsNew.Jan_Other 1.206 0.085 14.149 0.00% mVarsNew.Feb_Other 1.179 0.100 11.737 0.00% mVarsNew.Mar_Other 1.365 0.080 17.122 0.00% mVarsNew.Apr_Other 1.5...
AI summary The text presents a series of variables and statistical values, likely related to energy load forecasting, including heating, cooling, and monthly other variables, along with their standard errors and t-values. The data appears to be part of a technical analysis for load forecasting, possibly for a regulatory proceeding.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 3 of 9 Figure C3: Firm Peak Demand Figure C4 below provides an overview of the energy forecast accuracy. For these calculations, the load of the...
AI summary The document discusses the accuracy of energy and load forecasts, highlighting that the 10-year forecast series shows an average error of just over 2 percent for a 5-year lead time, with accuracy diminishing beyond this period. Peak load forecasts show higher errors, averaging just over 5 percent for the first 5 years.
Figure C4: Energy Forecast Accuracy NSR less mills Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for: for: for: for: for: Issued 2015 2016 2017 2018 2019 2020 20...
AI summary The document presents a table titled 'Figure C4: Energy Forecast Accuracy,' comparing forecasted values (NSR less mills) across multiple years from 2014 to 2024. The data shows discrepancies between forecasts and actuals, highlighting the accuracy of energy forecasts over time.
10117 10213 2023 10266 Actuals minus Mills Percent Error 2014 0.1% 2.6% 2.0% -0.9% -1.4% 2.3% 1.4% -0.9% -3.1% -3.9% 2015 2.1% 1.7% -1.4% -2.2% 0.7% -1.0% -4.0% -7.0% -8.3% 2016 2.9% 0.2% -0.1% 3.9% 2.8% 0.6% -2.0% -2.6% 2017 -2.3% -3.1% 0...
AI summary The text presents a table showing percent errors across various years from 2014 to 2023. The data appears to relate to forecasting accuracy, likely in the context of energy or utility planning, but key details are redacted.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 5 of 9 NSR less mills Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for...
AI summary The document presents forecast statistics for NSR less mills, including lead time, number of observations, average percent error, and MAPE across different time horizons from 1 to 10 years. The data shows increasing error percentages as the forecast lead time increases.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 7 of 9 Firm Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for...
AI summary The document presents forecast statistics for firm peak load over a 10-year period, including average percent error and MAPE values for different lead times. The data indicates varying levels of accuracy across the forecast horizon, with increasing errors for longer lead times.
2185 2215 2023 2256 Actual System Peak: 2,015 2,111 2,018 2,073 2,060 2,050 1,968 2,216 2,455 2,088 Percent Error 2014 3.1% -2.3% 2.0% -0.9% -0.5% -0.3% 3.7% -8.0% -16.6% -2.0% 2015 -3.8% 0.9% -1.8% -1.6% -1.7% 1.5% -10.4% -19.6% -4.6% 201...
AI summary The text presents actual system peak values and percent error over multiple years, likely related to load forecasting. Data spans from 2014 to 2023, showing fluctuations in system peak and percent error. This information may be used for evaluating forecasting accuracy and planning energy resources.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix C Page 9 of 9 System Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: f...
AI summary The document presents a sensitivity analysis for the 2025 Load Forecast Report, using Monte Carlo simulation with economic and weather variables. The analysis is based on deterministic SAE class regression models and utilizes Oracle Crystal Ball as the Monte Carlo tool.
: 1. Once the deterministic SAE class regression models are completed, the regression coefficients are exported into the Monte Carlo tool, called Oracle Crystal Ball (MS Excel add-on). 2. The Monte Carlo process assumes that the regression...
AI summary The text describes a forecasting methodology that uses deterministic SAE regression models and Monte Carlo simulations to analyze the impact of historical variations in weather and economic drivers on load forecasts. Oracle Crystal Ball is used to run 10,000 trials to model probabilistic outcomes.
. 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 forecasting methods used to estimate system peak demand, highlighting the impact of variables on forecast accuracy. It explains how the use of the MAX function introduces skewness in the distribution and how sensitivity to monthly heating degree days (HDD) affects peak demand forecasts.
4 +/-135 +/-324 +/-161 Price Elasticity of -0.3 (2x -17 -4 -140 -10 current elasticity of -0.15) At this time, any potential impacts of the proposed hydrogen facilities on the NS Power’s Net System Requirement and System Peak are still bei...
AI summary The document discusses the 2025 Load Forecast Report, highlighting updates and improvements to the preliminary 10-year load forecast. It outlines the agenda for discussion, including changes from the 2024 forecast and ongoing work. The report also mentions the evaluation of potential impacts from proposed hydrogen facilities on NS Power’s system requirements.
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.
1 Request IR-1: 2 3 Figures 1, 2 & 3 in the application present graphs and a table with historic and forecasted 4 Net System Requirement (NSR) and System Peak. 5 6 (a) In the 2024 Load Forecast, M11689, the forecast for NSR growth in 2024...
AI summary The response explains that the 2024 NSR growth was lower due to warmer-than-normal winter weather, leading to a lower actual growth compared to the forecast. The 2025 and 2026 forecasts use normal weather assumptions, hence higher than the 2024 overestimation. The System Peak drop in 2024 was due to an extreme 2023 peak and actual conditions, with NS Power expecting a return to the forecast track.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Page 16 of the application, Historical Class Sales and Energy Data, states that data from NS 4 Power’s Advanced Meteri...
AI summary NSPI explains that integrating Advanced Metering Infrastructure (AMI) data into load forecasts requires a multi-year dataset and comprehensive customer coverage to ensure accuracy. Recent AMI data are not yet included due to ongoing integration processes that need validation to avoid reducing forecast precision.
1 2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-3: 2 3 Page 20 of the application, Weather Data, explains that the heating degree days (HDD) trend 4 is reduced over time t...
AI summary NSPI responds to NSEB's IR-3 and IR-4 requests regarding the 2025 Load Forecast Report. Adjustments to heating degree days (HDD) trends are made from the forecast's start to avoid zero HDD, with unadjusted projections reaching zero HDD in 2257. Residential customer growth is tied to housing completions, expected to decline as population growth slows post-2024 peak.
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.
25 forecast shows a significant decrease 5 in employment in 2024 and 2025. NS Power has attributed this result to Statistics Canada 6 reporting and modeling and not an actual drop in employment. 7 8 (a) Does NS Power consider the data repo...
AI summary NS Power disputes the accuracy of both the Conference Board of Canada's (CBoC) employment forecast and Statistics Canada's data, citing historical manufacturing job declines. However, Statistics Canada's data shows a 5.6% drop (2,100 jobs) between 2023 and 2024, not the 28,000+ losses claimed in the CBoC forecast.
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 1 Request IR-8: 2 3 Figure 23 shows the estimated energy and peak impacts comparing NS Power’s model to E3 and the 4 adjustments to those...
AI summary NSPI explains adjustments to energy and peak forecasts in the 2025 Load Forecast Report, attributing differences to hybrid heating inclusion in the E3 model versus NS Power's initial model. Changes in saturation and intensity assumptions between 2024 and 2025 reports are cited as reasons for updated values, with claims that incorporating hybrid heating improves forecast accuracy.
and peak values in the tables when compared to 28 2024. These values can be found in Figure 21 of the 2024 Load Forecast Report and Figure 29 24 of the 2025 Load Forecast Report. Date Filed: August 19, 2025 NSPI (NSEB) IR-8 Page 1 of 2 202...
AI summary NSPI explains changes in heat pump installation percentages in the 2025 Load Forecast Report, citing installer feedback and saturation estimates. Non-electric heat installations dropped to 66% from 68%, while electric heat installations rose to 34% from 32%, reflecting updated assumptions about market saturation and adoption trends.
cent. As the percentage of installations in previously non- 19 electrically heated properties decreases, the balance is attributed to previously electrically 20 heated properties. Date Filed: August 19, 2025 NSPI (NSEB) IR-9 Page 1 of 1 20...
AI summary NSPI explains that updated 2025 load forecasts show increased electric water heater saturation linked to higher heat pump adoption. This reflects improved accuracy through updated saturation estimates, as heat pump conversions often require electric water heaters.
1 2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 Figure 37 presents the Commercial and Industrial Electrification Forecasts. Please discuss 4 the revisions made to...
AI summary NSPI responds to NSEB's IR-13 request regarding revisions to the Commercial and Industrial Electrification Forecasts in the 2025 Load Forecast Report, noting changes are due to removing 2024 forecast numbers from the cumulative series.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Please provide an updated version of Figure 38: Comparison of Forecast to Actuals One 4 Year Ahead from the 2024 Load...
AI summary NSPI provided updated load forecast data for 2025, showing forecasted vs. actual sales, mean absolute deviation (MAD), and mean absolute percentage error (MAPE) metrics across 2022–2025. The response includes a table comparing predicted and actual load values, with variance percentages for each year.
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-15: 2 3 Page 58 of the application discusses how residential sales expectations have been estimated 4 using downward revised population growth estimates of 21,000 and new housing of 58,000 5 units. 6 7 Understanding that some...
AI summary The request questions NS Power's use of population growth and new housing estimates for residential sales forecasts. NS Power responds by explaining they use the Conference Board of Canada's housing forecasts, account for household size in modeling, and argue tourism's impact is minimal due to average annual consumption metrics.
han the annual consumption of the underlying housing stock. While new housing 27 would be built to modern standards, the average existing stock would include many older structures 28 with varying efficiencies. Date Filed: August 19, 2025 N...
AI summary NSPI defends its use of outdated EIA data for Building Shell Efficiency (BSE) forecasts, citing lack of updated 2024 data. They plan to reassess the BSE adjustment in 2026 with new EIA data from the 2025 Annual Energy Outlook.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-20: 2 3 The Atlantic Economic Council (AEC) published an Investment Outlook report: Rising 4 capital investment continues to...
AI summary The 2025 Load Forecast Report (NSEB M12349) addresses NSPI's responses to NSEB queries about housing, infrastructure, and industrial growth in Halifax. NSPI clarifies that localized grid congestion is managed through load impact studies and stakeholder collaboration, not the load forecast itself, and notes limited engagement with Irving Shipbuilding due to their classification outside large industrial customers.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 In reference to the attachments: 4 5 (a) Please confirm or explain otherwise that Attachment 8 column Q ManGDP is GDP...
AI summary NSPI confirms Attachment 8's ManGDP data represents Nova Scotia manufacturing GDP estimates (2015-2024) and explains discrepancies between annual and monthly values due to a centered moving average methodology. Differences between NSPI's figures and Statistics Canada data are noted but not fully resolved in this response.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to NSEB Information Requests NON-CONFIDENTIAL 1 (ii) The difference in Figures 17 and 18 is likely due to fact that the Statistics Canada 2 table cited above has a release date of May...
AI summary NSPI explains discrepancies in load forecast figures (17 vs. 18) due to Statistics Canada data timing (May 2025 vs. February 2025) and methodology using annual data from the Conference Board of Canada transformed via moving averages for consistency with forecast models.
N-7NSPI (Synapse) RIR 1 to 29 - Redacted
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REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests CONFIDENTIAL (Attachment Only)
AI summary The document references the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse Information Requests. The content is marked as confidential and redacted, focusing on procedural matters related to energy forecasting and information disclosure.
Figure 54 2025 LFR Attachment 4, tab Medium Industrial Figure 60 2025 LFR Attachment 4, tab DR Figure 68 Synapse IR-16 Attachment 1 Figure 69, 70 SBA IR-13 Attachment 1 CA IR-3 Partially Confidential Attachment 1, tabs Figure 71, 72 Peak D...
AI summary The document outlines the submission of the 2025 Load Forecast Report (LFR) by NSPI, including attachments and responses to Synapse information requests. Confidential information has been redacted, and electronic filings are noted for certain attachments.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-1 Attachment 3 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-1 Attachment 4 has been filed e...
AI summary NSPI responds to Synapse's questions about the 2025 Load Forecast Report, explaining that temperature, wind, and timing factors drive system peak volatility. The 14.9% 2024 decline is attributed to these factors, with a reference to NSEB IR-1 part (b) for further details.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
1 Request IR-3: 2 3 Unexplained residential forecast variance (Section 2.0, pp. 12-13; Section 5.0, p. 57) 4 5 (a) The report notes the Board’s direction in M11689 to “continue to investigate the 6 unexplained variance between forecast and...
AI summary The document addresses unexplained residential forecast variances, with NSPI explaining adjustments to heating intensities in the 2024 Load Forecast Report and concluding that other model components were not investigated due to winter-month variance patterns linked to heating.
to the conclusion that they were likely driven by the heating 27 component of the model. As a result, other components were not investigated as possible 28 causes of the variance. Date Filed: August 19, 2025 NSPI (Synapse) IR-3 Page 1 of 1...
AI summary The analysis concludes that the heating component of the model was the primary driver of variance, leading to other components not being investigated further. This is part of the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse's information requests.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-4: 2 3 Billed versus Accrued Sales and Regression Period (Section 4.0, p. 16) 4 5 (a) The report notes that it uses monthl...
AI summary NSPI responds to Synapse's queries about the 2025 Load Forecast Report (NSEB M12349), addressing data period discrepancies, model accuracy implications, and forecasting methodologies for different customer classes. NSPI highlights statistical improvements from extended data periods and discusses potential accuracy impacts for Medium Industrial forecasts.
sales to estimate the medium industrial model results in a more statistically significant 30 economic variable (P-value of 0.00 percent and T-stat of 16.34 for 18 years, vs P-value of Date Filed: August 19, 2025 NSPI (Synapse) IR-4 Page 1...
AI summary NSPI argues that using an 18-year data period for the medium industrial model improves statistical significance (P-value 0.00%, T-stat 16.34) and model fit (adjusted R-squared 0.688) compared to a 10-year period. NSPI asserts this approach enhances forecast accuracy without compromising comparability to other sectors' models.
ed by the MAPE values in the table below. These values, reproduced from 12 Appendix B, are supported by additional model performance statistics provided in that 13 appendix. 14 Class MAPE 1 (%) Residential 2.05 Small General 3.53 General 2...
AI summary The text discusses load forecast accuracy using MAPE values from Appendix B, referencing historical load factors for peak contribution calculations. It notes the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse information requests, emphasizing methodology for large customer class forecasts.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 Cloud Cover (Section 4.2.1, p. 23) 4 5 (a) Does NSPI have any concern about how accurately the SWin data from NASA,...
AI summary NSPI responds to Synapse's questions about cloud cover data accuracy, alternative sources, and modeling approaches in the 2025 Load Forecast Report. NSPI asserts no concerns about data accuracy, no alternative sources, and no need for alternative modeling due to insignificant observed impacts.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-6: 2 3 Economic Information (Section 4.3, pp. 24-27) 4 5 (a) Please refer to t...
AI summary The NSEB is requesting NSPI to explain its methodology for validating housing completions in the 2025 Load Forecast Report, address adjustments to the Conference Board’s housing forecast, clarify manufacturing employment data discrepancies, and justify the use of econometric models for industrial forecasts versus commercial models.
do not use an econometric framework? 27 Date Filed: August 19, 2025 NSPI (Synapse) IR-6 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary NSPI explains that housing completions, not population, drive new customer numbers and adjusts forecasts by 20% for Halifax's Housing Accelerator. For modeling, econometric frameworks suit industrial energy use, while commercial consumption requires bottom-up SAE approaches due to sensitivity to heating/cooling.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
1 Request IR-7: 2 3 COVID-19 Variable (Section 5.0, p. 57; Section 6.0, p. 63; Appendix B, p. 5; Attachment 05 4 EO) 5 6 (a) Please provide an explanation for the statement about the use of the COVID-19 quasi- 7 binary variable in the resi...
AI summary Request IR-7 seeks clarification on NSPI's use of the COVID-19 quasi-binary variable in residential load forecasting models. Questions focus on why the variable becomes unnecessary post-2024, how input values (1, 0.5, 0.13, 0.01) were determined, differences from prior models, and discrepancies between General rate class and residential model historical data capture.
9 variable for the years 2020-2024. 28 Date Filed: August 19, 2025 NSPI (Synapse) IR-7 Page 1 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFI...
AI summary The 2025 Load Forecast Report (NSEB M12349) involves NSPI responding to Synapse's information requests. Filed August 19, 2025, the document addresses load forecasting for 2020-2024, though specific details are redacted. The report is part of a regulatory proceeding under the Nova Scotia Energy Board.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
1 Response IR-7: 2 3 (a) In the residential model the variable is still required in the historic period. However, as the 4 annual average use has stabilized over 2023/2024/2025, there is no need to project any 5 further step changes in the...
AI summary The residential model retains the COVID-19 variable in the historic period but no longer projects future step changes due to stabilized annual average use. The 2025 COVID variable shows reduced impact compared to prior years, with diminishing effects on average kWh use, as detailed in LFR Attachments 5 for 2024 and 2025.
200 0.01 2 2025 0.65 200 0 0 2026 0.65 200 0 0 2027 0.65 200 0 0 2028 0.65 200 0 0 2029 0.65 200 0 0 2030 0.65 200 0 0 2031 0.65 200 0 0 2032 0.65 200 0 0 2033 0.65 200 0 0 2034 0.65 200 0 0 16 Date Filed: August 19, 2025 NSPI (Synapse) IR...
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse Information Requests, noting commercial model inputs use economic variables tracking post-COVID sales trends to explain reduced sales from 2020-2024.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Annual Consumption by Building Type (Section 5.0, p. 59; Appendix B, p....
AI summary NSPI explains that assumptions about energy consumption by house type in the 2025 Load Forecast Report apply only to new construction (single and multi-family units) and are incorporated into total consumption calculations, referencing Appendix B.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Electric Vehicles (EVs) ((Section 4.4.3, p. 38-41) 4 5 (a) Please provide the source data and calculations behind F...
AI summary NSPI is responding to Synapse's information requests regarding the 2025 Load Forecast Report, addressing data sources, EV types, forecasting methods, rebate impacts, and managed charging. The response includes details on EV sales forecasts, rebate elimination effects, and data incorporation from the Smart Grid Nova Scotia report.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
1 • Figure 26: sales comparison tab 2 • Figure 27: EV effect tab 3 • Figures 28 and 29: 2025 EV load forecast tab 4 5 (b) Please refer to Attachment 1, 2025 EV load forecast tab. 6 7 (c) The method for estimating the number of total EVs in...
AI summary The 2025 EV load forecast estimates EV fleet growth using Statistics Canada's vehicle registration data and S&P Global Mobility's quarterly reports. NS Power notes concerns about inaccuracies in Open Data Nova Scotia's EV registration data, citing errors in model classifications and hybrid vehicle misreporting.
PHEV), leading to uncertain reporting, and there are 24 clearly models included in the data that are Hybrid Electric Vehicles (that do not operate 25 on electricity only and cannot be plugged in to charge the battery). 1 Statistics Canada....
AI summary The text highlights data classification challenges in reporting Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs), leading to uncertainty. It cites Statistics Canada data and references the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse information requests.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
1 After estimating the current fleet, the forecast is interpolated from the policy mandate that 2 in 2035 all new light duty vehicles sold will be ZEV, but that interim goals will follow a 3 low adoption curve as there is no provincial ZEV...
AI summary The load forecast for EV adoption in Nova Scotia adjusts for ending rebates, federal carbon levy impacts, and data limitations. Projections show a 74% drop in 2025 ZEV registrations compared to prior forecasts, with policy mandates driving long-term adoption. The forecast excludes Smart Grid Nova Scotia data and relies on AMI analysis for home charging impacts.
26 to the results of the analysis of AMI data outlined in Section 4.4.3, but the impact of 27 commercial charging and medium and heavy duty vehicle charging continues to come from 28 the estimates provided by E3’s EV Load Shaping Tool, whi...
AI summary The analysis relies on AMI data and E3’s EV Load Shaping Tool to estimate impacts of commercial and medium/heavy-duty EV charging, as outlined in the 2025 Load Forecast Report (Synapse IR-9).
ENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-9 Attachment 1 Page 1 of 4 2024 EV Forecast 2025 EV Forecast Dunsky EV Low Dunsky EV High 2023 4,100 3,619 4,100 4,100 2024 7,391 6,769 5,200 8,700 EV Forecast 2025 13,368 8,...
AI summary The document presents electric vehicle (EV) forecast data from 2023 to 2035, including multiple scenarios (2024 EV Forecast, 2025 EV Forecast, Dunsky EV Low/High) with varying adoption rates and associated load impacts, as part of the 2025 Load Forecast Report.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-9 Attachment 1 Page 2 of 4 EV effect month Avg kwh/d 1 12.9 EV effect 2 11.4 13 3 9.9 4 9.7 12 5 8.4 Average kWh / day 6 9.2 11 7 11.0 8 9.9 10 9 8.2 10 10.1...
AI summary The 2025 Load Forecast Report (Synapse IR-9 Attachment 1) presents monthly average kWh usage data, highlighting the 'EV effect' on load forecasts. The table shows varying kWh/d values across months, with visual representation of trends, though specific claims or analysis are redacted.
2025 Load Forecast Report Synapse IR-9 Attachment 1 Page 3 of 4 kWh/year kW/vehicle on kW/vehicle on peak (unmanaged) Vehicles already in system at end of 2024: Peak (MW), Peak (MW), LDV 4202 0.60 0.74 BEV PHEV Energy (GWh) managed unmanag...
AI summary The 2025 Load Forecast Report details energy consumption and peak demand for various vehicle types, including Light Duty Vehicles (LDV), Medium Duty Vehicles (MDV), Transit Buses, and Plug-in Hybrid Electric Vehicles (PHEV). It provides data on energy usage (kWh/year), peak demand (kW/vehicle), and the number of Battery Electric Vehicles (BEV) and PHEVs in the system by the end of 2024.
New vehicles Energy Peak Total Cumulative Total New Cumlative vehicles in Cumulative Total Load Avg kW per Cumulative Peak (MW), Peak MW, from 2024, Peak (MW), Year BEV tot PHEV tot MDV HDV Vehicles from 2024 system GWh from 2024 (GWh) Pea...
AI summary The table presents data on the growth of battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and their impact on energy demand and peak load from 2024 to 2028, showing increasing numbers and corresponding energy consumption and peak load figures.
112.4 110 115 2035 113,483 45,424 2,256 1,199 162,363 159,663 166,163 727 718 740 107.2 0.66 106 109 153.9 152 157 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-9 Attachment 1 Page 4 of 4 Home Workplace P...
AI summary The text presents data from the 2025 Load Forecast Report (Synapse IR-9 Attachment 1), discussing peak demand by charging type (managed/unmanaged) across home, workplace, and public locations. The content is redacted, with numerical values indicating forecasted load scenarios.
30.8 85.6 14.3 7.6 6.7 18.3 8.9 19.8 132.9 44.3 101.7 20.4 10.8 8.0 26.1 12.7 23.5 194.2 64.7 118.6 29.3 15.6 9.4 37.6 18.3 27.3 287.4 95.8 136.4 42.5 22.5 10.9 54.5 26.6 31.4 429.2 143.1 155.1 62.0 32.9 12.5 79.5 38.8 35.6 REDACTED (CONFI...
AI summary NSPI explains that electricity price forecasts show a 5% annual increase from 2026-2029 based on internal Q2 2025 forecasting, with a 2% increase thereafter to approximate long-term inflation expectations due to uncertainty in revenue requirement projections.
the expected rate of inflation. Date Filed: August 19, 2025 NSPI (Synapse) IR-10 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIA...
AI summary NSPI responds to Synapse's IR-11 request regarding the 2025 Load Forecast Report (NSEB M12349), stating that the Renewable to Retail (RTR) program does not impact peak load forecasts as NSPI must serve the full peak demand of customers if required.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-12: 2 3 Prevalence of AMI (Section 10.4, pp. 88-89) 4 5 (a) Please provide the percent of customers with AMI for each of t...
AI summary NSPI provided data on AMI prevalence in customer classes for the 2025 Load Forecast Report (NSEB M12349), detailing percentages and the breakdown of the 'Rest' category.
Real Time Pricing 1 PHP 1 Municipal 5 18 Date Filed: August 19, 2025 NSPI (Synapse) IR-12 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CO...
AI summary NSPI responded to Synapse's information request regarding the 2025 Load Forecast Report and the ELCC study. NSPI provided the draft study scope, noted the timeline for the final report is not yet determined, and expects the ELCC study results may be included in the 2026 or 2027 Load Forecast.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 DSM Potential Study (Section 4.5.1, p. 53) 4 5 Refer to the following statement from the Load Forecast. “Beyond 20...
AI summary NSPI responds to Synapse's information request regarding the 2019 DSM Potential Study used in the 2025 Load Forecast Report. The study used data up to 2019, and actual DSM results for 2021-2023 show a gap between potential and actual outcomes. NSPI has not provided full details on whether an update is planned.
114 125 433 395 31,941 2,072 34,013 45,095 543 2032 349 382 141 155 536 490 39,612 2,570 42,182 53,264 646 2033 426 467 173 189 656 599 48,438 3,142 51,579 62,661 766 2034 508 556 206 225 781 714 57,700 3,742 61,442 72,524 891 2035 593 650...
AI summary The document references the 2025 Load Forecast Report (NSEB M12349) and NSPI's response to Synapse Information Request IR-16, specifically regarding the source data and calculations for the values in Figure 68. The response includes an attachment related to the 'Previous Coincidence Factor.'
0 0 0% 0% 0% 0% 0% 0% 0% 0% 0% 2024 Coincidence Factor
AI summary The text presents a table with percentages and mentions the '2024 Coincidence Factor', which may be related to energy usage patterns or load forecasting in the context of Nova Scotia's energy sector.
0 0% 0% 0% 0% 0% 0% Average Coincidence Factor Month Percent 1 0% 2 0% Adjusted to 0 as 2024 peak occurred in late February, typically would be earlier in winter in the evening. 3 2% 4 2% 5 17% 6 28% 7 27% 8 31% 9 24% 10 5% 11 0% 12 0% RED...
AI summary The document discusses the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse Information Requests. It includes data on the Average Coincidence Factor for each month, with adjustments made due to the 2024 peak occurring in late February.
al BES (5.00) kW/unit per year incremental peak impact versus equivalent uninfluenced scenario SGNS (Tesla Powerwall, 5 kW) Residential Customer Forecast Data: Year Customer Count Residential Total (December 31) 2035 561,353 Best estimates...
AI summary The document presents residential customer forecast data and load impact scenarios for 2025 and 2026, including the impact of demand response (DR) programs and distributed energy resources (DER) on peak load. The data includes customer counts and projected impacts in kilowatts, with uncertainty noted in the estimates.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-18 Attachment 1 Page 4 of 4 Regression Model Output (GWh) New Custo Hybrid AdjuSolar ImpacEV Impact (RTR Sales (GDSM Total DSM includ DSM additi Total Res Sales (...
AI summary The table presents a regression model output for the 2025 Load Forecast Report, showing various factors impacting electricity sales, including new customer growth, hybrid adjustments, solar impact, EV impact, and DSM programs, with projections from 2025 to 2035.
5131 2035 5570 464 -202 -650 429 -117 -703 -413 -290 5205 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-19: 2 3 Renewable to...
AI summary NSPI responded to Synapse's information request regarding the Renewable to Retail (RTR) assumptions in the 2025 Load Forecast Report. NSPI stated that RTR assumptions were updated with more recent estimates from the Licensed Retail Supplier and referred to Synapse IR-11 for further details on the impact of RTR on peak load forecasts.
D) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL GWh Res Comm Ind Other Losses NSR 2025 Forecast 5,289 3,135 2,258 148 777 11,607 Model 303 251 38 -77 28 544 New Customers 403 36 43...
AI summary The 2025 Load Forecast Report (NSEB M12349) includes NSPI's responses to Synapse Information Requests. It outlines various factors affecting load forecasts, including demand-side management (DSM), electric vehicle (EV) growth, and solar energy impacts, with a focus on residential, commercial, and industrial load segments.
1 Request IR-21: 2 3 Appendix A: Forecast 4 5 (a) Please provide in electronic format the specific calculations used to create the values 6 in Tables A1 and A2. If this information has already been provided in electronic 7 format in one of...
AI summary The request seeks detailed calculations for Tables A1 and A2 from the Load Forecast Report. The response refers to Attachment 4 of the 2025 Load Forecast Report and provides a breakdown of interruptible peak forecasts compared to actual values, noting that the forecast is expected to remain steady at 132 MW.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-22: 2 3 Sensitivity Analysis (Section 11.0 and Appendix D) 4 5 (a) Please provide in electronic format the data and calcul...
AI summary NSPI provided responses to Synapse's information requests regarding the 2025 Load Forecast Report, including details on the sensitivity analysis, statistical distributions used, and variables considered for future modeling.
Monthly Monthly Peak Day Month Month # HDD 18 Std Dev CDD 15 Std Dev AvgTemp Std Dev Base Wind Std Dev 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 January 1 638.68 55.07 -15.00 2.86 1...
AI summary The text presents a table containing monthly data, including heating degree days (HDD), cooling degree days (CDD), average temperatures, and peak day demand values for the years 2005 to 2024. This data is likely used for forecasting and planning purposes related to energy demand.
2.02% 3.56% 2026 1.37% 1.12% 0.09% 1.39% 0.35% 3.25% -0.04% 1.27% -0.12% 0.77% 2.62% 3.56% 2027 1.43% 1.12% -0.08% 1.39% 0.48% 3.25% 0.05% 1.27% -0.26% 0.77% 3.17% 3.56% 2028 1.39% 1.12% 0.07% 1.39% 0.49% 3.25% 0.30% 1.27% 0.00% 0.77% 3.57...
AI summary The document contains a table with percentages and years, likely related to financial or load forecasting data. It also references a redacted 2025 Load Forecast Report from Synapse, indicating the document is part of a regulatory proceeding involving energy forecasting and planning.
Shares” for details and calculations. Date Filed: August 19, 2025 NSPI (Synapse) IR-24 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFI...
AI summary The 2025 Load Forecast Report (NSEB M12349) details the HP Cool share forecast, which is expected to rise from 59.4% in 2025 to 96.4% in 2035. The report also notes no significant changes in the OtherUse variable over a 10-year span and mentions the introduction of two new binary variables in the 2025 model to correct for residual values and update the COVID-related variable.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The 2025 Load Forecast Report (NSEB M12349) details NSPI's responses to Synapse's information requests. The document is marked non-confidential and pertains to regulatory proceedings involving load forecasting and information disclosure.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-27: 2 3 Residential Water Heaters (WH) (Section 4.4, p. 37) and the “2025 LFR Attachment 01 EO 4 - Residential Intensities...
AI summary NSPI provided detailed responses to Synapse's information requests regarding the 2025 Load Forecast Report, specifically addressing the development of water heating intensity forecasts, data sources for efficiency values, and the methodology for estimating peak load impacts from electric water heaters.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-28: 2 3 Demand Side Management (Section 4.5.1, pp 53-55). 4 5 (a) Please provide the source data for the DSM values used i...
AI summary NSPI responds to Synapse's information requests regarding the 2025 Load Forecast Report, specifically addressing the source data and methodology for Demand Side Management (DSM) values used in the forecast and in the Integrated Resource Plan (IRP).
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary This document outlines NSPI's responses to information requests from Synapse related to the 2025 Load Forecast Report (NSEB M12349), which is part of a regulatory proceeding.
N-8Evidence - Synapse
29 passages
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.
hows growth slowing considerably in subsequent years, with a projected compound annual growth rate in annual sales of 0.2 percent per year over the entire forecast period, and of 1.2 percent for peak. The principal drivers of growth in bot...
AI summary NSPI's 2025 load forecast projects slower energy sales growth (0.2% annually) and reduced peak load growth (1.2% annually) due to lower EV adoption projections and expanded Renewable to Retail (RTR) market forecasts. The forecast combines statistical models, DSM adjustments, and customer growth factors, reflecting updated assumptions compared to prior years.
years when compared with the projections for the same period in the 2022, 2023, and 2024 load forecasts. 1 Nova Scotia Power, Inc. 2025 Load Forecast Report, June 27, 2025 (2025 Load Forecast). Synapse Energy Economics, Inc. Evidence Regar...
AI summary The text references Nova Scotia Power, Inc.'s 2025 Load Forecast Report and Synapse Energy Economics' analysis, comparing actual load data with projections from 2022-2024 forecasts. It cites Figure 1 on net system requirements, sourced from NSPI's report and Synapse's 2024 IR-1 responses.
ctrification Large customer projects 9 50 43 Hybrid model adjustment -202 -85 -287 Renewable to Retail (RTR) -117 -176 -128 135 -269 Demand-side management (DSM) -265 -202 -53 -3 -51 -575 2035 forecast 5,205 3,014 2,187 203 755 11,365 Sour...
AI summary The 2025 Load Forecast by Synapse Energy Economics Inc. indicates a 10.4% increase in firm peak demand, driven primarily by electrification. Key adjustments include Hybrid model, Renewable to Retail (RTR), and Demand-side Management (DSM) components, with the forecast showing slower growth due to reduced EV impact modeling.
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.
norms as not embedded in forecast variables, but instead to subtract these out at their full nominal value over the relevant period of time. 3 Table 4. DSM program savings versus forecast adjustments Forecast Forecast Forecast DSM DSM DSM...
AI summary The text discusses adjusting forecast variables by subtracting DSM program savings at their full nominal value over time, illustrated in a table comparing residential, commercial, and industrial DSM savings against forecast adjustments and coefficients.
forecast (GWh) forecast (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) 2025 65.6 71.3 12.6 38.5 47.6 27.2 36.3 2026 69.6 56.7 10.0 40.8 37.8 28.8 28.9 2027 71.6 55.7 9.8 41.9 37.2 29.6 28.4 2028 73.0 62.9 11.1 42.8 42.0 30.2 32.0 2029 73.7 52.2 9.2 4...
AI summary The text presents energy forecast data (in GWh) for multiple years and references Board directives from Matter 11689, which required NSPI to address issues in its 2025 forecast and other areas. The data originates from Synapse's 2025 Load Forecast.
dated October 22, 2024, the Board issued several directives to NSPI for its 2025 forecast and also encouraged NSPI to address or otherwise attend to multiple other issue areas in this year’s evidence. First, the Board directed NSPI to impl...
AI summary The Board directed NSPI to implement recommendations from the 2025 Load Forecast Report, including reassessing work-from-home variables, updating capacity value studies, and investigating forecast variances. It also encouraged NS Power to evaluate residential model inputs and adjust forecast classes based on CMHC data limitations.
growth targets, changes to building permitting and funding such as the Housing Accelerator, and a re-evaluation of the use of housing completions for the near term. It further encouraged NS Power to: • continue to provide a comparison of t...
AI summary The document outlines NSPI's responses to regulatory directives on energy forecasting, including revisiting EV adoption rates and TVP elasticity. It notes NSPI's general compliance with Board Decision M11689 but highlights exceptions. Synapse's 2024 recommendations for improving the 2025 load forecast are referenced, focusing on modeling methodology and data alignment.
s for each of the sectors. 2.1. Major Inputs and Regression Models In addition to changes in end-use technology, the forecast is influenced by economic, demographic, and weather-related factors. Changes in overall economic health drive the...
AI summary NSPI's forecast considers economic, demographic, and weather factors, using CBoC projections and adjusting housing completions. Electrification of heating/cooling increased residential energy use by 5.8%, while new customers added 7.6%. Regression models are deemed acceptable, though alternative models could be explored.
tomers increased the total residential by 7.6 percent. 8 The treatment of a COVID and work-from-home variable is discussed in a later section of this Evidence, and so is the treatment of DSM effects. For the commercial (General Services) m...
AI summary The document discusses NSPI's 2025 load forecast, noting a 7.6% increase in residential demand. It outlines methodological updates, including climate change-adjusted HDD/CDD trends (-16 HDDs and +1.4 CDDs annually), longer regression timescales for industrial models, and a 10-year temperature averaging approach. Commercial and industrial sector modeling uses distinct economic indicators.
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.
tial SAE model in some detail to better understand the drivers behind the forecast. 9 2025 Load Forecast, page 20. 10 2025 Load Forecast, page 61. 11 2025 Load Forecast, Appendix B, pages 1-8. Synapse Energy Economics, Inc. Evidence Regard...
AI summary The document details the SAE model's XHeat, XCool, and XOther variables, explaining their components and forecasted changes. XHeat is projected to increase 7.7% due to electric heating and heat pumps, XCool will rise 58.4% from heat pump cooling adoption, and XOther will decrease 1.3% from reduced lighting and TV use. Cites pages 20, 61, and Appendix B of the 2025 Load Forecast.
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.
SAE regression model results, and the other columns reflect various adjustments to the forecast. 12 2025 Load Forecast, Appendix B, pages 2, 8-9. 13 2025 Load Forecast, Appendix B, pages 2, 9. Synapse Energy Economics, Inc. Evidence Regard...
AI summary Synapse Energy Economics, Inc. provides evidence on Nova Scotia Power’s 2025 Load Forecast, including regression model results and a table showing residential load forecasts for 2025 and 2035. Adjustments for factors like EVs, solar, RTR, and DSM are detailed, with DSM capturing a portion of residential demand.
onversions for low- and moderate-income households. Heat pump installations in 2024 reached about 22,250 units, up by about 5 percent (about 1,000 units) from the previous year’s estimate for 2024. 17 As with NSPI’s previous forecasts, NSP...
AI summary NSPI reports increased heat pump installations in 2024 and adjusts its load forecasts for hybrid systems using E3's modeling, reducing energy and peak load projections by 2035.
These adjustments are applied annually and presented in Figure 46 of the 2025 Load Forecast report as “Hybrid Adjust.” Table 6. NSPI’s analysis of the impacts of hybrid heating on heat pump (HP) load E3 HP Change in NS Power Change in NS P...
AI summary The document discusses annual adjustments to load forecasts, highlighting discrepancies between NSPI's and E3's assumptions about hybrid heating saturation rates and efficiency. NSPI's 2025 Load Forecast shows significant energy and peak load changes, while Synapse critiques the validity of comparing models with differing assumptions.
gy Economics, Inc. Evidence Regarding Nova Scotia Power’s 2025 Load Forecast 12 Figure 3. E3 2023 study’s estimates of non-coincident peak load impacts, by scenario Source: E3. 2023. The Economics of Electrification in Nova Scotia. Figure...
AI summary Nova Scotia Power Inc. (NSPI) faces criticism for its 2025 load forecast methodology, particularly its commercial heat pump assumptions. The forecast uses an unexplained linear trajectory for electric heating stock and an unspecified U.S. Energy Information Administration (EIA) source for efficiency improvements after 2029, raising concerns about validity and transparency.
5 Load Forecast 13 improvement rate to just 0.1 percent per year, based on a source that appears to come from U.S. Energy Information Administration (EIA), but is not actually specified by NSPI. 25 NSPI’s approach to efficiency improvement...
AI summary The text critiques NSPI's load forecasting methodology for underestimating efficiency gains from heat pump adoption and relying on unspecified EIA data. It supports NSPI's planned use of AMI data to improve forecasting accuracy by analyzing end-use technologies' impacts on load shapes.
ommendations in previous years. It represents an important step toward improving the accuracy and transparency of NSPI’s load forecasting by grounding assumptions in observed customer usage patterns. Recommendations and considerations For...
AI summary The text recommends improving NSPI’s load forecasting by adjusting heat pump models with scaling factors based on E3 scenarios, developing explicit hybrid heating modeling, and validating assumptions using AMI data. It emphasizes long-term modeling improvements and supports NSPI’s commitment to analyzing AMI data for accuracy and transparency.
generation with monthly system peaks, NSPI confirmed that it had updated the coincidence factors based on 2024 data. These factors are based on both weather patterns and the timing of system peak. 39 Recommendations and considerations NSPI...
AI summary NSPI updated solar coincidence factors using 2024 data and recommends ongoing evaluation of solar projections. Solar-plus-battery systems may have limited near-term impact but warrant re-evaluation. New customer load growth is projected to increase residential demand by 7.6% by 2035. Rate design and incentives could influence solar adoption.
related to growth in customers. New customers are expected to add about 464 GWh (7.6 percent) to the residential load by 2035, a modest increase in the growth rate relative to last year’s forecast. 41 For this year’s forecast, NSPI revisit...
AI summary NSPI updated residential load forecasts for 2035, noting a 7.6% increase from new customers, with revised consumption estimates based on AMI data. Single-family home usage has risen while multi-unit consumption has declined. Synapse previously raised concerns about using housing completions as a proxy for customer growth, prompting the Board to evaluate alternatives.
page 57. 41 2024 Load Forecast, Appendix B, page 8 and 2025 Load Forecast, Appendix B, page 8. 42 2025 Load Forecast, pages 59-60. 43 2025 Load Forecast, page 60. 44 2025 Load Forecast, page 60. Synapse Energy Economics, Inc. Evidence Rega...
AI summary Synapse Energy Economics Inc. acknowledges NSPI's use of housing completions as a proxy for residential customer growth in its 2025 Load Forecast but raises concerns about the methodology's reliability due to past underestimations and a 20% upward adjustment applied to the Conference Board's forecast. NSPI defends its approach, citing historical correlation between housing completions and customer additions.
’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.
cific inputs for this variable for 2020–2024 in the residential model could be better supported, Synapse agrees with NSPI’s overall approach in phasing out reliance on the separate COVID-19 variable. 2.3. Commercial Sector The commercial s...
AI summary Synapse agrees with NSPI on phasing out the COVID-19 variable in residential load forecasts. Commercial sector load declines due to RTR adoption, lower EV forecasts, and higher solar generation. Industrial forecasts use historical data and surveys, with subsectors showing mixed trends, including flat load for large industrial customers.
r the forecast period as load migrates to RTR providers. Load for the other (large) category, which represents approximately two-thirds of the industrial load, is projected to remain essentially flat. The forecast projects increased electr...
AI summary The document discusses the forecasted load migration to RTR providers and the projected increase in industrial electrification. It notes that the industrial load is expected to remain flat for the majority of the sector, with a small increase in electrification by 2035. The forecast assumes current major customer operations and highlights uncertainties in the industrial forecast.
lly derived, however, there is necessarily some associated uncertainty. Overall, we feel that this type of approach and the proposed magnitudes of the adjustments are appropriate for this forecast. Synapse Energy Economics, Inc. Evidence R...
AI summary The 2025 peak load forecast shows a slight reduction in growth rate compared to the 2024 forecast, primarily due to lower EV adoption growth. The actual 2024 peak was significantly lower than forecasted, leading to a larger anticipated increase in 2025. Adjustments for weather, wind, interruptible load, and timing explain most of the variance between forecast and actual peak loads.
nd timing with the presence of electric heating. Research targeted at specific end uses may also allow for potential refinements of saturation and intensity values used in the forecasting SAE models.” We are very supportive of NS Power’s i...
AI summary The document discusses the use of AMI data to improve load forecasting and presents a sensitivity analysis in the 2025 Load Forecast. The analysis highlights the impact of weather, economic drivers, and DSM on energy and peak demand forecasts. The importance of evaluating hydrogen production facilities and battery adoption scenarios is emphasized.
N-9Rebuttal Evidence - NSPI
22 passages
Nova Scotia Energy Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended M12349 2025 Load Forecast Report NS Power Rebuttal Evidence November 6, 2025 NON-CONFIDENTIAL 2025 Load Forecast Report Reply Evidence Non...
AI summary The Nova Scotia Energy Board proceeding (M12349) involves NS Power submitting non-confidential rebuttal evidence to the 2025 Load Forecast Report under the Public Utilities Act. The submission addresses regulatory oversight and forecasting methodology in the context of load management.
NON-CONFIDENTIAL 2025 Load Forecast Report Reply Evidence Non-Confidential
AI summary This document is a non-confidential submission of reply evidence related to the 2025 Load Forecast Report, likely part of a regulatory proceeding involving energy forecasting and planning methodologies.
2................................................................................................................ 16 27 3.0 CONCLUSION ...........................................................................................................
AI summary The document outlines the filing of the 2025 Load Forecast Report Reply Evidence on November 6, 2025, including a conclusion section (3.0) and non-confidential status. The content appears to be a placeholder or partial submission within a regulatory proceeding.
Page 2 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential
AI summary The document is a non-confidential reply evidence submission for the 2025 Load Forecast Report, part of a regulatory proceeding. It contains no substantive content beyond the title and page reference.
1 1.0 INTRODUCTION 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules 1 4 the Nova Scotia Power System Operator (NSPSO) is required each year to provide the Nova Scotia 5 Energy Board (NSEB, B...
AI summary NS Power submitted its 2025 Load Forecast Report following a cybersecurity incident extension. The NSEB issued a Hearing Order, prompting interventions from advocates, industry groups, and energy organizations. Synapse provided positive feedback on the report's quality, while evidence and submissions were filed by interveners.
Synapse has reviewed these forecasts for many years and observes overall positive 26 trends in their expository quality. This year’s iteration shows that NSPI continues 27 to enhance the caliber of the forecast. 28 // 1 Nova Scotia Wholesa...
AI summary Synapse observes positive trends in the expository quality of NSPI's forecasts, noting continued enhancement of forecast caliber. References include market rules, NS Power's 2025 Load Forecast Report, and a 2025 NSEB Hearing Order.
Page 4 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential 1 2.0 RECOMMENDATIONS 2 3 Synapse, the CA, the SBA, and SNS/ESC all provided additional recommendations for future load 4 forecasts. NS Power’s responses to those recom...
AI summary NS Power responds to Synapse's recommendations on load forecasting, agreeing to consider trade tariffs' economic impacts and adjust heat pump load forecasts using scaling factors based on E3 scenarios. The response highlights collaboration with the Conference Board of Canada and methodological adjustments for accuracy.
tor based 31 on the difference between E3’s hybrid and non-hybrid scenarios. This adjustment 32 would allow NSPI to incorporate the mitigating effects of hybrid heating while 5 M12349, Exhibit N-8, page 27. DATE FILED: November 6, 2025 Pag...
AI summary The text references an adjustment based on E3’s hybrid and non-hybrid scenarios to incorporate hybrid heating effects for NSPI. It cites Exhibit N-8, page 27 from matter M12349, and relates to the 2025 Load Forecast Report.
Page 5 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential
AI summary This document is a reply evidence submission for the 2025 Load Forecast Report, part of a regulatory proceeding in Nova Scotia. It discusses load forecasting methodologies and data considerations.
previous Evidence, and it remains important 26 because of the forecast surge in overall electric water heater saturation. While NSPI 27 has argued that current uptake of heat pump water heaters is too low to warrant 28 separate treatment,...
AI summary The Board disagrees with NSPI's argument that low heat pump water heater adoption justifies omitting them from modeling. Explicit modeling is necessary for accuracy due to their distinct load characteristics, aligning with electrification goals and preparing for their growing market presence over the next decade.
S Power Response: 29 30 Solar installation projections and coincidence factors are updated annually. 31 8 M12349, Exhibit N-8, page 27. 9 M12349, Exhibit N-8, page 27. DATE FILED: November 6, 2025 Page 7 of 17 2025 Load Forecast Report Rep...
AI summary NS Power agrees to incorporate rate design and incentives for solar-plus-battery adoption into load forecasts but notes these will be handled separately. They commit to monitoring housing completion projections and adjusting forecasts using CBoC data. The document also references recommendations to explore industrial RTR participation impacts.
eases in industrial RTR 28 participation. 12 29 10 M12349, Exhibit N-8, page 27. 11 M12349, Exhibit N-8, page 28. 12 M12349, Exhibit N-8, page 28. DATE FILED: November 6, 2025 Page 8 of 17 2025 Load Forecast Report Reply Evidence Non-Confi...
AI summary The document references the 2025 Load Forecast Report's reply evidence, citing exhibits from M12349 and discussing industrial RTR participation. It includes page numbers and a filing date of November 6, 2025.
pies of the certification required in subsection 17(2) from each renewable 30 low-impact electricity generation facility that the licence holder owns or 31 operates; 32 33 (f) forecasts of renewable low-impact electricity generation at the...
AI summary The text outlines requirements for certification from renewable low-impact electricity generation facilities, forecasts of generation at interconnection points, and transmission/distribution loss forecasts, as part of a 2025 Load Forecast Report Reply Evidence submission.
Page 9 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential 1 such that the requirements set out in Section 10 are met. 13 2 3 According to Section 12 of the Board Electricity Retailers Regulations, the NSEB will then 4 “review...
AI summary The document discusses the NSEB's role in reviewing NSPI's compliance plan under Section 12 of the Board Electricity Retailers Regulations, emphasizing NSPI's inclusion of municipal peak demand in load forecasts due to backup capacity obligations. It also addresses a recommendation for NSPI to clarify its forecasting methodology for municipal sector energy versus peak load.
er 31 and better particulars on the information it has relied upon to date with respect to 32 the RTR in its 2025 Load Forecast. To the extent NS Power has solely relied upon 19 M12349, Exhibit N-8, page 28. 20 M12349, Document 99295, 2025...
AI summary NS Power's reliance on the RTR in the 2025 Load Forecast Report has been challenged, requiring more detailed information. The Consumer Advocate submitted evidence citing specific exhibits and documents to support this.
1 data provided by the LRS, the Consumer Advocate would request that NS Power 2 be directed to conduct its own independent analysis and provide an update to the 3 Board through a compliance filing, or alternatively, at the Board’s directio...
AI summary The Consumer Advocate requests NS Power to conduct an independent analysis and update the Board on load forecasting. The Small Business Advocate (SBA) criticizes the proposed 2% electricity price increase as arbitrary and urges NS Power to consider additional factors. NS Power responds that rate modeling is outside the load forecast's scope and that their assumptions are based on a robust internal forecast, with uncertainties increasing beyond 2029.
es, using the 21 M12349, Document 99295, page 5. 22 M12349, Document 99289, 2025 Load Forecast Report, Small Business Advocate, Submission, Sep 10, 2025, page 1. DATE FILED: November 6, 2025 Page 13 of 17 2025 Load Forecast Report Reply Ev...
AI summary Submission of reply evidence related to the 2025 Load Forecast Report, referencing documents M12349, Document 99295, and Document 99289. Filed November 6, 2025, as part of a regulatory proceeding.
Page 13 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential 1 expected rate of inflation for 2030 onward is a reasonably proxy until more certainty is achieved 2 as time goes on. Where there is an expectation of rate changes in...
AI summary The SBA criticizes NSPI's load forecast for potentially relying on outdated data, risking overstated industrial load projections. NS Power defends its assumptions as more accurate than those from CBoC and Statistics Canada. A recommendation for a DER Potential Assessment is proposed for the 2026 Load Forecast Report.
Potential Assessment is undertaken to identify the 29 growth potential of DERs, and where the grid can (or cannot) accommodate them, 30 to inform the 2026 Load Forecast Report. 24 23 M12349, Document 99295, page 2. 24 M12349, Document 9929...
AI summary The text discusses a potential assessment of DERs' growth and grid accommodation to inform the 2026 Load Forecast Report, citing documents M12349, 99295, and 99296. The assessment aims to evaluate where DERs can be integrated into the grid and their growth potential.
Page 14 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential
AI summary Non-confidential reply evidence submitted for the 2025 Load Forecast Report, likely addressing load forecasting methodologies, data sources, or stakeholder input related to electricity demand projections in Nova Scotia.
at is probable. NS Power believes the data and the 25 assumptions used in the Load Forecast are reasonable for the purpose of the Load Forecast. With 26 respect to “identifying where the deployment of DERs could bring most value”, the Comp...
AI summary NS Power asserts that the Load Forecast's data and assumptions are reasonable, arguing that assessing DERs' value lies outside its scope. The text suggests a DER Potential Assessment should inform the upcoming IRP by IESO-NS, which plans to develop its first IRP for Nova Scotia, sharing its approach early in 2026.
Section 4.4.3 of the report, the forecast for EV sales was revised downwards in response to the end 24 of government incentives for EVs, which is an up-to-date reflection of government policy. 25 26 https://ieso-ns.ca/wp-content/uploads/20...
AI summary NS Power revised its 2025 Load Forecast Report, adjusting EV sales projections downward due to expired government incentives. The report reflects methodological improvements and stakeholder input, with NS Power requesting NSEB approval. The document cites Synapse's analysis and references external filings.
100378Board Decision Letter
12 passages
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) filed its 2025 Load Forecast Report, detailing energy and demand projections for 2025–2035. The Board granted two extensions to meet the filing deadline. The proceeding includes interventions from groups like the Consumer Advocate and Energy Storage Canada, with Synapse Energy Economics Inc. reviewing the filing. Evidence submissions and rebuttals were filed by stakeholders.
Board staff. Evidence was filed by Synapse and submissions were made by the CA, SBA, and SNS & ESC on September 10, 2025. NS Power filed its Rebuttal Evidence on November 6, 2025. 2025 Load Forecast NS Power continued using two discrete me...
AI summary NS Power's 2025 Load Forecast uses SAE models and DSM adjustments to predict a 2.1% decrease in net system requirement (NSR) from 2025 to 2035. Key factors include reduced EV sales projections, increased RtR market sales, and customer solar investments offsetting growth from electrification. The forecast shows near-term NSR increases due to RtR sales changes but long-term declines due to model assumption updates.
to the assumptions contribute to offsetting the expected customer growth and electrification of space and water heating. Overall, the Report forecasts an annual decrease of 0.2% between 2025 and 2035. The forecast predicts the system peak...
AI summary The report forecasts a 0.2% annual decrease in NSR between 2025-2035, contrasting with a 28% increase in system peak demand by 2035. While NSR forecasts align closely with actual results, system peak predictions show significant variance, attributed to electrification trends and model assumption changes.
1.7% 8.6% -4.0% 2020 -0.3% -3.1% 8.9% -0.5% 2019 0.7% -1.5% 7.2% -0.6% The Load Forecast Report is a critical input to NS Power’s planning, budgeting and operational processes, including generation planning, capital program delivery, fuel...
AI summary The Load Forecast Report is critical to NS Power’s planning and rate-setting, but the Board has raised concerns about its accuracy. In Matter M11689, NS Power agreed to implement recommendations from the 2024 report, including reassessing work-from-home variables, updating capacity studies, and investigating forecast-actual variances.
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.
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.
a 2% annual increase from 2030 onwards as arbitrary. The SBA recommended NS Power incorporate measured factors to project long term sales, including load growth, generator mix, transmission upgrades. The SBA had concerns with NS Power’s ap...
AI summary The SBA criticized NS Power's load forecast methodology for being arbitrary and lacking transparency, particularly regarding electrification data. ESC and SNS recommended a DER potential assessment and lower capital cost assumptions for battery storage. Synapse praised the report but suggested improvements for future forecasts.
sidered NS Power’s Report to be very well done and noted that the Report explained the underlying factors driving the forecast. Synapse made 12 recommendations to improve future Load Forecast Reports.
AI summary NS Power's report was praised for its thorough explanation of forecast drivers, while Synapse proposed 12 recommendations to enhance future Load Forecast Reports.
1. Trade Tariffs: Analyse how 2025 U.S. trade tariffs affect the economic forecast. If the Conference Board forecast does not capture these impacts, NS Power should create its own method to incorporate the effects of tariffs. 2. Residentia...
AI summary The text outlines NS Power's recommendations for improving forecasting methodologies, including modeling trade tariffs, heat pump impacts, EV adoption, solar + battery integration, and refining housing and industrial load projections. It emphasizes data validation, scenario analysis, and alignment with emerging technologies.
for demand response. • Evaluate additional demand response programs, with a greater level of peak loads. 11. Assess the probability of “other possible scenarios” in Figure D8 and consider additional analyses aimed at mitigating projected p...
AI summary NS Power's rebuttal agrees with most Synapse recommendations but highlights constraints in implementing some, particularly regarding AMI data integration and managed charging strategies. They argue that certain analyses, like rate design, are better suited elsewhere. The discussion includes demand response, DSM, and load forecasting scenarios.
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 NS Power revised its EV forecast due to the end of government incentives. The Board agrees that its Load Forecast reports have improved methodologies and values intervenor input. NS Power must implement agreed recommendations, monitor housing completions, and investigate unexplained residential sales variance. The Board acknowledges NS Power's model adjustments for policy changes affecting electrification and technology adoption.
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
10 passages
M12349 NOVA SCOTIA ENERGY BOARD IN THE MATTER OF: THE PUBLIC UTILITIES ACT - and - IN THE MATTER OF: NOVA SCOTIA POWER INCORPORATED’S 2025 Load Forecast Report REDACTED INFORMATION REQUESTS To: Nova Scotia Power Incorporated Mark Peachey,...
AI summary The Nova Scotia Energy Board has issued a redacted information request to Nova Scotia Power Incorporated regarding their 2025 Load Forecast Report under the Public Utilities Act. Responses are due by August 19, 2025, with contact details provided for submission.
Document: 322941 Date Filed: 07/24/25 Page 1 1 Request IR-1: 2 Figures 1, 2 & 3 in the application present graphs and a table with historic and forecasted Net 3 System Requirement (NSR) and System Peak. 4 a) In the 2024 Load Forecast, M116...
AI summary The Nova Scotia Utility and Review Board requests clarification on NS Power's 2024 Load Forecast discrepancies, AMI data usage, HDD model adjustments, and housing completions. Questions focus on forecasting accuracy, data sufficiency, model parameters, and housing impacts on energy demand.
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.
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.
ure 20 for NS Power 31 HP Peak (MW) for both 2030 and 2035. 32 c) Please explain how these changes will improve the accuracy of the near term and long- 33 term forecast. 34
AI summary The text requests NS Power to explain how proposed changes will improve the accuracy of near-term and long-term forecasts, specifically referencing HP Peak (MW) values for 2030 and 2035.
Document: 322941 Date Filed: 07/24/25 Page 3 1 Request IR-9: 2 Figure 24 provides a table with the Heat Pump Forecast from 2025 to 2035. 3 a) Please explain why the % Install Non-Elec. Heat has been reduced to 66% from 68% used 4 in the pr...
AI summary The text outlines regulatory requests seeking explanations for changes in heat pump and water heater forecasts, EV load modeling assumptions, and electrification revisions. It asks for clarity on data sources, accuracy improvements, and the impact of population and housing estimates on residential sales projections.
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.
Document: 322941 Date Filed: 07/24/25 Page 5 1 a) The AEC report notes that investment in housing is up 17% and identifies growth nodes 2 in the Halifax Regional Municipality that will be developed over the Load Forecast period. 3 Does NS...
AI summary The text includes questions from intervenors to NS Power regarding capacity to address demand increases in Halifax, consideration of infrastructure projects in load forecasts, updates from industrial customers, and the methodology for using 'achievable potential' in peak demand estimates. It also references the Evergreen Integrated Resource Plan (IRP) and requests data comparisons for demand response programs.
a) Has the Evergreen IRP E1 – Current Policy and Trends scenario been updated to reflect 26 the current environmental policies and consumer incentives for electrification? 27 b) The table shows that the IRP has underestimated the 2024 and...
AI summary The text includes questions about the Integrated Resource Plan (IRP) alignment with energy demand forecasts, underestimation of demand in the IRP, and the long-term compounding effects of economic factors on energy sales. It also references a document (322941) and requests analysis of economic impacts on energy sales.
98721Synapse (NSPI) IR-1 to IR-29
11 passages
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.
tual drop 32 in employment? Has NSPI addressed the possibility that continued reliance on the 2024 33 forecast will result in an over-estimation of 2025 manufacturing employment? Please 34 explain in detail. 35 d. Why is the industrial for...
AI summary The text contains regulatory questions directed at NSPI regarding employment forecast methodologies, econometric framework discrepancies between industrial and commercial models, and the use of a COVID-19 quasi-binary variable in residential energy consumption modeling. It requests detailed explanations for model assumptions and data input choices.
ort. 3 h. Please confirm whether managed charging is incorporated into the 2025 forecast. If it is 4 not, please explain. If NS Power has forecasted managed charging, please provide this 5 information. 6 Request IR-10: 7 Price Forecast (Se...
AI summary The text outlines regulatory requests to NS Power regarding managed charging forecasts, electricity price increases, renewable-to-retail (RTR) impacts on peak load, AMI prevalence, ELCC study timelines, and DSM potential based on E1’s 2019 study. Requests focus on clarifying assumptions, data sources, and timelines for key planning documents.
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.
itivity Analysis (Section 11.0 and Appendix D) 17 a. Please provide in electronic format the data and calculations used to create Figures 74 18 and 75. 19 b. Please provide the actual statistical distributions used for the sensitivity vari...
AI summary The text includes requests for data and analysis related to sensitivity analysis, probability distributions, and model outputs, as well as inquiries about the use of P10/P90 analysis in system planning decisions.
and forecasts used to develop the probability 30 distributions for each variable used in the Monte Carlo analysis. 31 d. Please provide the source data and calculations for Figure D8.
AI summary The text requests the source data and calculations for Figure D8, which is part of a Monte Carlo analysis. This suggests a focus on transparency and methodology in the analysis, likely related to forecasting or risk assessment in a regulatory proceeding.
Date Filed: 07/28/2025 Synapse (NSPI) Page 7 of 12 1 Request IR-24: 2 Appendix B: Residential Model 3 a. Please provide in electronic spreadsheet format the data and the statistical model 4 parameters and the full data and results used to...
AI summary The text outlines requests for detailed data and model parameters related to residential and peak forecasts, including factors affecting heat pump usage, model specification changes, and customer heating system distributions. It emphasizes transparency in statistical models and forecast methodologies for regulatory review.
Date Filed: 07/28/2025 Synapse (NSPI) Page 9 of 12 1 ii. Has NS Power considered incorporating hybrid systems as a separate end-use or 2 intensity variable in the SAE model? If not, explain why. 3 iii. What steps would be required to enabl...
AI summary The document contains eight questions directed at NS Power (NSPI) regarding hybrid heating systems modeling, heat pump peak load impacts, forecast discrepancies, and methodology. Key topics include hybrid system integration in SAE models, temperature thresholds for heat pump performance, and NSPI's use of E3's analysis versus its own estimates.
asis for using E1’s 2019 Potential Study base case scenario for DSM 26 amounts beyond 2025 and how valid the use of the 2019 Potential Study is for developing 27 NSPI’s load forecast. 28 e. Please provide the inputs and calculations used t...
AI summary The regulatory body requests NSPI to justify using E1’s 2019 DSM study for load forecasts beyond 2025, explain changes in Annual DSM Savings forecasts compared to the 2024 report, and confirm price elasticity differences between CPP and TOU customers as required by the Board’s 2024 Load Forecast Decision.
100378Board Decision Letter
11 passages
to the assumptions contribute to offsetting the expected customer growth and electrification of space and water heating. Overall, the Report forecasts an annual decrease of 0.2% between 2025 and 2035. The forecast predicts the system peak...
AI summary The report forecasts a 0.2% annual decrease in Net System Requirement (NSR) between 2025-2035, contrasting with a 1.1% annual increase in system peak demand. The 2035 system peak is projected to be 28% higher than 2024 levels, driven by electrification of heating and model assumptions. Table 1 shows NS Power’s NSR forecasts align closely with actuals, while system peak predictions show greater variance.
1.7% 8.6% -4.0% 2020 -0.3% -3.1% 8.9% -0.5% 2019 0.7% -1.5% 7.2% -0.6% The Load Forecast Report is a critical input to NS Power’s planning, budgeting and operational processes, including generation planning, capital program delivery, fuel...
AI summary The Load Forecast Report is critical for NS Power's planning and rate-setting, but the Board has raised concerns about its accuracy. In Matter M11689, NS Power agreed to implement recommendations, including reassessing work-from-home variables, updating demand response studies, and investigating unexplained residential NSR variances.
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.
-4- increasing on a per EV basis more so than the previous Reports. This forecast is worth revisiting for the 2026 Load Forecast Report to confirm accuracy. In the 2023 decision, the Board did not agree with NS Power’s attribution of the C...
AI summary The document discusses load forecast variances, the Board's rejection of NS Power's pandemic and heat pump explanations for 2022 NSR discrepancies, and directives to re-evaluate unexplained variances. It notes reduced unexplained variance in 2024, declining demand projections due to RtR adoption and solar growth, and a 7.3% general demand decline by 2035.
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 discusses forecasts for electricity demand, noting a projected decline in General and Medium Industrial demand between 2025-2035. The CA recommended class-specific peak load forecasts and questioned the reliability of RtR sales projections. The SBA criticized NS Power’s arbitrary 2% annual electricity price increase assumption, advocating for data-driven long-term sales projections.
sidered NS Power’s Report to be very well done and noted that the Report explained the underlying factors driving the forecast. Synapse made 12 recommendations to improve future Load Forecast Reports.
AI summary Synapse praised NS Power’s Load Forecast Report for its thorough explanation of forecasting factors but recommended 12 improvements to enhance future reports.
1. Trade Tariffs: Analyse how 2025 U.S. trade tariffs affect the economic forecast. If the Conference Board forecast does not capture these impacts, NS Power should create its own method to incorporate the effects of tariffs. 2. Residentia...
AI summary The text outlines NS Power's initiatives to refine forecasting models for trade tariffs, heat pumps, EVs, solar, housing, industrial RtR, municipal load, ELCC, and demand response. Emphasis is placed on improving accuracy through scaling factors, empirical data validation, and separate modeling of technologies like heat-pump water heaters.
mendation #8, NS Power explained that the RtR forecast is based on the expectations developed by the Licensed Retail Supplier and is the best available information to be included in the Load Forecast. In response to Synapse’s recommendatio...
AI summary NS Power responds to recommendations regarding load forecasting, integrated resource planning, and rate assumptions. It defends including municipal utilities' full load, using internal forecasts for rate projections, and disagrees with recommendations on DER deployment and EV forecasts, citing data alignment and annual evaluation practices.
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.
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 them with bank data, track battery storage price trends, and defer some intervenor suggestions to the next Integrated Resource Plan. The Board agrees with NS Power on certain points, noting the IESO's stakeholder engagement process.