N-12025 Load Forecast Report + Appendices - Redacted
63 passages
8 4.1 Historical Class Sales and Energy Data .................................................................... 16 9 4.2 Weather Data ..........................................................................................................
AI summary The document outlines sections analyzing historical energy sales, weather data, economic factors, end-use trends (including heat pumps, EVs, solar PV), and price data. It emphasizes load forecasting, renewable integration, and demand-side management as key themes in the regulatory proceeding.
ce Data ................................................................................................................. 51 21 4.5.1 Demand Side Management .....................................................................................
AI summary The text outlines sections of a 2025 Load Forecast Report, including demand-side management, renewable energy integration, and sector-specific analyses for residential, commercial, and industrial/municipal sectors. The document is redacted, with confidential information removed.
............................................. 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.
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 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.
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.
ot necessarily correlate 24 with new customer additions in the near term. 25 9 Nova Scotia Power Inc. 2024 Load Forecast Report, UARB Decision, October 22, 2024, page 7 (M11689). DATE: June 27, 2025 Page 24 of 94 REDACTED (CONFIDENTIAL INF...
AI summary The 2025 Load Forecast Report discusses the discrepancy between actual customer additions and housing completion forecasts. The Conference Board of Canada's forecast underestimated customer additions by 20% over the past five years, leading to a +20% adjustment to their forecast for 2025–2030. Beyond 2031, the forecast remains unadjusted.
EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 15: Household Size 2 3 4 Household Size decreased steadily from 2000 to 2016 as population growth stagnated while new 5 housing continued to increase. F...
AI summary The 2025 Load Forecast Report discusses trends in household size and economic factors influencing load forecasting. Household size decreased from 2000 to 2016 and stabilized until 2022, with a slight increase followed by a decline as population growth slows. Economic models use GDP and employment data, with specific considerations for manufacturing employment forecasts based on the 2024 CBoC forecast.
er Electrification Strategy Report released in late 2023, which 23 states “Winter peak impacts on the electricity system from cold-climate heat pumps can be 24 mitigated by encouraging hybrid (mini-split) systems and best-in-class performi...
AI summary The document references a 2023 Electrification Strategy Report discussing the impact of cold-climate heat pumps on winter electricity peaks and the 2030 Clean Power Plan's goal of reducing peak demand by 150 MW through electrification, demand response, and efficiency measures. It also cites a 2024 Annual Capital Expenditure proceeding and a 2025 Load Forecast Report.
1 the demand response and efficiency programming incorporated in the Base DSM scenario, is the 2 scenario studied in the Evergreen IRP which most closely matches this objective.” 16 The forecast 3 continues to assume 100 percent heat pump...
AI summary The text discusses NS Power's involvement in assessing demand response and efficiency programming, particularly within the Base DSM scenario and the hybrid peak scenario. NS Power is collaborating with stakeholders and organizations like NRR and E1 to evaluate the cost impacts of the hybrid approach as part of the Clean Power Plan and Load Management initiative.
1 4.4.2 Water Heaters 2 3 NS Power anticipates that some customers who convert their oil heating systems to heat pumps 4 will also convert their hot water supply to electric hot water tanks because of the annual operating 5 savings. Growth...
AI summary NS Power anticipates increased adoption of electric water heaters as customers switch from oil heating systems to heat pumps. Saturation is expected to reach 90% by 2035, though the efficiency of heat pump water heaters is not yet fully reflected in the forecast due to low uptake. Efficiency improvements are expected over time through new technology and replacement of older units.
1 4.4.5 New Technologies 2 3 The 2025 Load Forecast does not assume a significant amount of distributed solar/battery storage 4 combinations or storage only deployments. The cost of home batteries is still relatively expensive, 5 in the ra...
AI summary The 2025 Load Forecast does not assume significant adoption of distributed solar/battery storage due to high costs, with gas generators being a more cost-effective solution for backup power. Vehicle-to-Grid (V2G) technology is still in development and not widely available, though some vehicles have limited capabilities. As battery technology improves, adoption may increase, but timelines are uncertain.
s and vehicle-to-grid can also discharge to support the system, 24 further capitalizing on the coordinated opportunity of distributed energy resources. For example, 25 Figure 33 below shows a range of peak mitigation based on residential c...
AI summary The document discusses the potential impact of residential battery uptake on peak load mitigation by 2035, assuming optimal utility-level control. It references a figure showing potential peak impacts under different battery adoption scenarios and notes that the estimates are based on 5 kW batteries from the SGNS project.
0 0 0 0 Control (MW) Battery Peak Impact - (1,403) (702) (281) (140) Optimal DR Control (MW) 2 3 The impacts of technologies related to direct load control (DLC) of heating and hot water loads 4 are discussed in Section 10. 5 6 4.4.6 Inten...
AI summary The text discusses the impact of technologies related to direct load control (DLC) of heating and hot water loads, referencing Section 10 for further details. It also outlines residential end-use intensities and their contribution to the forecast model, including variables such as electric furnaces, heat pumps, and water heaters.
2025 Load Forecast Report Redacted 1 Figure 36: Historical and Projected General Commercial End-Use Intensity (kWh/m2) 2 3 4 Supporting data for General commercial end-use intensities is included in Attachment 3. 5 6 For the 2025 Load Fore...
AI summary The 2025 Load Forecast Report discusses projected growth in the commercial and industrial sectors due to electrification programs aimed at reducing carbon emissions. These programs include converting heating loads to electricity and accelerating electric cooling technologies. Large industrial customers are assessed individually to enable electricity use while providing system benefits.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.5 Price Data 2 3 Price data is an input to the SAE forecasts for the residential, small general and general services 4 classes, and the price series is calc...
AI summary The 2025 Load Forecast Report discusses price data as an input for SAE forecasts, calculating real revenue per kWh and using a 12-month moving average. Electricity prices are projected to increase by 3.8% in 2025 and 5% annually from 2026 to 2029, with subsequent increases at approximately inflation rates. Price elasticities of -0.15 are applied in the SAE models.
4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 test the TVP elasticity in the load forecast as the elasticity changes over the course of the TVP 2 Pilot.”24 The TVP Pilot estimates two elasticities: 25 3...
AI summary The 2025 Load Forecast Report discusses the estimation of price elasticity for Time-Varying Pricing (TVP) rates, including own/daily price elasticity and substitution price elasticity, based on data from the TVP Pilot. The elasticities for Time-of-Use (TOU) and Critical Peak Pricing (CPP) rates are presented in Figure 39.
aily Price Elasticity -1.607 +/- 0.317 -0.017 +/- 0.173 Inter-Period Substitution Price Elasticity -0.105+/- 0.005 -0.029 +/- 0.001 18 19 The elasticity values have changed significantly from the prior report, and although the Daily Price...
AI summary The document discusses changes in price elasticity values from prior reports, noting that the Daily Price Elasticity for the TOU rate is significantly higher than previously used in load forecasts, while Inter-Period Substitution values remain similar. These elasticity values impact sales in the SAE model but are less influential than other factors like DSM and EVs.
1 4.5.1 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the use of 4 electricity in Nova Scotia, and the forecast takes the projected energy and demand savings into 5 account. Betwe...
AI summary The document discusses the role of Demand Side Management (DSM) in Nova Scotia's electricity use and forecasting, noting that DSM savings are incorporated into regression models. It highlights the challenge of double-counting DSM impacts and describes an approach to address this by introducing historical DSM savings as a load-modifying variable in the model.
1 provided they have similar characteristics (historical trend, potentially included in other inputs, 2 and some information about future impact). 3 4 In the Residential model, adding the historic DSM improves the fit of the Residential mo...
AI summary The text discusses the inclusion of historical Demand Side Management (DSM) data in load forecasting models for residential, commercial, and industrial classes. The residential model shows that 41.4% of DSM savings are not captured by other variables, while the combined model for commercial and industrial classes shows 43% of DSM impacts should be adjusted in future forecasts.
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.
1 Figure 40: Annual Forecast Residential DSM Savings (incremental) Year Forecast Forecast Forecast DSM DSM DSM DSM Residential Commercial Industrial captured by captured by Adjustment Adjustment DSM DSM savings DSM savings Residential Comm...
AI summary The table presents annual forecasts of residential and commercial/industrial DSM savings from 2025 to 2032, including captured savings, adjustments, and coefficients. It outlines the incremental impact of demand-side management programs over time.
5 43.4 32.1 30.7 24.5 2032 73.2 46.8 8.3 42.9 31.2 30.3 23.8 2033 71.3 43.4 7.7 41.8 28.9 29.5 22.1 2034 69.1 44.0 7.8 40.5 29.4 28.6 22.4 2035 66.0 42.5 7.5 38.7 28.3 27.3 21.6 2 3 The methodology used to determine the DSM coefficient onl...
AI summary The text discusses the methodology for determining the DSM coefficient, noting that it works best for consistent historical DSM levels and may need revision if future forecasts change significantly. It also mentions a third-party application for a Licensed Retail Supplier (LRS) under the Renewable to Retail (RTR) tariffs starting in 2026, subject to regulatory conditions.
able that was added in 2020 continues to be used for the years 2020-2024, but 22 has been removed from the forecast years. The variable helps to explain changes in consumption 23 patterns over the 2020-2024 time period, but it is expected...
AI summary The 2025 Load Forecast Report indicates that weather-adjusted sales in 2024 were close to forecast, but warm weather reduced sales by 104 GWh. Load is expected to decline from 2026 to 2033 due to migration to the RTR market and solar adoption, but will increase afterward due to EV load. DSM and efficiency improvements are expected to reduce sales over time.
in this class decrease 0.2 percent annually. Historical and forecast annual residential 8 sector loads are shown in Figure 43. 9 10 Figure 43: Historical and Forecast Annual Residential Sales 11 12 13 The forecast for new construction in 2...
AI summary This text discusses forecasted residential sector load growth, noting a 0.2% annual decrease in this class. It also highlights discrepancies between forecasted and actual customer growth, and provides context on population growth and housing projections in Nova Scotia through 2035.
2031 5398 351 -140 -308 92 -117 -186 5090 -451 -265 2032 5463 383 -156 -382 133 -117 -213 5112 -517 -304 2033 5486 413 -171 -467 194 -117 -240 5098 -581 -341 2034 5529 440 -186 -556 287 -117 -265 5132 -643 -378 2035 5570 464 -202 -650 429...
AI summary The text discusses the methodology used to approximate heat pump heating and cooling loads, electric baseboard, and water heater loads at the system level, referencing the NSUARB IR-12 (e) from the 2020 Load Forecast. It notes that these numbers are illustrative and do not include DSM amounts. Total DSM is adjusted for losses and allocated to the Municipal class customers.
Page 62 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 6.0 COMMERCIAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General rate 4 classes. Like the resident...
AI summary The Commercial SAE model forecasts electricity use for the Small General and General rate classes based on factors like heating, cooling, GDP, employment, and monthly HDD and CDD. The model incorporates annual end-use intensity projections and adjusts for sector-specific employment data. The impact of the COVID-19 pandemic on commercial sales is reflected in historical data, and the pandemic variable has been removed from the 2025 forecast.
he 2024 Load Forecast, the heating penetration from the 8 residential class was used as the end-use intensities are similar. 9 10 Figure 49: Historical and Forecast Annual Small General Sales 11 12 13 Please refer to Appendix B for tables...
AI summary The 2025 Load Forecast Report discusses changes in load demand, highlighting a 0.8% annual decrease in General class load over the 10-year forecast period. Factors include reduced EV load, commercial energy impacts from hybrid heating, and the influence of DSM programs and increased efficiency. Sales shifting to the RTR market and higher solar generation are expected to reduce sales significantly by 2035.
g to the RTR 4 market (-165 GWh per year), and higher solar generation will reduce sales by a further 237 GWh 5 by 2035. 6 7 Figure 50: Historical and Forecast Annual General Demand Sales 8 9 10 Please refer to Appendix B for tables with a...
AI summary The 2025 Load Forecast Report discusses the impact of Renewable to Retail (RTR) market participation and solar generation on electricity demand, projecting a decrease in sales by 7.3% between 2025 and 2035. Large General Service class forecasts are based on customer surveys and historical data, with growth expected from institutional facilities, particularly hospital expansions.
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.
1 9.0 NET SYSTEM REQUIREMENT 2 3 The NSR is the energy required to supply the sum of residential, commercial, and industrial 4 electricity sales, plus the associated system losses, within the province of Nova Scotia. Loads 5 served by indu...
AI summary The Net System Requirement (NSR) for 2024 was 11,326, with the largest variances attributed to weather and a single large customer. Forecasts indicate a slight annual decline in NSR from 2025 to 2035, driven by new customers and EV adoption, offset by solar, DSM, and RTR initiatives.
, and with solar, DSM and RTR migration 19 offsetting sales. Annual NSR is shown below in Figure 58. Forecast NSR values and the 20 contribution to NSR from the different sectors can be found in Appendix A. 21 DATE: June 27, 2025 Page 74 o...
AI summary The text discusses the 2025 Load Forecast Report, including historical and forecast annual NSR values, and provides a breakdown of the forecast components from 2025 to 2035, including contributions from various sectors such as solar, DSM, RTR migration, and EVs.
-269 DSM -265 -202 -53 -3 -51 -575 2035 Forecast 5,205 3,014 2,187 203 755 11,365 Total DSM forecast -700 -533 -94 -7 -122 -1,456 DSM captured in underlying -434 -331 -41 -4 -71 -882 models 8 DATE: June 27, 2025 Page 75 of 94 REDACTED (CON...
AI summary The document presents a table with DSM-related figures and a 2025 Load Forecast Report, though the content is partially redacted. The table includes forecast data for DSM and total DSM forecast, with negative values indicating reductions or savings.
1 10.0 PEAK DEMAND 2 3 The total system peak is defined as the highest single hourly average demand experienced in a 4 year. It includes both firm and interruptible loads. Due to the weather-sensitive load component 5 in Nova Scotia, the t...
AI summary The text discusses peak demand forecasting in Nova Scotia, including the definition of total system peak, the use of an end-use approach by NS Power since 2015, and the impact of EV charging and demand response (DR) programs on peak demand. The DR savings are based on 2022 models, with deployment targets moved to 2028.
een moved back to 2028 to align with the current program development and expected ramp- 24 up for both DLC and CPP. DR forecasts continue to use an effective load carrying capacity 25 (ELCC) of 48 percent to account for the fact that the f...
AI summary The document discusses the alignment of program development timelines with the 2028 timeframe, the use of effective load carrying capacity (ELCC) for demand response (DR) forecasts, and the distribution of a draft study scope document for the next ELCC study by NS Power in February 2025.
1 measured results from NS Power’s (CPP and TVP) and E1’s current demand response 2 programs.” 31 3 4 Annual DR totals by program are provided in Figure 60. 5 6 Figure 60: Demand Response Year Direct TVP Rate Business, Total Total Load (MW...
AI summary The document discusses demand response (DR) programs by NS Power and E1, including annual DR totals by program from 2025 to 2035. It also references a pilot project completed in 2021 and 2022 involving direct load control through water heater controls, with results detailed in E1’s 2023 Demand Response Program Final Report.
benefit of utility-managed load 11 shift events. Results from the pilot, as presented in E1’s 2023 Demand Response Program Final 12 Report, 32 indicated that the average available DR capacity per controller during the utility winter 13 pea...
AI summary The document discusses a pilot project involving utility-managed load and demand response (DR) capacity, highlighting average DR capacity during peak periods and the impact of controller removal due to quality concerns. The project was paused for the 2023/2024 season, but new controllers are being installed in 2024. References to the Effective Load Carrying Capacity (ELCC) Study and a DSM Programs Evaluation Report are included.
1 Efficient Product Installation Program. 803 controllers were installed in 2024. 33 E1 integrated this 2 pilot project into the Eco Shift program for the 2024/2025 season. 3 4 NS Power is also working with E1 on a two-phase pilot project...
AI summary NS Power is collaborating with E1 on multiple demand response (DR) pilot projects, including the installation of controllers and the Eco Shift program, to manage load for residential and commercial/industrial customers. Results from the 2023/2024 season show 8 MW of DR capacity for C&I customers and 0.1 MW for residential customers. Data from these pilots will inform future load forecasts and remain within the sensitivity analysis provided.
forecast, is 24 expected to fall within the sensitivity analysis provided in Section 11. Like the interruptible load, 25 DR programs are a resource that can be called upon if required, but they will not inherently reduce 26 demand. In reco...
AI summary The text discusses demand response (DR) programs and their role in managing peak demand, noting that while DR can be called upon during peak times, it does not inherently reduce demand. It also references the 2024 DSM Annual Progress Report (M12186) and the 2025 Load Forecast Report.
1 contributions, and finally DSM. As discussed in Section 4.4, the EV contribution to peak is 2 expected to be partially mitigated via utility managed charging. The firm peak assuming the 3 current non-coincident residential EV peak value...
AI summary The document discusses the impact of EVs and space heating on peak demand, estimating a 60 MW increase from EVs and a 46 MW reduction from space heating by 2035. It also analyzes the 2024 system peak, which occurred at 8am on February 21, with a recorded peak of 2,088 MW and a firm peak of 2,001 MW, influenced by factors like interruptible load, weather, wind, and unexplained variances.
of system peak (87 MW). The variance is largely the result of 22 related to the ELIADC load, which was reduced in response to planned dispatch requirements. 23 The remainder (-7MW) is related to variance within the underlying interruptible...
AI summary The 2025 Load Forecast Report discusses variations in system peak load, particularly related to the ELIADC load and interruptible customers. It also compares morning and evening peak loads, noting a difference of approximately 121 MW due to lighting load variations.
INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 70: Commercial End-Use Peak Shares 2 3 4 5 As with the residential class, there is a significant increase in peak contribution from EVs, 6 increasing from 0.3 percent in 2025...
AI summary The 2025 Load Forecast Report highlights a growing contribution of electric vehicles (EVs) and electric heating to peak demand in the commercial sector, increasing from 0.3% to 6.2% for EVs and from 39.8% to 42.8% for electric heating between 2025 and 2034. Other end uses, such as lighting, see a decline in peak contribution.
Page 91 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 11.0 SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, including 4 economics, weat...
AI summary The 2025 Load Forecast Report discusses the uncertainty in sales and peak forecasts due to variables like economics, weather, and DSM. A P10/P90 probability analysis using Monte Carlo simulation was conducted to estimate the probable distribution of future load, showing a range of 480-636 GWh over 10 years, influenced mainly by weather and economic factors.
Interruptible Demand Firm Net System Temp at 12hr Lag Contribution to Response Contribution Growth Peak Peak Temp Year Peak (reduction in to Peak Notes Firm Peak only, (%) (MW) MW) (MW) (MW) (deg C) (deg C) - January 6 weekday 2015 141 1,8...
AI summary The table presents data on interruptible demand and firm peak contributions to net system peak over several years, including reductions in firm peak, net system peak growth, temperature at 12-hour lag, and notes on specific dates and conditions.
IDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 4 of 34 OtherIndex is defined as: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 �𝑆𝑆𝑆𝑆𝑆𝑆𝑦𝑦 /𝐸𝐸𝐸𝐸𝐸𝐸𝑦𝑦 � 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑦𝑦,𝑚𝑚 = � 𝐸𝐸𝐸𝐸15 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 × 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚 𝑇𝑇...
AI summary The document defines the OtherIndex term as a function of saturation, efficiency, and calibration weights for various end-uses, and explains that the AvgEESavings term captures past DSM savings for the residential class, with a regression coefficient indicating that DSM activity reduces load.
d regression coefficient, b4, assesses the portion of embedded DSM activity that is already included in the billed sales information. The negative sign of b4 indicates that DSM activity reduces load. The COVID variable is a binary that sta...
AI summary The text discusses the use of regression coefficients and binary variables in modeling load impacts, including the effect of DSM activities and the influence of the COVID-19 pandemic on residential load. It also mentions the inclusion of binary shift variables to improve model fit and address anomalies in billing data.
Variable Coefficient StdErr T-Stat P-Value MStructRes.WtXHeat 0.926 0.018 51.423 0.00% MStructRes.WtXCool 1.147 0.185 6.205 0.00% MStructRes.WtXOther 0.925 0.032 29.357 0.00% MSales.AvgEESavingsProfiled -0.414 0.161 -2.577 1.14% MBin.Jan 64...
AI summary The text presents a statistical model summary with coefficients, standard errors, t-statistics, and p-values for various variables in a residential load forecasting model. The table includes variables such as heating, cooling, other usage, energy efficiency savings, and monthly bins, as well as a variable related to the Covid-2020 stepped update.
OVED) 2025 Load Forecast Report Appendix B Page 7 of 34 Residential SAE Model Fit Residential Model 2025-2035 Reconciliation The following tables provide details reflecting the changes between 2025 and 2035 forecast years. Some of the numb...
AI summary The Residential Load – Post Regression table compares residential energy usage between 2025 and 2035, showing changes in average use, new EVs, solar, RTR, hybrid, and DSM. Discrepancies exist due to conversion from monthly to annual data. The change in load factors is also outlined.
(703) (412) Change 5.8% 7.6% 7.9% -11.7% -2.2% -3.8% -5.0% -1.6% to load Res Sales = Existing Customer + New Customer + EV + Solar + RTR + Hybrid + DSM Existing customer load is calculated as Res Average Use (10,475 kWh/customer in 2025, 1...
AI summary The text discusses the calculation of residential load, including existing customer load, new customer load, and the impact of factors like EVs, solar, and demand-side management. It provides data on residential average use and its components, such as heating, cooling, and other uses, along with statistical models and variables used in the analysis.
CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 12 of 34 Small General Model Statistics Model Statistics Iterations 14 Adjusted Observations 120 Deg. of Freedom for 109 Error R-Squared 0.954 Adjusted R-Squared 0...
AI summary The Small General Demand customer forecast model is constructed similarly to the residential model, including heat pump programs within the SAE model. Adjustments outside the regression include estimates for other commercial and industrial growth programs, PV, EV, RTR, and DSM.
sidential model (including heat pump programs inside the SAE model). Adjustments done outside the regression include estimates for other commercial and industrial growth programs, PV, EV, RTR and DSM. Historically the XHeat, XCool and XOth...
AI summary The document provides a residential load forecast model for 2025 and 2035, incorporating adjustments for EVs, solar, RTR, and DSM. The model calculates load based on average use per customer and customer count, with projections showing increases in energy use despite some reductions from efficiency programs.
2025 Load Forecast Report Appendix B Page 15 of 34 Heating is calculated as [(Heat2035-Heat2025) x HeatUse x Coeff x Scaling]/WtXHeat2025 Small General Input Variables – XCool Cooling CoolUse Coefficient Scaling Total Variable Factor Xcool...
AI summary The text presents formulas and tables related to heating and cooling load calculations for a 2025 load forecast report, including variables such as HeatUse, Coeff, Scaling Factor, and CoolUseVariable. It also details changes in load from 2025 to 2035 for various components.
VED) 2025 Load Forecast Report Appendix B Page 19 of 34 General Service Model Fit General Demand 2025-2035 Reconciliation The general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total sal...
AI summary The document discusses the reconciliation of general demand load forecasts for 2025 and 2035, including adjustments for factors such as RTR, EV load, solar, and DSM. It highlights changes in load and the impact of various factors on overall demand.
6% 9.9% -9.7% -6.8% -7.3% to load Gen Sales = Sales + Model alignment (2024 actuals vs forecast) + RTR + EV Load + Solar Load + Hybrid + DSM General Demand Sales –Regression XHeat XCool XOther Binaries ARMA Sales (GWh) 2025 564,737 127,070...
AI summary The document presents load forecasts and demand modeling for 2025 and 2035, including variables such as XHeat, XCool, and XOther, along with their intensity factors and coefficients. It explains the methodology for calculating heating and cooling demand, incorporating economic and structural variables.
dium Industrial Model Fit REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 28 of 34 Combined Model for Commercial and Industrial DSM Coefficient NonResSalesm = b1×NonResEESavingsProfiledm + b2×GenWtXHea...
AI summary This section presents a combined model for commercial and industrial demand-side management (DSM) coefficients, including variables such as non-residential energy efficiency savings, weighted heating and cooling factors, and binary variables for specific months and years. The model aims to explain sales trends over time and improve fit with historical data.
19 -4.080 0.01% MBin.Jun20 -43630.113 11114.974 -3.925 0.02% MBin.Yr21to24 -6009.765 2528.720 -2.377 1.92% The coefficient on the EESavings variable represents the amount of DSM needed to explain historical sales trends beyond the changes...
AI summary The text presents statistical data related to load forecasting, including coefficients for variables like EESavings, which indicate the amount of demand-side management (DSM) needed to explain historical sales trends beyond changes in end-uses. The data includes figures for different time periods.
le, the energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coefficients found after running the sales model. ResOther can be written as: ResOtherm =ResSalesm- b1×ResXHeatm-b2×Re...
AI summary The energy sales model separates residential energy sales into weather-dependent and non-weather-dependent components, with the latter including past DSM activity. Non-weather variables are normalized to an average MW load basis, and binaries are included to account for billing issues and improve model fit.
0.7283 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 34 of 34 Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. A...
AI summary The document discusses the Peak Model Fit and its alignment with historical data, noting that while a peak DSM variable could not be explicitly included due to insignificant parameters, indirect effects of energy-related DSM are carried over into the peak model. The document also includes figures related to total energy requirement, system peak demand, and firm peak demand.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2025 Load Forecast Report Appendix D Page 6 of 8 Figure D5: Relative Sensitivity of Peak In terms of the sensitivity of the energy sales forecast to the various input variables, Figures...
AI summary The document discusses the sensitivity of energy sales forecasts to various input variables, highlighting that weather has the strongest near-term impact, while economics becomes equally important in the long term. Demand-side management (DSM) has the largest impact on both energy and peak demand, with solar, EVs, hydrogen facilities, and batteries also showing significant influence.
drogen facilities could all have a significant impact on energy and EVs, hydrogen facilities and batteries could have a significant impact on peak. Figure D8 shows the relative impact of these items. Figure D8: Relative Impact of Inputs 20...
AI summary The document discusses the potential impacts of various energy-related factors on energy and peak demand in Nova Scotia for the years 2025 and 2035, including demand-side management, solar PV, EVs, hydrogen production, batteries, and weather/economics scenarios.
ad, timing pushed out Residential COVID variable removed from forecast COVID Variable timeframe, commercial variable removed entirely 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 6 of 19 Average C...
AI summary The document discusses updates to residential load forecasting, including the removal of the residential COVID variable and the adjustment of average consumption estimates for new customers based on AMI and customer segmentation data. The forecast also incorporates the Conference Board of Canada’s housing completion forecasts, adjusted for historical underestimation.
over year change in customer number indicated that their forecast underestimated additions by around 20%. The current forecast has been adjusted accordingly over the 2025- 2030 time period. 7 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 202...
AI summary The forecast for customer numbers has been revised upward due to an underestimation of additions by around 20%. Additionally, EV load forecasts have been adjusted downward following the removal of federal and provincial EV incentives, with analysis showing a decrease in peak contribution per vehicle and an increase in annual energy consumption per EV.
15 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 16 of 19 Forecast Comparison – Peak • The system peak forecast is similar to the 2024 forecast in the near term (RTR and behind the meter solar have n...
AI summary The 2025 Load Forecast Report compares peak demand forecasts with actuals, noting similarities in the near term but a reduction in peak demand starting in 2030 due to lower EV sales. Variations between the 2024 forecast and actuals are outlined, including impacts from weather, wind, lighting, and other factors.
N-7NSPI (Synapse) RIR 1 to 29 - Redacted
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ailable for inclusion in the 2026 Load Forecast. However, NS Power 27 expects the results of the study will be available for inclusion in either the 2026 or 2027 28 Load Forecast. Date Filed: August 19, 2025 NSPI (Synapse) IR-13 Page 1 of...
AI summary The document discusses the need to update the Effective Load Carrying Capability (ELCC) study, focusing on the interactive effects of clean resources such as wind, solar, battery energy storage, and demand response. This was highlighted in a Board decision and supported by NS Power in its 10 Year System Outlook.
nd, solar, battery energy storage, and demand response). Subsequently the Board asked for an update on NS Power’s progress on this recommendation in NSUARB IR-02 (M11764). NS Power’s response stated: NS Power has not started a new capacity...
AI summary The NSEB requested an update on NS Power’s progress on a recommendation from NSUARB IR-02 (M11764). NS Power has not initiated a new capacity value study but plans to update the existing one prior to the next IRP analysis, focusing on the interactive effects of clean resources like wind, solar, battery storage, and demand response. A draft scope of work will be circulated for stakeholder input.
Updated examination of the ELCC values for demand response programs using program details and measured results from NS Power’s (CPP and TVP) and E1’s current demand response programs. 3. Assessment of the seasonal ELCC contributions for so...
AI summary The document outlines an updated examination of ELCC values for demand response programs using data from NS Power and E1, and an assessment of seasonal ELCC contributions from solar, wind, hydro, and storage during summer. The update is driven by stakeholder requests and the need to align with current programs in Nova Scotia.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL Year Potential Study, Base (GWh) Actuals (GWh) 2024 124 173 1 2 The totals over the time period in question are similar. 3 4 (c-d) As t...
AI summary The document discusses the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse Information Requests. NSPI refers to the Net Metering Report (M12165) for data on solar PV capacity and capacity factors. EfficiencyOne is noted as the demand side management franchise holder in Nova Scotia.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 or commercial net metering, which is a banked energy credit system and not direct 2 monetary compensation. 3 4 (e) Confirmed. 5 6 (f)...
AI summary The 2025 Load Forecast Report discusses the impact of distributed energy resources (DER) on peak demand, including the influence of demand response programs and residential battery energy storage systems. The report estimates a potential incremental peak impact of -5.00 kW/unit per year under a demand response scenario involving Tesla Powerwall systems.
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.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 Appendix D: Forecast Sensitivity Analysis 4 5 (a) Please provide the model outputs in electronic form. 6 7 (b) Ple...
AI summary NSPI has responded to Synapse's information requests regarding the 2025 Load Forecast Report, providing model outputs, Oracle input data, historical data, and source calculations for specific figures. The response includes references to other information requests for detailed calculations related to solar, EV, battery impact, and P10/P90.
0.27 † Economics 0.381 0.58 Wind at Peak 0.032 0.1 Sensitivity: 2026 Peak Sensitivity: 2026 No DSM Total Sales Sensitivity: 2035 No DSM Total Sales 1% 3% † Monthly HDD † Monthly CDD † Economics 8% 38% 49% 47% 17% 47% 15% 75%
AI summary The text presents a sensitivity analysis related to wind energy at peak times and total sales under different scenarios, including the impact of demand-side management (DSM) and economic factors. It also references monthly heating degree days (HDD) and cooling degree days (CDD) as variables affecting energy demand.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 Residential and Commercial Heating and Heat Pumps (Section 4.4, pp 30-35) 4 5 (a) Refer to Figure 20. Please provi...
AI summary The document is a response from NSPI to Synapse Information Requests related to the 2025 Load Forecast Report. It includes detailed requests for data on residential and commercial heating and heat pump usage across various forecast years, including customer numbers and heating technology breakdowns.
1 (v) For the hybrid heating scenario, how did E3 model switching behavior 2 between electric and fossil backup heat? What temperature threshold, if any, 3 was assumed for backup system use? 4 5 (vi) Please explain how NSPI estimates its H...
AI summary The text consists of a series of questions directed at Nova Scotia Power Inc. (NSPI) regarding its modeling of heat pump behavior, estimation of heating intensities, and the discrepancy between energy and peak forecasts. The questions focus on assumptions, data sources, and validation of models related to hybrid heating systems and residential energy use.
conducted any surveys or studies to confirm actual heating 29 system usage post-installation for hybrid heating systems? If so, please provide 30 the surveys or studies. Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 4 of 10 REDACTE...
AI summary The document requests information on whether NS Power has conducted surveys or studies on hybrid heating system usage post-installation and whether AMI data has been used to analyze heat pump load patterns. NSPI responds by referring to Attachment 1 and explains assumptions made regarding backup heating sources for heat pumps.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (vi-viii) The “Fossil Fuel” category in Attachment 1 includes all fossil fuel types but does 2 not distinguish between them. The pred...
AI summary NSPI's responses to Synapse Information Requests discuss the 'Fossil Fuel' category in the 2025 Load Forecast Report, noting that oil is the predominant fossil heating type in Nova Scotia. It also outlines the scope of the Hybrid Heating Study, which aims to assess the potential of hybrid heating programs to manage winter peak demand as clean energy and electrification expand.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) Please refer to Figure 24 of the report. 2 3 (f) 4 (i) For Figure 21, please refer to Attachment 1, HP Stock tab. For Figure 22,...
AI summary NSPI provides responses to Synapse Information Requests regarding the 2025 Load Forecast Report, referencing specific figures and attachments. The report discusses the use of the E3 model for forecasting hybrid heating systems and heat pump impacts, citing the 'Current Trends Hybrid' scenario and peak temperature assumptions.
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).
or Annual DSM Savings in the 2025 LFR 17 compared to the 2024 LFR, except for the addition of a forecast value for the year 2035 18 and updated coefficients in the regression model. Date Filed: August 19, 2025 NSPI (Synapse) IR-28 Page 2 o...
AI summary The document discusses NSPI's response to Synapse's information requests regarding the 2025 Load Forecast Report. NSPI explains that TVP elasticity was not directly tested in the load forecast and confirms that price elasticity for CPP customers is lower than for TOU customers, citing a previous evaluation report.
N-8Evidence - Synapse
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Evidence Regarding Nova Scotia Power’s 2025 Load Forecast Evidence RE: M12349 Prepared for the Nova Scotia Utility and Review Board September 10, 2025 AUTHORS Kenji Takahashi Aidan Glaser Schoff Selma Sharaf Ben Havumaki 485 Massachusetts...
AI summary The document provides evidence on Nova Scotia Power’s 2025 load forecast, prepared by Synapse Energy for the Nova Scotia Utility and Review Board. It includes sections on forecast comparisons, sector and DSM overviews, Board directives from Matter 11689, and previous forecast recommendations.
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.
The 2025 forecast predicts a 242 gigawatt-hour (GWh) decrease—2.1 percent—in the net system requirement (NSR) from 2025 to 2035. NS Power lays out the components of this change in Figure 59 of its report, as shown in Table 1. The “Model” g...
AI summary The 2025 forecast predicts a 2.1% (242 GWh) decrease in net system requirement (NSR) from 2025 to 2035, driven by electrification, EV growth, and new customers, offset by rooftop solar, DSM, and RTR sales. Hybrid heating adjustments further reduce NSR.
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.
ng energy use and, to a lesser degree, peak loads. Specific effects appear in Figures 40 and 59 of the Report. Overall, NSPI projects that DSM will reduce the 2035 load by 575 GWh, or about 5 percent. It should be noted, however, that the...
AI summary NSPI projects DSM will reduce 2035 load by 575 GWh (5%), but modeled savings are adjusted due to historical embedded effects from prior DSM programs. Residential and commercial sectors require 58.6% and 43% adjustment factors, respectively, to avoid double-counting. Synapse acknowledges NSPI’s approach to subtract incremental savings above historical norms.
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.
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.
t residential load, accounting for the load-reducing effects of DSM programs, decreases by about 1.6 percent over the forecast period. Without DSM programs, the increase would be about 3.9 percent. 10 The two largest contributors to the in...
AI summary Residential load is projected to decrease by 1.6% with DSM programs, versus a 3.9% increase without them. Key drivers include new customers (7.6% growth) and EV load (7.9% growth), partially offset by solar PV and DSM. The forecast uses a regression-based SAE model incorporating heating, cooling, work-from-home trends, and time-fixed effects.
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.
ghtforward. But given these disparities—and the likelihood of additional differences in efficiency assumptions—it is not valid to estimate hybrid effects by directly comparing the two models’ outputs. A better method would be for NSPI to r...
AI summary The text critiques NSPI's approach to estimating hybrid heating impacts on load forecasts, advocating instead for scaling its models using E3's hybrid heating analysis. It highlights a 40% lower peak load impact in dual-fuel scenarios and emphasizes ensuring consistency in heat pump saturation levels across scenarios for accurate adjustments.
tize this effort as it works to refine its modeling of electric heating impacts. 25 2025 LFR Attachment 03 EO – Commercial General Intensities. “Efficiency” tab. 26 2025 Load Forecast, page 90. Synapse Energy Economics, Inc. Evidence Regar...
AI summary Synapse Energy Economics Inc. analyzes Nova Scotia Power’s 2025 load forecast, highlighting assumptions about electric water heater adoption, projected increases in XOther due to water heating usage, and the exclusion of heat pump efficiency improvements despite rebate programs. The analysis emphasizes the need for monitoring these assumptions and their impacts on residential energy demand.
ater heaters are not incorporated into the forecast, citing still-limited uptake (163 installations in 2022 and 141 in 2023, per EfficiencyOne’s DSM evaluation) despite an available rebate of $400. 30 On the demand response side, NSPI and...
AI summary The document discusses NSPI's load forecasting challenges, noting limited adoption of heat pump water heaters despite rebates. It highlights NSPI and EfficiencyOne's collaboration on direct load control of water heaters, with pilot results showing peak demand reductions. The recommendation emphasizes modeling heat pump water heaters separately in forecasts due to their distinct load characteristics, countering NSPI's argument about low current uptake.
ix B, page 9 28 2025 Load Forecast report, Section 4.4.2, pages 37; Section 10.0, pages 77–78. 29 2025 Load Forecast report, Appendix B, page 9 30 2025 Load Forecast report, Section 4.4.2, page 37. Synapse Energy Economics, Inc. Evidence R...
AI summary Synapse Energy Economics Inc. provides evidence on Nova Scotia Power's 2025 Load Forecast, noting reduced EV adoption projections due to expired incentives and the 2025 consumer carbon levy. NSPI adjusted forecasts based on 2025 Q1 sales data, resulting in a 74% decrease in 2025 EV registration projections compared to prior forecasts, lower than scenarios in Dunsky Energy+Climate Advisors' analysis.
umers. NSPI states that this development “is considered but not used directly in the forecast,” as there has not been enough time to collect data on the impact of the policy change on sales volumes.33 The forecast predicts that there will...
AI summary NSPI forecasts over 160,000 EVs in Nova Scotia by 2035, projecting 718 GWh energy load and 106-152 MW peak load impacts. The forecast uses AMI data for at-home charging but relies on E3’s EV Load Shaping Tool for commercial and heavy-duty vehicle assumptions. NSPI notes limited data on policy impacts and uncertainty around managed charging assumptions.
t, page 38. 33 Response to Synapse IR-9(e). 34 2025 Load Forecast, Figure 29. 35 2025 Load Forecast, Figure 29. 36 2025 Load Forecast, Figure 3, Figure 29, Figure 65. 37 Response to Synapse IR-9h. Synapse Energy Economics, Inc. Evidence Re...
AI summary Synapse recommends NSPI monitor EV sales impacts, adjust forecasts, detail managed charging assumptions, and develop incentives for managed charging as EV adoption grows. Solar generation forecasts show increased installations and a projected 1,023 GWh load reduction, with updated coincidence factors based on 2024 data.
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.
’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.
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.
the Report.52 The forecast rates of growth in electrification appear to be generally consistent with those in the previous year’s forecast, though this year’s projections are slightly lower overall. 2.6. The Municipal Sector The municipal...
AI summary The municipal sector's energy load is projected to increase slightly over time, though its forecasted energy use is questioned due to assumptions about third-party service. DSM savings remain modest, with residential savings accounting for a small percentage of total load, and historical effects influencing model results.
effects that are already embedded in the model results. To account for past savings that are embedded in the residential results, the residential statistical model includes the variable AvgEESavings. The coefficient has been moderately var...
AI summary The document discusses the statistical modeling of demand-side management (DSM) savings in residential and commercial/industrial sectors, noting variations in adjustment coefficients over time and the impact on load forecasts. The residential sector is projected to have -265 GWh of DSM savings by 2035, while the commercial and industrial sectors have -202 GWh and -53 GWh respectively in 2025. The approach is deemed reasonable despite some uncertainty.
17 -37 -48 4 105 -114 2,559 132 2,729 coincident 8 EV peak) Source: Figure 65 in the 2025 Load Forecast. Table 8. 2024 Peak contribution components (MW) Modeled Res. heat C&I Large Firm Inter. System EV DR Hybrid DSM peak peak elect. cust....
AI summary The text presents data on peak contribution components for 2024 and 2034, including residential heat, electric vehicle (EV) demand, demand response (DR), and other factors. It highlights the impact of EVs on peak demand and provides comparative figures for scenarios with and without EV mitigation.
no EV 2,505 13 281 -37 -68 4 115 -145 2,670 147 2,851 mitigation) Source: Figure 61 in the 2024 Load Forecast. Synapse Energy Economics, Inc. Evidence Regarding Nova Scotia Power’s 2025 Load Forecast 23 3.1. Electric Vehicles The contribut...
AI summary The document discusses the 2025 load forecast for Nova Scotia Power, noting a decrease in the contribution of electric vehicles (EVs) to peak demand compared to the 2024 forecast. It also raises concerns about the modeling approach for hybrid heating scenarios and water heater penetration, suggesting that these factors introduce uncertainty into peak demand forecasting.
modeled peak resulting from these issues in the approach to assessing water heater and heat pump energy and peak effects, since NS Power uses an end-use approach to formulating its peak forecast. 57 3.3. Demand-side Management To develop i...
AI summary The document discusses NS Power's approach to forecasting peak load reductions from demand-side management (DSM) programs, including the use of an effective load carrying capacity (ELCC) of 48% and the adjustment of DSM targets from 2025 to 2028. It also references the Board's directive to update the Capacity Value Study for demand response and the potential challenges in incorporating the next ELCC study into the 2026 Load Forecast.
ends to focus on demand response in the next ELCC study. 62 In responses to discovery, NS Power clarified that it may not be possible to incorporate the next ELCC study into the 2026 Load Forecast. 63 We appreciate that NS Power is elevati...
AI summary The text discusses concerns regarding Nova Scotia Power's (NS Power) use of the Energy Load Contribution Credit (ELCC) factor for demand response (DR) in its load forecasts. Synapse Energy Economics Inc. (Synapse) raises concerns about the outdated basis of NS Power's ELCC assumptions and the lack of consideration for interactive effects and future electrification impacts. Recommendations are made for NSPI to conduct a portfolio ELCC analysis and consider more demand response programs.
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.
2035). NSPI is continuing to evaluate the potential impacts of the proposed hydrogen facilities and should continue to also assess the plausibility of all the listed scenarios and their combinations. As we did last year, we note that NSPI’...
AI summary The document highlights the need for NSPI to evaluate a range of future scenarios, particularly for heat pumps, EVs, DSM, and demand response, due to uncertainties in their adoption rates. It recommends developing low and high case scenarios in addition to the reference case for more accurate load forecasting.
Evidence Regarding Nova Scotia Power’s 2025 Load Forecast 26 5. QUESTIONS AND RECOMMENDATIONS In this Evidence we ask for further clarifications and make several recommendations: 1. The impacts of trade tariffs are an ongoing uncertainty....
AI summary The document requests clarifications and recommendations regarding Nova Scotia Power’s 2025 load forecast, emphasizing the need to account for trade tariffs and improve heat pump modeling. It suggests incorporating trade tariff impacts into economic forecasts and refining heat pump load assumptions using AMI data for greater accuracy and transparency.
ces. We strongly support NSPI’s new commitment to analyzing AMI data and encourage the utility to prioritize this effort as it works to refine its modeling of electric heating impacts. 3. We recommend that NSPI begin modeling heat pump wat...
AI summary The text emphasizes the importance of modeling heat pump water heaters as a separate end use technology in load forecasts, monitoring the impact of the carbon levy removal on EV sales, and updating solar installation projections. These actions are recommended to improve forecast accuracy and align with electrification goals.
ign or other programmatic options. 5. NSPI should continue to evaluate and update its solar installation projections and coincidence factors for solar so they align with the latest data. 6. NSPI should begin to incorporate the impacts of r...
AI summary The text outlines several recommendations for NSPI regarding the accuracy and comprehensiveness of its load forecasting and analysis, including updates to solar projections, incorporation of rate design impacts, and scenario analysis for uncertain technologies and programs.
in particular heat pumps, EVs, DSM, and demand response, we again recommend that NSPI develop a few different scenarios (e.g., low case and high case) in addition to the reference case. We support NSPI’s ongoing efforts to improve the tran...
AI summary The text discusses recommendations for improving NSPI's load forecast, particularly regarding the modeling of heat pumps, EVs, DSM, and demand response. It suggests exploring different scenarios and increasing DSM levels, as well as modeling heat pump water heaters as a separate end-use technology.
of making a simplified adjustment based on E3’s hybrid scenario. 3. We recommend that NSPI model heat pump water heaters as a separate end-use technology in the next load forecast. 4. NSPI should carefully monitor EV adoption and update it...
AI summary The document outlines several recommendations for NSPI, including modeling heat pump water heaters as a separate end-use technology, monitoring EV adoption, updating load forecasts with empirical analysis, examining solar generation coincidence factors, investigating battery storage incentives, and validating the use of new home construction as a proxy for customer growth.
ially for peak management. 7. NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. 8. We ask that NSPI reassess its modeling approach...
AI summary The document outlines several requests for NSPI to refine its load forecasting and modeling approaches, including validating proxies for customer growth, reassessing the impact of the pandemic on residential load, and considering the effects of solar, DSM, and industrial electrification on load forecasts. It also emphasizes the need to explore real-time rates and time-of-use rates to manage peak load increases.
gs. 16. We ask NSPI to investigate what can be done with time-of-use rates and other measures to mitigate the peak load increases for all these components, especially for the C&I sectors. 17. NSPI should conduct an analysis of portfolio EL...
AI summary The text outlines several recommendations for NSPI regarding load management, including investigating time-of-use rates, analyzing ELCC values for demand response, evaluating impacts of electrification and EVs, and developing scenarios for uncertain future technologies such as heat pumps and demand-side management.