N-12025 Load Forecast Report + Appendices - Redacted
67 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.
............................................. 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.
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.
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.
l system monthly energy and monthly demand data is 23 derived from system hourly load data for the period January 2015 to December 2024. Large 24 customer peak demand is forecast separately. 25 DATE: June 27, 2025 Page 16 of 94 REDACTED (C...
AI summary The document discusses the derivation of system load data from 2015–2024 hourly measurements and the use of Heating Degree Days (HDD) and Cooling Degree Days (CDD) to model weather impacts on energy demand. It notes a declining trend in annual HDD due to climate change, affecting long-term load forecasting.
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.
94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.3 Economic Information 2 3 Economic and other provincial statistics used in the load forecast are from the Conference Board 4 of Canada’s 20-year forecas...
AI summary The 2025 Load Forecast Report uses economic data from the Conference Board of Canada's 20-year forecast, including housing completions, to predict residential customer growth. The NSUARB directed a re-evaluation of housing completions due to population growth targets and housing initiatives like the Housing Accelerator.
Page 27 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The document is a redacted portion of the 2025 Load Forecast Report, which outlines projections for electricity demand in Nova Scotia. Due to confidentiality, specific details and data are not disclosed in this section.
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.
2032 4,089 -7.7 21,660 0.6 2033 3,753 -8.2 21,813 0.7 2034 3,431 -8.6 21,979 0.8 2035 3,150 -8.2 22,136 0.7 15‐24 9.3 2.1 25‐35 -8.6 0.4 5 6 DATE: June 27, 2025 Page 28 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast R...
AI summary The document contains a redacted section of the 2025 Load Forecast Report, which includes data on load forecasts for various years and age ranges, though specific details have been removed due to confidentiality.
Page 29 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary This document is a redacted section of the 2025 Load Forecast Report, which likely contains confidential information related to energy demand projections for the year 2025.
1 items to be tracked more directly to help fine-tune future forecasts. The PV forecast has been 2 updated based on actual installations in 2024. Key end uses are discussed individually below. 3 4 As with the 2024 Load Forecast, the foreca...
AI summary The document discusses the growth of heat pump usage in Nova Scotia, citing factors like grants and financing programs, and references the hybrid adoption scenario used in forecasts. It also notes the update of the PV forecast based on 2024 installations and the use of E3's load shape forecasts for space heating and EVs.
94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The 2025 Load Forecast Report provides an analysis of expected electricity demand for the year 2025, though specific details have been redacted due to confidentiality.
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.
2035 225,606 66 34 80 3,945 96 358 5 6 The overall heating intensity is similar to the 2024 forecast, with higher intensity by 2035 as a 7 result of a higher number of installs. 8 DATE: June 27, 2025 Page 36 of 94 REDACTED (CONFIDENTIAL IN...
AI summary The 2025 Load Forecast Report discusses heating intensity trends, noting similar levels to the 2024 forecast but projecting higher intensity by 2035 due to an increased number of installations.
6 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, though specific details have been redacted due to confidentiality.
1,897 2035 90 1,916 17 18 2023 DSM Programs Evaluation Reports, Residential Efficient Product Rebates Program, EfficiencyOne, Table 17, page 20. DATE: June 27, 2025 Page 37 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Foreca...
AI summary The 2025 Load Forecast Report discusses the impact of the elimination of federal and provincial EV incentives in 2025, except for medium and heavy-duty vehicles, leading to a slower growth in EV sales and a revised forecast of over 160,000 EVs in Nova Scotia by 2025.
E3, including the impact to LDV load and peak from 15 both at-home and public charging (assumed to add 10 percent to annual energy and 0.2 kW/vehicle 16 to peak based on the E3 load shapes). 17 DATE: June 27, 2025 Page 40 of 94 REDACTED (C...
AI summary The document discusses the impact of electric vehicle (EV) charging on load and peak demand, including both at-home and public charging scenarios. It assumes a 10 percent increase in annual energy use and an additional 0.2 kW/vehicle to peak demand based on E3 load shapes.
Page 40 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. It includes redacted information, indicating that sensitive or confidential data has been removed from the document.
Page 42 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. It includes redacted information, indicating that sensitive or confidential data has been removed from the document.
s show in Figures 8 31 and 32 below. 9 DATE: June 27, 2025 Page 43 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 31: Average Winter Profiles 2 3 4 Figure 32: Average Summer Profiles 5 6 DATE:...
AI summary The document contains redacted figures related to the 2025 Load Forecast Report, specifically Average Winter and Summer Profiles, as part of a regulatory proceeding in Nova Scotia.
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.
Page 46 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 • Other: all other major appliances (stoves, dishwashers, clothes washers and dryers, 2 televisions) as well as smaller appliances such as compu...
AI summary The 2025 Load Forecast Report discusses residential end-use intensity trends, noting an increase in heat pump usage and its impact on heating and cooling demand, a slow increase in electric water heating, and a decline in lighting and refrigerator/freezer usage due to improved efficiency. The 'Other' category shows a decline due to reduced EV sales and increased solar PV generation.
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.
pplier licence issued by the NSUARB included a condition that no sales can occur before the effective date prescribed by the Governor in Council. In addition, it was a condition of the license DATE: June 27, 2025 Page 55 of 94 REDACTED (CO...
AI summary The document discusses the 2025 Load Forecast Report, which outlines energy production and load distribution for the RTR market. It includes a forecast of 500 GWh of wind energy production by 2027 and details the load distribution across customer classes, with NS Power providing top-up energy under the Energy Balancing Service tariff.
Page 56 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. It includes key assumptions, methodologies, and data sources used to estimate future load requirements. The report is redacted and contains confidential information.
Page 58 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The 2025 Load Forecast Report provides an analysis of expected electricity demand in Nova Scotia for the year 2025. It includes projections and considerations related to load forecasting, which are essential for planning and managing the electricity grid.
Page 59 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 analysis of projected electricity demand in Nova Scotia for the year 2025.
ss customers. 30 These amounts are embedded in the Regression Model Output column through the end use efficiency estimates (i.e. the amount not captured by the DSM coefficient). DATE: June 27, 2025 Page 61 of 94 REDACTED (CONFIDENTIAL INFO...
AI summary The text discusses the 2025 Load Forecast Report, which includes illustrative contributions of specific end uses such as heat pumps, baseboard heat, and water heating from 2025 to 2035. The data shows increasing usage trends for various heating and cooling applications over time.
ebound 19 in 2022. The COVID variable that was used in the General rate class model in prior years has 20 been removed in the 2025 forecast as the historic data captures the impact to sales. 21 DATE: June 27, 2025 Page 63 of 94 REDACTED (C...
AI summary The 2025 Load Forecast Report discusses changes in load forecasts, noting that the impact of RTR and solar has increased, while EV load has decreased, leading to lower growth compared to the 2024 forecast. The Small General Service load is forecasted to grow at 1.5% annually, slightly lower than the 1.8% in the 2024 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.
Page 67 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 7.0 INDUSTRIAL AND MUNICIPAL SECTORS 2 3 The forecast models for the Small Industrial and Medium Industrial classes are econometric-based 4 mode...
AI summary The 2025 Load Forecast Report discusses the forecast models for the Small Industrial and Medium Industrial sectors, which are based on economic variables such as provincial manufacturing GDP and employment. The Small Industrial class is expected to grow slightly annually due to economic growth, offset by a shift in load to RTR.
Page 68 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 7.2 Medium Industrial 2 3 Figure 54 depicts historical and projected sales for the Medium Industrial class. Load in this class 4 has been flat s...
AI summary The 2025 Load Forecast Report discusses historical and projected sales for the Medium Industrial class, noting flat load since 2014 and a projected decline due to migration to the RTR market. Other Industrial rate classes are also outlined, with forecasting methods involving customer surveys and historical data.
Page 69 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 electricity requirements over the next three-year period. Details on planned production levels or 2 equipment changes help inform expectations o...
AI summary The 2025 Load Forecast Report discusses electricity requirements over the next three years, noting that load levels are expected to be flat. However, one major customer is forecast to increase load by a significant amount in 2025. Load migration to the RTR market is expected to reach 34 GWh by 2027, though there is uncertainty around new industrial projects and their impact on load growth.
Page 71 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 energy under the Wholesale Market Backup/Top-Up (BUTU) Tariff in cases where their third- 2 party supply is unavailable or interrupted. Their lo...
AI summary The 2025 Load Forecast Report discusses the inclusion of municipal electric utilities' peak demand in the load forecast due to NS Power's requirement to provide backup capacity. It also addresses system losses and unbilled sales, with system losses expected to remain between 6.0 and 7.0 percent over the next decade.
, 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.
Page 75 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 in Nova Scotia for the year 2025, though specific details are redacted due to confidentiality.
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.
Page 76 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted
AI summary The 2025 Load Forecast Report has been redacted, indicating that confidential information has been removed. The content of the report is not available for review due to confidentiality restrictions.
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.
Page 78 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 For the 2025 Load Forecast, as discussed in Section 4.2, the assumed peak temperature inputs use 2 a lagging 12-hour average as well as windspee...
AI summary The 2025 Load Forecast Report discusses the methodology for calculating peak load, including the use of temperature lag, wind speed, and historical load factors. It notes that the forecast system peak is expected to increase by 1.2% annually from 2025 to 2035, with a near-term similarity to the 2024 forecast and a long-term reduction due to lower EV peak impact.
Page 79 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 62: Historical and Forecast System Peak (no DR) 2 3 4 As indicated in Figure 63, the firm peak (system peak less interruptible and DR) is...
AI summary The 2025 Load Forecast Report discusses historical and forecasted system peak and firm peak, including the impact of demand response (DR). It notes an annual increase of 1.1 percent in firm peak and references weather-normalized firm peak data in Figure 64.
VED) 2025 Load Forecast Report Redacted 1 Figure 64: Weather-Normalized Firm Peak (including DR) 2 3 4 Figure 65 below shows the breakdown of the peak forecast by the various components. 5 6 Figure 65: Peak Contribution Components (MW) Mod...
AI summary The 2025 Load Forecast Report provides a detailed breakdown of peak load contributions by various components, including residential, industrial, and demand response factors, with projections for 2025 and 2035. The report includes modeled peak values, EV contributions, and DR impacts.
Page 82 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 provides an overview of load forecasting for the year 2025, though specific details have been removed.
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.
May 10 24 17 June 21 35 28 July 35 20 27 August 33 28 31 September 39 9 24 October 5 6 5 November 0 0 0 December 0 0 0 18 19 DATE: June 27, 2025 Page 85 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1...
AI summary The 2025 Load Forecast Report discusses challenges in assessing individual end-use contributions to peak demand by class, using load research data and end-use models to derive coincident peak contributions for Residential, Commercial, and Industrial classes.
the 18 forecasting SAE models. 19 DATE: June 27, 2025 Page 90 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 73: Example of time-varying EV effect detection in AMI data 2 DATE: June 27, 2025 P...
AI summary The text references a 2025 Load Forecast Report, which includes a redacted figure showing an example of time-varying EV effect detection in AMI data. The report is part of a regulatory proceeding and contains confidential information that has been removed.
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.
g the 10-year period, which is explained mainly by 21 the impact of weather variation (HDD) and economic impact in the long term. The black line and 22 points represent actual system totals. 23 DATE: June 27, 2025 Page 92 of 94 REDACTED (C...
AI summary The 2025 Load Forecast Report discusses system energy and peak demand sensitivity over a 10-year period, influenced by weather variations (HDD) and economic factors. It presents scenarios using P10/P90 ranges and includes adjustments to the peak end-use model based on wind and temperature data.
1.1% 25.2% 9.2% 6.1% 0.0% 58.4% to load XCool = (Central AC + HP Cool + Room AC) x CoolUseVariable x Coeff Residential Input Variables – XOther Intensities Econ + Reg Struct Water Cook Ref/Frz Wash/ TV Light Misc Other Coeff Total Heat Dry...
AI summary The document outlines the methodology used for forecasting load in the residential and small general service commercial sectors. It details the calculation of XCool and XOther, which are derived from various end-use intensities, prices, and climatic factors such as HDD and CDD, multiplied by coefficients and other variables.
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.
(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.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 30 of 34 Peak Forecast (Accrued Classes) The long-term system peak forecast for the accrued classes is derived through a monthly peak linear regression m...
AI summary The document describes a monthly peak linear regression model used to forecast long-term system peak demand, incorporating heating, cooling, base load requirements, and average daily wind. The model normalizes heating and cooling load requirements based on the number of days and hours in the month to estimate monthly peak demand.
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.
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.
rmal distributed weight, meaning that after 10,000 trials, a histogram of the variable will have an average and standard deviation that coincides with the distribution of the last 20 years. 6. Incorporating variability in the individual en...
AI summary The document discusses the use of Monte Carlo simulations and statistical analysis to model variability in energy and peak load forecasts. Historical data challenges and the inclusion of heat pumps in models are noted. Normal distributions are used to generate probabilistic forecasts and sensitivity diagrams.
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.
2025 Load Forecast Report Appendix E Page 3 of 19 Agenda • Summary of Changes from 2024 Forecast • Preliminary Results by Class • Ongoing Work 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 4 of 19...
AI summary The 2025 Load Forecast Report Appendix E outlines changes from the 2024 forecast, including updates to new residential load assumptions, EV load contributions, solar generation, and the removal of the residential and commercial COVID variables. The timeline includes stakeholder meetings and submission of the report to the NSEB.
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.
12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 13 of 19 Forecast Comparison – Commercial • Similar to the Residential forecast, Commercial sales will be impacted by slower EV sales, increased behin...
AI summary The 2025 Load Forecast Report Appendix E discusses forecast comparisons for Commercial, Industrial, and Energy sectors. It notes impacts from slower EV sales, increased behind-the-meter solar production, and higher RTR sales, leading to changes in load forecasts and sales trends through 2034.
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.
17 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 18 of 19 2024 vs 2025 Peaks • The warmer-than-normal weather in 2024 resulted in a lower than forecast peak load, as well as a small number of high-lo...
AI summary The 2025 Load Forecast Report highlights differences in peak load between 2024 and 2025, noting colder-than-average weather in 2025 led to more high-load days. Ongoing work includes integrating AMI data, evaluating electrification impacts, and assessing new technologies like time variable pricing and direct load control.
N-7NSPI (Synapse) RIR 1 to 29 - Redacted
41 passages
1 Request IR-1: 2 3 Report Tables and Graphs 4 5 (a) Please provide in electronic spreadsheet format all tables and graphs with 6 identification of the data source(s) that appear in the load forecast report and 7 appendices, retaining all...
AI summary The document outlines a request for electronic spreadsheet formats of tables and graphs from the 2025 Load Forecast Report, with responses indicating data locations in specific attachments. References to Synapse and 2025 LFR attachments are provided.
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.
values as applicable to TVP (and the technologies that deliver the load shifting such as batteries) including avoided costs and Effective Load Carrying Capability (ELCC) • Topic three: The Summer Reliability Assessments (SRA) completed by...
AI summary The document discusses the importance of determining Effective Load Carrying Capability (ELCC) values for technologies that enable load shifting, such as batteries, and their impact on reliability assessments. NS Power is seeking stakeholder feedback on its proposed ELCC study scope and mentions the relevance of Summer Reliability Assessments (SRA) by the Northeast Power Coordinating Council (NPCC) and North American Electric Reliability Corporation (NERC).
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.
acity factor are provided in the annual Net Metering Report. 1 13 14 (b) Please see Attachment 1. 1 M12165, Exhibit N-1, NS Power, 2024 Net Metering Report, March 27, 2025. Date Filed: August 19, 2025 NSPI (Synapse) IR-15 Page 1 of 1 REDAC...
AI summary The text references the 2024 Net Metering Report and includes a Load Forecast Report from Synapse, submitted to the NSEB on August 19, 2025. It also mentions a redacted attachment, indicating confidential information has been removed.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 New Technologies (Section 4.4.5, pp 45-46) 4 5 (a) Please provide the assumptions and calculations behind the valu...
AI summary NSPI responded to Synapse's information requests regarding the 2025 Load Forecast Report, addressing assumptions and calculations for new technologies, battery cost declines, installed capacity, monetary compensation for battery exports, and the meaning of the Residential Share (%) variable. NSPI indicated no direct compensation for battery exports and no incorporation of Figure 33 into the forecast.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-17 Attachment 1 Page 3 of 3 Residential Uptake 50% 25% 10% 5% Battery Peak Impact - No Control (MW) 0 0 0 0 Battery Peak Impact - Optimal DR Control (MW) (1,4...
AI summary The document discusses the 2025 Load Forecast Report and includes responses from NSPI to Synapse Information Requests related to the residential sector, specifically referencing figures and attachments for data and calculations.
ION REMOVED) 2025 Load Forecast Report Synapse IR-18 Attachment 1 Page 1 of 4 2022 2023 2024 2025 Forecast Sales 4715 4830 5180 5289 Residential Weather variance -127 -126 -104 - Non-weather variance 263 230 -12 - Actual Sales 4851 4934 50...
AI summary The text presents a portion of the 2025 Load Forecast Report, including forecast sales and residential weather-adjusted sales for the years 2022 to 2025. It also includes monthly residential weather-normalized (WN) energy usage data for 2022. This data is essential for understanding energy demand patterns and planning for future electricity needs.
ATION REMOVED) 2025 Load Forecast Report Synapse IR-18 Attachment 1 Page 2 of 4 Dec-22 Jan-23 Feb-23 Mar-23 Apr-23 May-23 Jun-23 Jul-23 Aug-23 Sep-23 Oct-23 Nov-23 Dec-23 2022 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 (37...
AI summary The text presents financial data from the 2025 Load Forecast Report, including figures for various months spanning from December 2022 to December 2024. The data includes both positive and negative values, indicating fluctuations in load forecasts over time.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-20: 2 3 Net System Requirement (Section 9.0). 4 5 (a) Please provide the source data and the calculations used to produce...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2025 Load Forecast Report, specifically addressing figures 57, 58, and 59. An updated attachment was provided to correct an error in the original filing.
TED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-20 Attachment 1 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Inform...
AI summary The 2025 Load Forecast Report (NSEB M12349) has been filed by Synapse, and NSPI has provided responses to Synapse's information requests. The report is part of the regulatory proceeding and involves load forecasting information.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests REDACTED
AI summary The 2025 Load Forecast Report (NSEB M12349) includes NSPI's responses to Synapse Information Requests, though the content is redacted and not fully visible.
80 156 76 2019 111 163 52 2020 96 152 56 2021 94 158 64 Date Filed: August 19, 2025 NSPI (Synapse) IR-21 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information...
AI summary The document presents load forecast data for the years 2022 to 2024, including actual and forecasted values, differences, and variances. It is part of the 2025 Load Forecast Report (NSEB M12349) and includes NSPI responses to Synapse Information Requests. The data is redacted and confidential.
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.
11040.3 11352.7 11676.5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-22 Attachment 1 Page 2 of 2 Year Actual System Peak p10 p50 p90 2010.0 2114.2 2011.0 2168.1 2012.0 1881.7 2013.0 2032.7 2014.0 2118.2...
AI summary The document presents historical and projected system peak load data from 2010 to 2035, including actual values and statistical percentiles (p10, p50, p90). It references the 2025 Load Forecast Report and NSPI's responses to Synapse Information Requests under matter number M12349.
15% 75% † Temperature at Peak (Peak HDDs) † Monthly HDD † Economics Wind at Peak † Monthly HDD † Monthly CDD † Economics REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Synapse IR-23 Attachment 1 Page 2 of 3 Weather N...
AI summary The document includes a Load Forecast Report for 2025, with data on temperature, heating degree days (HDD), cooling degree days (CDD), and wind at peak. It references historical HDD data from Weather Canada (Shearwater).
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 Appendix B: Residential Model 4 5 (a) Please provide in electronic spreadsheet format the data and the statistical...
AI summary NSPI responded to Synapse's information requests regarding the 2025 Load Forecast Report, providing details on the residential model, including statistical parameters, data, and factors influencing HP Heat, HP Cool, and Other variables. The response also outlined the forecasted HP Heat share for 2025 and 2035.
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 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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (c) Refer to page 33 of the report. Please provide the full scope of work for “The Path to 2 2030” regarding the Province’s work to s...
AI summary The document contains requests from the NSEB to NSPI regarding the 2025 Load Forecast Report, specifically concerning the 'Path to 2030' hybrid scenario study, the development of stock forecasts for residential and commercial hybrid heating systems, and the heat pump saturation rate in 2024. The NSEB is seeking detailed explanations, supporting evidence, and modeling documentation.
he latest heat pump saturation data? 28 Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 2 of 10 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CO...
AI summary The document references the 2025 Load Forecast Report (NSEB M12349) and includes NSPI's responses to Synapse Information Requests. It mentions heat pump saturation data and is part of a regulatory proceeding.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) Please provide NSPI’s own forecast of space heating stock saturation from 2 2024 to 2050 for both heat pumps and electric resist...
AI summary The document contains information requests from the NSEB to NSPI regarding the 2025 Load Forecast Report, specifically concerning space heating stock saturation forecasts, hybrid heating systems, and modeling assumptions in the SAE framework. It also requests details on how peak load impacts were estimated.
esponse, include the lowest 27 temperature assumed in the analysis and the corresponding heat pump 28 performance (coefficient of performance) at that temperature. Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 3 of 10 REDACTED (CON...
AI summary The document discusses the 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to Synapse Information Requests, including details about heat pump performance at low temperatures.
backup source. 25 26 (v) The “Hybrid” category in Attachment 1 only assumes that the backup source is 27 non-electric, it does not specify the fuel type. 28 Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 5 of 10 REDACTED (CONFIDENTI...
AI summary The text references a 'Hybrid' category in Attachment 1, which assumes a non-electric backup source without specifying the fuel type. It also mentions the 2025 Load Forecast Report (NSEB M12349) and NSPI Responses to Synapse Information Requests.
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.
chnologies, incentive structures, administration 28 models, and performance metrics. 29 30 2. Customer Research Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 6 of 10 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Re...
AI summary The document outlines steps for customer research, electric-system modeling, program operationalization, and reporting. It references the 2025 Load Forecast Report and the E3 analysis, which was completed in 2022. The report includes data on residential electrification and heating system configurations.
heat pumps. 26 27 (vi) NS Power does not estimate peak impacts of heat pumps specifically. The E3 data 28 is used as a proxy for the impact of heat pumps to peak. 2 https://www.ethree.com/wp-content/uploads/2023/12/E3_NS-Power_Electrificat...
AI summary NS Power does not estimate the peak impacts of heat pumps specifically and instead uses E3 data as a proxy for these impacts. The document references a 2025 Load Forecast Report (NSEB M12349) and NSPI responses to Synapse Information Requests.
𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒2015 19 20 This is calculated for all the heating components (electric baseboards, heat pumps, 21 secondary heat and furnace fans). 22 23 (ii) In Figure 24, the terms “% Install Non-Elec. Heat” and “% Install Elec. Heat...
AI summary The text discusses the calculation of heating components, including electric baseboards, heat pumps, and secondary heat, and references the percentage of heat pumps installed as replacements for non-electric or electric heating sources. It also mentions the 2025 Load Forecast Report and NSPI's responses to Synapse Information Requests.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) NS Power does not use data from E1 regarding heat pump installations subject to 2 rebate programs, as it provides only partial d...
AI summary NSPI states that it does not use data from E1 regarding heat pump installations due to incomplete data, and instead collects data directly from contractors. NSPI has not conducted a dedicated analysis of AMI data for heat pump load patterns due to the complexity of disaggregation, though it is working on techniques to evaluate heating loads using AMI data.
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 𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑥𝑥 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖2015 × ( ) 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑥𝑥 1 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑥𝑥 = 𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑎𝑎2015 ( ) 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒...
AI summary The document discusses NSPI's responses to Synapse's information requests regarding the 2025 Load Forecast Report. It mentions that heat pump water heaters are not directly incorporated into the forecast but are expected to improve water heater efficiency. Data is provided by Itron, and NS Power does not estimate peak load impacts for individual appliances.
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.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (b) Please see the table below. The values for 2025 are from E1’s supply agreement, while the 2 values from 2026-2035 are from the ba...
AI summary The 2025 Load Forecast Report provides energy and demand projections from 2025 to 2035, sourced from E1’s supply agreement and the E1 Potential Study. The base case aligns with current DSM levels and is used in the IRP forecast, with no changes to the forecast for Annual DSM Savings in the 2025 LFR compared to the 2024 LFR except for the addition of a 2035 forecast value and updated regression model coefficients.
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
21 passages
0% -1.6% load Note: Residential sales are the sum of existing customer load, new customer load, and EV load, minus solar load, RTR, hybrid, and DSM. Source: Appendix B from 2025 Load Forecast In addition to the outsized contribution of EVs...
AI summary Residential electricity consumption is projected to grow due to EV adoption, new customers, and electrification (heat pumps, electric water heating). Existing customer usage is expected to rise 5.8% by 2035, with heat pumps driving 64% of the increase. NSPI attributes this to rising heat pump adoption, increasing XHeat and XCool load intensities by 33.3% and 25.2%, respectively.
reases in heating and cooling are driven by growing use of heat pumps, which contributes to a 33.3 percent increase in the XHeat load intensities and a 25.2 percent increase in XCool load intensities. In total energy terms, Figure 47 of th...
AI summary Heat pump adoption is driving significant increases in residential heating and cooling loads in Nova Scotia, with projections showing 685 GWh additional heating and 102 GWh cooling demand by 2035. NSPI forecasts 80% heat pump saturation by 2035, supported by financial incentives, while noting displacement of electric baseboard heating and fossil fuel use.
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.
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.
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.
arious adjustments to arrive at the system peak. We note that a peak increase of 269 MW by 2035 means an increase in capacity requirements of about 322 MW (using a 20 percent planning reserve margin). Table 7. 2025 Peak contribution compon...
AI summary The text discusses the increase in system peak demand from 2025 to 2035, noting a projected increase of 269 MW by 2035, which would require an additional 322 MW in capacity due to a 20% planning reserve margin. Table 7 provides detailed peak contribution components for different years and sectors.
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.
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.
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.