N-12024 Load Forecast Report + Appendices - Redacted
61 passages
REDACTED (CONFIDENTIAL INFORMATION REMOVED) Nova Scotia Utility and Review Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended 2024 Load Forecast Report April 30, 2024 REDACTED REDACTED (CONFIDENTIAL INFORMATI...
AI summary The document is a 2024 Load Forecast Report prepared under the Public Utilities Act, R.S.N.S. 1989, c.380. It includes sections on executive summary, introduction, forecasting approach, and discussion of major inputs. The report is part of a regulatory proceeding involving Nova Scotia's utility and review board.
ts from Batteries ........................................................................ 45 31 Figure 29: Residential End-Use Intensities................................................................................... 47 32 Figure 30:...
AI summary The document contains a list of figures related to energy use trends, electrification forecasts, demand-side management (DSM) savings, and residential/commercial electricity consumption patterns, including historical data and projections.
DATE: April 30, 2024 Page 3 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The text is a redacted excerpt from the 2024 Load Forecast Report, indicating confidential information has been removed. The document's title and date are visible, but content details are obscured.
..................................................................................................... 82 16 Figure 57: Peak Regression Coefficients ...................................................................................... 84 1...
AI summary The text lists technical figures related to energy demand forecasting, peak load analysis, and system sensitivity. It includes historical data, forecasts, and components contributing to peak loads, with references to demand response (DR) and load research data. Topics focus on forecasting methodologies, load management, and system reliability.
............................ 98 30 Figure 71: System Peak Sensitivity .............................................................................................. 99 31 Figure 72: 2022 Evergreen IRP Scenarios Comparison ....................
AI summary The document is a redacted 2024 Load Forecast Report containing attachments and appendices detailing forecast models, stakeholder presentations, and partially confidential data. It outlines residential, commercial, and industrial demand projections, including sensitivity analyses and model inputs.
1 1.0 EXECUTIVE SUMMARY 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market 4 Rules, Nova Scotia Power Incorporated (NS Power, the Company) is required to provide 5 the Nova Scotia Utility and Review...
AI summary NS Power is required to submit a 10-year energy and demand forecast (Load Forecast) to the NSUARB, outlining considerations like weather, economic indicators, and energy efficiency program effectiveness. The forecast uses Statistically Adjusted End-Use (SAE) models for residential and commercial sectors, acknowledging inherent uncertainties from factors like technological changes and electrification impacts.
al and commercial rate classes. The SAE models explicitly 27 incorporate end-use energy intensity projections into the Load Forecast. End-use energy 28 forecasts derived from the residential and commercial SAE models are then combined with...
AI summary The 2024 Load Forecast Report details higher near-term growth due to customer additions and adjusted weather, mid-term EV growth impacts, and long-term reductions from DSM and solar. The net annual increase is projected at 0.2%. Forecasts combine SAE models, industrial econometric data, and customer-specific inputs to determine Net System Requirement (NSR).
0.5 2,623 1.5 2033F 11,605 0.2 2,670 1.8 2034F 11,695 0.8 2,727 2.1 3 DATE: April 30, 2024 Page 9 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 2.0 INTRODUCTION 2 3 NS Power annually develops a for...
AI summary NS Power's 2024 Load Forecast Report, covering 2024-2034, serves as a planning tool for energy sales and peak demand. The NSUARB initiated a paper hearing in 2023 to review the 2023 Load Forecast Report, with stakeholder input considered for accuracy and adequacy of the forecasting methodology.
customers to ensure system adequacy. 26 // 27 28 The Board expects NS Power to continue exploring all realistic scenarios 29 related to commercial hydrogen production and, where there is sufficient 30 data, incorporate them to the extent p...
AI summary The NSUARB directs NS Power to explore commercial hydrogen production scenarios in load forecasting and address the significant 2022 forecast variance (234 GWh) attributed to pandemic and heat pump impacts. The Board emphasizes revisiting unexplained variance and considering factors like cooling demand and home electrical upgrades in residential load projections.
2022 unexplained variance and 2 report on its findings in the 2024 Load Forecast Report. 3 2F 3 4 In accordance with the Board’s direction, NS Power revised and enhanced the 2024 Load 5 Forecast in the following manner: 6 7 • The EV foreca...
AI summary NS Power revised the 2024 Load Forecast Report per the Board's direction, updating EV forecasts, integrating hybrid electrification scenarios, summarizing Smart Grid and Demand Response projects, discussing economic inputs, hydrogen production impacts, and analyzing residential forecast variances.
Page 12 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Summary of Stakeholder Consultations 2 3 On April 11, 2024, NS Power conducted a stakeholder session by videoconference with 4 representatives...
AI summary NS Power held a stakeholder session on April 11, 2024, to discuss updates to the 2024 Load Forecast, including EV forecasts, renewable-to-retail impacts, and forecasting methodologies. The session involved NSUARB, the Consumer Advocate, and other stakeholders, with a focus on revised assumptions and class-level trends.
Industrial classes, and customer 5 surveys and historical data for the Large Industrial 6 customer classes. 5F 6 7 The SAE model is a hybrid of the econometric and end-use methodologies, incorporating 8 economic and end-use forecast variab...
AI summary The text explains the SAE model, a hybrid forecasting approach combining econometric and end-use methodologies. It incorporates variables like end-use saturation, efficiency trends, population changes, economic conditions, price, and weather. The model integrates bottom-up appliance-level data with economic indicators to forecast energy consumption, capturing both efficiency improvements and structural changes.
ural changes are captured in the 22 residential forecast model through the SAE model specifications. Figure 4 shows the 23 general forecast approach used in the SAE models. 24 4 References to the Residential class include Domestic Service...
AI summary The 2024 Load Forecast Report outlines the use of SAE model specifications for residential, commercial, and industrial load forecasting. It references Figure 4, which illustrates the general forecast approach, and includes footnotes clarifying class categorizations.
Page 15 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 4.0 DISCUSSION OF MAJOR INPUTS 2 3 4.1 Historical Class Sales and Energy Data 4 5 The Load Forecast is developed using NS Power’s “billed” sale...
AI summary The 2024 Load Forecast Report discusses the use of 'billed' sales data over 'accrued' sales for forecasting, noting annual alignment despite monthly discrepancies. Historical data spans 2014–2023 for residential/commercial forecasts and 2006–2023 for industrial. Peak demand data derives from hourly load records, with separate forecasts for large customers. The Renewable to Retail (RtR) market, established in 2016, is referenced as a contextual factor.
ent licensed retailers to sell renewable energy generated within the 25 province directly to NS Power’s retail customers. The impact of this input is discussed in 26 Section 4.7. 27 DATE: April 30, 2024 Page 16 of 100 REDACTED (CONFIDENTIA...
AI summary The text outlines the impact of renewable energy sales by licensed retailers to NS Power's customers, referencing Section 4.7. It discusses weather data's influence on electric sales, using Heating Degree Days (HDD) and Cooling Degree Days (CDD) calculated from 10 years of temperature data (2014–2023), noting a warming trend with a 10-year HDD average of 3,743 versus a 30-year average of 3,864.
INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 5: 10 Year Normal Monthly HDDs and CDDs 2 3 4 5 The annual number of HDD has been declining as a result of climate trends, as shown in 6 Figure 6. 7 8 Figure 6: Historic Annu...
AI summary The 2024 Load Forecast Report analyzes climate trends impacting Heating Degree Days (HDD) and Cooling Degree Days (CDD). HDD has declined by ~17/year due to warming, while CDD increased by ~1.4/year. Trends are derived from regression analysis of 30-year data with 10-year moving averages, illustrated in figures 5-8.
sources were consulted that support these assumptions. The Climate Atlas of 6 Canada 7 provides estimates for annual HDD as well as number of “Winter Days” that are 6F 7 -15° or colder under different climate change scenarios between now a...
AI summary The text references climate projections showing declining Heating Degree Days (HDD) and fewer 'Winter Days' in Halifax under both current and reduced emissions scenarios. It cites the Climate Atlas of Canada and New England temperature trends to support forecasts of warming patterns affecting energy demand and winter weather patterns.
f 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 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 4 Board of Canada’s 20-year fore...
AI summary The 2024 Load Forecast Report uses economic data from the Conference Board of Canada's 20-year forecast and the SAE framework to model residential demand, including work-from-home impacts. NSUARB's Decision 9 directed specific considerations for load forecasting.
ACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 12: Household Size 2 3 Household Size decreased steadily from 2000 to 2016 as population growth stagnated 4 while new housing continued to increase. From...
AI summary The 2024 Load Forecast Report analyzes household size trends from 2000 to 2025, noting stabilization until 2022 followed by growth due to population increases. It details econometric models for commercial and industrial sectors, emphasizing longer regression timescales for improved economic variable relevance and model fit.
38 0.9 14‐23 1.6 2.2 24‐34 3.3 0.6 3 4 The major Canadian banks provide short term (1-2 year) forecast for some of the key 5 economic indicators, and these are provided in Figure 16. The Conference Board of 6 Canada forecast is in line wit...
AI summary The document presents a comparison of economic forecasts from major Canadian banks and the Conference Board of Canada for GDP, employment, and housing starts in 2024 and 2025. The forecasts show variations in growth rates and housing starts across different institutions.
omic Outlook, Dec 2023 11 RBC Provincial Forecast, Dec 2023 12 TD Provincial Economic Forecast Dec 2023 13 National Bank of Canada Monthly Economic Monitor, Dec 2023 DATE: April 30, 2024 Page 29 of 100 REDACTED (CONFIDENTIAL INFORMATION RE...
AI summary The document includes economic forecasts from multiple financial institutions and a redacted 2024 Load Forecast Report, with confidential information removed. The report is part of a regulatory proceeding and involves load forecasting, which is relevant to energy planning and resource allocation.
fferent 13 components for 2021, 2022 and 2023 actuals vs forecast and weather normalized totals, 14 and the 2024 forecast. 15 16 Figure 37: Comparison of Forecast to Actuals 17 Year 2021 2022 2023 2024 Forecast Sales 4718 4715 4830 5175 We...
AI summary The text provides a comparison of forecasted and actual sales for the years 2021 to 2024, including weather and other variances. It discusses the 'Other variance' in 2022 and 2023, which is attributed to large variances in winter months, likely related to heating. The Load Forecast Report (LFR) is referenced, with a focus on the residential SAE model and statistical comparisons.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 electrification). The 2024 updates to heating intensity (see Figure 22) reduce the variance 2 significantly as shown in Figure 38: 3 4 Figure 38: Comparison o...
AI summary The 2024 Load Forecast Report discusses updates to heating intensity and compares forecasted load data with actuals, highlighting a reduction in variance. The report includes metrics such as MAD, MAPE, and variance percentages to evaluate forecast accuracy.
2024 5181 62 0 -44 10 0 -30 5180 -55 -24 2025 5220 120 0 -71 23 -18 -63 5211 -114 -51 2026 5240 173 -26 -99 31 -75 -98 5146 -177 -79 2027 5271 220 -53 -131 41 -75 -133 5140 -241 -108 2028 5349 262 -79 -165 54 -75 -170 5176 -307 -137 2029 5...
AI summary The text includes numerical data spanning from 2024 to 2034, potentially representing energy load forecasts or related metrics. It references Figure 42 and mentions the use of a regression model output and methodology from a 2020 Load Forecast response to NSUARB IR-12 (e).
Page 63 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The document presents the 2024 Load Forecast Report, which contains confidential information that has been redacted. The report is likely related to energy demand projections for the upcoming year.
GWh in the 2023 23 forecast), and the EV load has decreased (+244 GWh by 2034 compared to +403 GWH in 24 the 2023 forecast), resulting in lower growth than the previous forecast. 25 DATE: April 30, 2024 Page 64 of 100 REDACTED (CONFIDENTIA...
AI summary The 2024 Load Forecast Report discusses changes in energy demand, noting a decrease in EV load and the impact of the COVID-19 pandemic on commercial energy sales. A specific COVID variable was added to the General rate class model in 2023 to account for the lag in sales.
2024 Load Forecast Report REDACTED 1 Figure 46: Historical and Forecast Annual General Demand Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2024 6 to 2034. Total change between 2024 and 2...
AI summary The 2024 Load Forecast Report discusses the Large General class showing slower growth due to revised estimates of large project completion. The forecast uses customer surveys and historical sales data, with adjustments made based on the 2024 Board Decision to account for overestimations in prior years.
r over year changes, and the resulting 2024 forecast which has been adjusted 2 to account for the over estimation in prior years. 3 4 Figure 47: Large General Annual Growth (GWh) 5 Year 2022 2023 2024 2025 2026 2027 2028 2022 Forecast 12 2...
AI summary The text discusses changes in large general annual growth forecasts for electricity consumption, noting adjustments in the 2024 forecast due to overestimations in prior years. Growth is expected to be driven by institutional facilities, particularly hospital expansions, but overall demand is projected to decrease by 2034 due to demand-side management (DSM) efforts.
Page 70 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 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 4 econometric-based mod...
AI summary The Small Industrial class forecast uses econometric models based on provincial manufacturing GDP. Sales have been flat over the last 10 years but are expected to grow at 0.6% annually due to economic growth, offset by a migration of load to RTR (4 GWh).
2024 Load Forecast Report REDACTED 1 Figure 50: Historical and Forecast Annual Medium Industrial Sales 2 3 4 5 7.3 Other Industrial Rate Classes 6 7 Other Industrial rate classes include Large Industrial, Large Industrial Interruptible, 8...
AI summary The 2024 Load Forecast Report discusses the forecasting methodology for Other Industrial rate classes, including Large Industrial, Generation Replacement, and Load Following. Surveys of customers are used to predict load changes, with most expecting stable consumption, while one major customer is forecast to increase usage.
Page 75 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The 2024 Load Forecast Report is mentioned, though the content is redacted. It likely contains information related to electricity demand projections for the year 2024.
Res Comm Ind Other Losses NSR 2023 Forecast 4,830 3,142 2,436 138 742 11,288 Est. Weather Impact -126 -25 -3 -11 -164 Large Customer New Projects -3 -3 Large Customer Actuals -260 -260 Unexplained Variance 230 -8 -12 19 40 270 2023 Actual...
AI summary The text presents a forecast and actual data for 2023, highlighting variances due to weather, unexplained residential class variance, and large customer actuals. It notes that 2023 was warmer than average, impacting heating and cooling loads. NSR is projected to increase by 0.2% annually from 2024 to 2034, driven by new customers, space heating, and EV adoption.
TR 23 migration offsetting sales. Annual NSR is shown below in Figure 54. Forecast NSR values 24 and the contribution to NSR from the different sectors can be found in Appendix A. 25 DATE: April 30, 2024 Page 78 of 100 REDACTED (CONFIDENTI...
AI summary The document discusses the 2024 Load Forecast Report, including historical and forecast annual Net System Requirement (NSR) values, and a breakdown of forecast components from 2024 to 2034. Data for all classes is referenced in Attachment 4.
Page 80 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The document presents the 2024 Load Forecast Report, which includes confidential information that has been redacted. The report provides an analysis of projected electricity demand for the year 2024.
5 for the 9 participating C&I customers. Results also indicated that available capacity tends 6 to vary from event to event and be lower than enrolled capacity, and that available capacity 7 tends to vary depending on time of day, with cap...
AI summary The document discusses the performance of demand response (DR) programs, including the recruitment of new customers and the evaluation of DR capacity results for the winter 2023/2024 season. It also mentions a pilot project with E1 involving residential smart thermostats and EVs. The impact of these initiatives on load forecasts is expected to be within the sensitivity analysis provided.
firm peak is estimated to be 2,302 MW. Figure 62 provides a breakdown of actual system 16 peak compared to the forecast for 2022. 17 18 Figure 62: Forecast Peak Variance vs Actuals 19 MW 2023 Forecast Peak 2,256 Interruptible -88 Weather (...
AI summary The document discusses the forecast peak variance for 2023, comparing it to actual system peak demand of 2,455 MW. The peak was influenced by factors such as weather, wind, weekends, lighting, and unexplained variables. The forecast model struggled to predict this peak accurately, even with adjustments for known variables.
residential energy actuals versus forecast, as a test, the modeled peak 38 can be run, holding 37F 12 off the last 12 months of actuals of each of the last 3 LFRs. The end-use assumptions vary 13 between forecasts, and Figure 63 below show...
AI summary The text discusses residential energy actuals versus forecasts, highlighting how updated assumptions for 2024 have reduced uncertainty in peak load forecasting. The comparison of forecasted and actual peak loads shows improvements in accuracy, particularly in 2023 despite a rare mid-day peak occurrence.
-down model), and the resulting 2 forecast informed by the LRS-AMI historical series (bottom-up). 3 4 Figure 69: Monthly historical Residential LRS load at peak and forecasts 5 6 7 8 Both the current residential peak demand forecast (green...
AI summary The document discusses two forecasting methods for residential peak demand: a top-down approach and a bottom-up model based on LRS-AMI historical data. The top-down method uses annual load factors updated for the system peak month, while the bottom-up model provides more detailed forecasts. The top-down approach is currently preferred due to its simplicity and similar results in early forecast years.
1 11.0 SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, 4 including economics, weather, adoption of distributed generation, electricity rates and 5 DSM. Although each of these...
AI summary The text discusses a sensitivity analysis of sales and peak forecasts, highlighting the uncertainty influenced by factors like economics, weather, and DSM. A Monte Carlo simulation approach was used to estimate a probable distribution of future load, resulting in a P10/P90 range of 468-646 GWh over 10 years, primarily influenced by weather and economic variations.
represent actual system totals. 25 DATE: April 30, 2024 Page 97 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 70: System Energy Sensitivity 2 3 4 Similarly, a P10/P90 scenario was created fo...
AI summary The document discusses the creation of a P10/P90 scenario for peak demand using random sampling of weather and economic drivers, with a width of 430-530 MW. Adjustments to the peak end-use model, such as wind and 12-hour temperature averages, are included in the forecast.
140.062 22.657 6.182 0.00% MA(1) 0.506 0.094 5.412 0.00% Residential Model Statistics Model Statistics Iterations 21 Adjusted Observations 120 Deg. of Freedom for Error 106 R-Squared 0.989 Adjusted R-Squared 0.987 AIC 6.481 BIC 6.806 F-Sta...
AI summary The document presents statistical model outputs and reconciliation data for the 2024 Load Forecast Report, including model statistics, error metrics, and reconciliation details for residential energy demand forecasting from 2024 to 2034.
(698) (310) Change 9.0% 7.0% 6.0% -8.0% -1.4% -3.4% -6.9% 2.3% to load Res Sales = Existing Customer + New Customer + EV + Solar + RTR + Hybrid + DSM Existing customer load is calculated as Res Average Use (10,468 kWh/customer in 2024, 11,...
AI summary The document discusses the calculation of residential load, including existing customer load, new customer load, EV load, solar load, RTR load, hybrid load, and DSM load. It provides data on residential average use and its components, such as heating, cooling, and other uses, and includes a regression analysis for 2024 and 2034.
INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 12 of 35 Variable Coefficient StdErr T-Stat P-Value MStructSmlGen.WtXHeat 0.856 0.037 23.347 0.00% MStructSmlGen.WtXCool 0.330 0.042 7.797 0.00% MStructSmlGen.WtXOther 0.727 0....
AI summary This section presents statistical data from the 2024 Load Forecast Report, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. The data includes information on heating, cooling, and other factors, as well as monthly and yearly bin variables.
(CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 16 of 35 Small General Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total XCool (kWh) 2024 36,...
AI summary The text presents input variables for forecasting cooling and other energy usage in the 2024 Load Forecast Report. It includes values for 2024 and 2034, along with percentage changes, and provides a formula for calculating XCool.
FORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 18 of 35 Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.743 0.031 23.702 0.00% MStructGen.WtXCool 0.698 0.063 11.070 0.00% MStructGen.WtXOther 1.080 0.019 57.501...
AI summary This section presents statistical data from the 2024 Load Forecast Report, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. These statistics are used to analyze the impact of different factors on load demand.
NTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 19 of 35 General Service Model Statistics Model Statistics Iterations 12 Adjusted Observations 120 Deg. of Freedom for Error 109 R-Squared 0.920 Adjusted R-Squared 0.912...
AI summary This section presents statistical details from the 2024 Load Forecast Report, including model statistics such as R-squared, AIC, BIC, and error metrics. It also discusses the reconciliation of general demand forecasts for the commercial sector, noting that it is forecast as gross total sales rather than average use.
general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total sales rather than average use as is the case in the small general and residential classes. Like the small general model, a flat s...
AI summary The text discusses the forecast for general demand load in the commercial sector, highlighting the use of a flat scaling factor in the regression model and adjustments for factors like EV load, PV, RTR, Hybrid, and DSM. It also provides a comparison between 2024 and 2034 load forecasts and includes a formula for calculating general demand sales.
30.2% 0.0% 0.0% 26.4% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor General Demand Input Variables – XOther Intensities Econ Reg + Struct Vent Water Cook Refrig Light Office Misc Other Coeff Scaling Total Heat Use Factor Xothe...
AI summary The text presents data and models related to load forecasting, including demand input variables and an industrial econometric model. It includes percentages, coefficients, and variables used in forecasting energy demand for residential and industrial sectors, with specific references to factors like hurricane Fiona impacting billing delays.
nd REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 3 of 9 Figure C3: Firm Peak Demand Figure C4 below provides an overview of the energy forecast accuracy. For these calculations, the load of...
AI summary The document discusses the accuracy of energy and firm peak load forecasts over a 10-year period, noting that accuracy diminishes beyond 5 years. The analysis excludes major pulp and paper mills due to their significant variability, with average errors of just over 2% for energy forecasts and just over 5% for firm peak load forecasts in the first 5-year period.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 5 of 9 NSR less mills Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for...
AI summary The document presents a load forecast report with statistical data on forecast accuracy, including average percent error and MAPE values for different lead times ranging from 1 to 10 years. The data shows increasing error percentages as the lead time increases.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 6 of 9 Figure C5: Firm Peak Forecast Accuracy Firm Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecas...
AI summary This table presents the accuracy of firm peak load forecasts issued in various years from 2013 to 2022, comparing forecast values for each year up to 2023. It shows how forecasted load values have evolved over time, with fluctuations in forecast accuracy across different years.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 7 of 9 Firm Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for...
AI summary The document presents a load forecast report with statistics on forecast accuracy, including average percent error and MAPE across different lead times, indicating a trend of increasing error as the forecast horizon extends.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 8 of 9 Figure C6: System Peak Forecast Accuracy System Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast For...
AI summary The document presents a table showing the accuracy of system peak forecasts issued in various years from 2013 to 2022, comparing forecasted values against actual values for each year. This data is used to assess the performance of load forecasting over time.
2,225 2,240 2022 2,185 Actual System Peak: 2,118 2,015 2,111 2,018 2,073 2,060 2,050 1,968 2,216 2,455 Percent Error 2013 -1.9% 3.7% -1.2% 3.2% 0.1% 1.1% -5.0% -1.3% -12.8% -21.4% 2014 3.1% -2.3% 2.0% -0.9% -0.5% -0.3% 3.7% -8.0% -16.6% 20...
AI summary The text presents historical data on actual system peak and percent error from 2013 to 2022, indicating variations in load forecasting accuracy over time. The data is part of a load forecast report, with some sections redacted due to confidentiality.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 9 of 9 System Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: f...
AI summary The document discusses a sensitivity analysis using Monte Carlo simulation for economic and weather variables in the 2024 Load Forecast Report. The analysis is based on deterministic SAE class regression models, with coefficients exported into Oracle Crystal Ball for simulation.
. Once the deterministic SAE class regression models are completed, the regression coefficients are exported into the Monte Carlo tool, called Oracle Crystal Ball (MS Excel add-on). 2. The Monte Carlo process assumes that the regression co...
AI summary The text describes the use of deterministic SAE regression models and the Monte Carlo simulation tool Oracle Crystal Ball to forecast load demand. Historical weather and economic data are used as inputs, and the simulation runs 10,000 trials to assess how variations in these inputs affect the forecast.
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 text discusses the use of Monte Carlo simulations and statistical analysis tools like the Statistical Analysis Engine (SAE) and Oracle Crystal Ball to model variability in energy load forecasts. It mentions the incorporation of historical data and the impact of heat pumps on load simulations, as well as the generation of Normal distributed averages and standard deviations from 10,000 trials.
REDACTED 2024 Load Forecast Report Appendix D Page 5 of 9 Appendix D – Forecast Sensitivity Analysis Figure D3: Distribution of Peak (Residential, Commercial and Small and Medium Industrial) 8. From these annual forecast distributions, the...
AI summary The appendix discusses probabilistic load forecasting, focusing on the distribution of peak demand across residential, commercial, and small and medium industrial sectors. It highlights the impact of heating degree days (HDD) on peak demand, noting that monthly HDD has surpassed peak HDD importance in 2025 due to year-long residential heating effects.
ich now is based in a 12-hour average) in 2025 as year-long residential heating had an impact on sales, and therefore, that impact (sensitive to monthly variations) was translated into Peak Demand. Page 5 of 8 REDACTED (CONFIDENTIAL INFORM...
AI summary The document discusses the sensitivity of energy sales forecasts to various input variables, noting that weather has the strongest impact in the near term, while economics becomes more dominant in the long term due to compounding effects. This is illustrated through figures showing forecast sensitivity for 2025 and 2034.
2024 10yr Preliminary Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 2 of 18 Purpose • As in previous years, this presentation on the preliminary 10-year load forecast provides an opportunity...
AI summary This document outlines the 2024 10-year preliminary load forecast report, including updates from the 2023 forecast, the timeline for preparation and submission, and an agenda for stakeholder engagement.
Forecast Comparison - Commercial The significant changes to the forecast for 2024 are a larger drop in load between 2025 and 2026 due to the introduction of RTR, with lower growth from EVs. 12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The document discusses forecast comparisons for 2024, highlighting changes in load forecasts for commercial, industrial, energy, and peak demand. Key factors include the impact of RTR, changes in EV growth, heating intensity, and new customer forecasts.
N-4NSPI (NSUARB) RIR-1 to RIR-26
19 passages
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Figure 3: Historic and Forecast Net System Requirement and System Peak shows that 4 System Peak growth from 2023 t...
AI summary NSPI explains in its response to NSUARB that the 2025 forecast shows reduced NSR due to customer migration to LRS under the Renewable to Retail program and municipal load shifts. The 2023 system peak was unusually high due to extreme cold, contrasting with the 2024 forecast based on normal temperature averages.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 2 3 (i) The increasing number of cooling degree days (CDD) will drive additional cooling 4 load, but they are increasing at a very s...
AI summary NSPI highlights that while cooling degree days (CDD) increase slowly, heat-pump adoption drives larger load impacts. Regression equations in the 2024 Load Forecast Report (NSUARB M11689) changed due to updated data (1994-2023 vs. 1993-2022), with R² values for HDD slightly decreasing and CDD improving, though both remain strong fits.
DD increased from 0.6999 in 2023 to 0.7484 in 2024, implying 18 that the fit of the 2024 data set is better. In both cases the trends continue to show a good 19 fit with the data. Date Filed: June 19, 2024 NSPI (NSUARB) IR-4 Page 1 of 1 20...
AI summary The document notes an increase in DD (likely a metric) from 0.6999 in 2023 to 0.7484 in 2024, indicating improved data fit for the 2024 dataset. This is part of the 2024 Load Forecast Report (NSUARB M11689) submitted by NSPI in response to NSUARB information requests.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 Page 23 of the Report indicates that NS Power expects Nova Scotia’s annual population 4 growth peaked in 2023. 5 6...
AI summary NSPI attributes population growth projections to the Conference Board of Canada, noting slowing growth rates but no policies altering trends. The 2 million population target by 2060 is deemed aspirational and not factored into forecasts. The 2024 Load Forecast Report shows reduced population growth after 2024 compared to prior projections.
ge the rate 27 of population growth beyond demographics and current trends; the Provincial population 28 target of 2 million by 2060 is aspirational and not taken into account. 29 Date Filed: June 19, 2024 NSPI (NSUARB) IR-5 Page 1 of 2 20...
AI summary NSPI explains that population growth projections beyond current trends are not factored into forecasts, as the 2060 target of 2 million is aspirational. The 2024 Load Forecast Report uses updated data from the Conference Board of Canada, with source details in Synapse IR-05. NSPI did not adjust housing forecasts for the Housing Accelerator Fund, as it was ongoing during forecast development.
l in progress 12 when the forecast was developed; however, it is assumed that the additional new housing units 13 will fall within the underlying uncertainty of the housing completion forecast. Date Filed: June 19, 2024 NSPI (NSUARB) IR-6...
AI summary NSPI submitted responses to NSUARB information requests regarding the 2024 Load Forecast Report (NSUARB M11689), noting assumptions about new housing units falling within the forecast's uncertainty range. The document highlights forecasting methodology and infrastructure planning considerations.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-7: 2 3 In reference to Economic Drivers, please explain why the following data has changed from 4 the 2023 Load Forecast...
AI summary NSPI explains that 2022 economic driver data changes in the Load Forecast Report stem from revised data by the Conference Board of Canada and CMHC. Historic data adjustments do not impact forecasts as new construction data is only used in the forecast period, not historical series.
NSPI (NSUARB) IR-7 Page 1 of 2 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 (b) NS Power is not aware of why the historic series changes, but Statistics Canada lists a 2 change...
AI summary NSPI responds to NSUARB's information requests regarding the 2024 Load Forecast Report, noting revisions to historic employment data due to Statistics Canada's NAICS 2022 update and lack of details from the Conference Board of Canada on forecast changes.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Figure 16: Economic Forecast Comparison employs data from three of Canada’s Big 5 banks 4 and National Bank. Board...
AI summary NSPI explains omissions of Bank of Nova Scotia and Canadian Imperial Bank of Commerce forecasts in its 2024 Load Forecast Report (NSUARB M11689), citing data sufficiency and 2024-only coverage. NSPI claims Conference Board of Canada employment projections were not adjusted, as 2024 underestimation and 2025 overestimation offset each other over time.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-11: 2 3 In reference to Figure 21: Heat Pump Forecast on page 34. 4 5 (a) In the 2023 Load Forecast Report, NS Power fore...
AI summary NSPI responds to NSUARB's query about heat pump forecasts and heating intensity adjustments in the 2024 Load Forecast Report. Actual 2023 heat pump installations are incorporated into the model, and adjustments are based on overall winter load/weather variance, not daily data or the Polar Vortex.
of 2 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 (c) Cooling intensity has also increased but is of a smaller magnitude compared to heating. 2 No specific adjustments have been...
AI summary NSPI's response to NSUARB's information requests notes that while cooling intensity has increased, no specific model adjustments were made for cooling due to the model's good fit with actual summer load data. The report is part of the 2024 Load Forecast proceeding (NSUARB M11689).
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-12: 2 3 Figure 23: Water Heater Forecast projects the percentage saturation for 2024 to 2034. In the 4 2023 Load Forecast...
AI summary NSPI explains that actual 2023 electric water heater saturation is unverified due to data limitations, relying instead on a 2019 survey (63% electric heaters). The 2024 forecast's upward scaling for 2025-2028 is attributed to rounded data presentation, with minor changes in heat pump assumptions causing slight saturation adjustments.
V installs in accordance with the recent establishment of the commercial net 11 metering program based on corresponding legislation (installation of up to 1,000 kW for 12 commercial customers). Date Filed: June 19, 2024 NSPI (NSUARB) IR-14...
AI summary NSPI responds to NSUARB's inquiry about revisions in end-use intensity data for commercial customers, attributing changes to updated EIA inputs. The response notes a lack of access to underlying EIA data drivers. The text also references the commercial net metering program and the 2024 Load Forecast Report (NSUARB M11689).
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-16: 2 3 Figure 32: Commercial and Industrial Electrification Forecasts (cumulative) present values 4 that are different f...
AI summary NSPI revised its 2024 commercial and industrial electrification forecasts downward compared to the 2023 report, citing overestimation of past project completions. The response highlights that actual project completion rates were lower than previously assumed, leading to adjusted cumulative forecasts.
8 for example, changes to the fuel sources for heating or purchasing more efficient appliances 29 as they reach end of life. These longer-term changes are driven by the end use components 30 of the SAE model and therefore a separate elasti...
AI summary NSPI responds to NSUARB's IR-18 request regarding population projections in the 2024 Load Forecast Report. It clarifies that 2034 population growth projections (1.140 million) remain consistent between 2023 and 2024 forecasts, with differences attributed to updated 2022/2023 population counts. NSPI states it does not separately evaluate population growth against deaths/emigrations.
NSPI (NSUARB) IR-18 Page 1 of 1 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-19: 2 3 On page 60, the Report notes that the structural index, which considers building...
AI summary NSPI responds to NSUARB's questions about the 2024 Load Forecast Report, stating it lacks more recent building efficiency data and attributes changes in BSE Heat values and floor area estimates to external data sources (Itron, Natural Resource Canada).
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-21: 2 3 Section 6.0 examines the Commercial Sector and indicates that a detailed breakdown of the 4 two commercial SAE mo...
AI summary NSPI explains that the continued use of the COVID variable in the load forecast model is justified by the lag between economic growth and flat commercial energy sales. They commit to updating data in future reports. NSUARB requested clarification on this variable's relevance given positive economic growth since 2020.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 Section 10 Peak Demand forecast estimates that the system peak is higher in the near term 4 because of heating lo...
AI summary NSPI responds to NSUARB's questions about heating load stabilization in the 2024 Load Forecast Report and discrepancies in peak demand data. NSPI explains hybrid heating systems will mitigate heating load growth and clarifies the 2024 report corrected preliminary 2023 data.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 In Appendix D, NS Power’s sensitivity analysis incorporates total GWh and Peak MW of 4 firm supply for two hydrog...
AI summary NSUARB requested clarification on NSPI's 2024 Load Forecast Report regarding hydrogen facilities. NSPI explained only two facilities were included due to insufficient details from others, with Appendix D providing preliminary load impact assessments. The response highlights forecasting methodology and load management considerations for hydrogen projects.
N-6NSPI (Synapse) RIR-1 to RIR-54 - Redacted
51 passages
Synapse IR-09 Attachment 2 Figure 27, 64 Synapse IR-10 Attachment 1 Figure 28 Synapse IR-11 Attachment 1 Figure 30 2024 LFR Attachment 2, tab Intensity Figure 32 2024 LFR Attachment 4, tab CommGrowth Figure 33 Synapse IR-17 Attachment 1, t...
AI summary The document lists figures and attachments from the 2024 Load Forecast Report (NSUARB M11689) and Synapse IR submissions, including responses by NSPI to information requests. It references multiple tabs and appendices containing load forecast data, regression analyses, and model details.
2024 LFR Attachments 5-10 Regression breakdowns in Synapse IR-37 Attachment 1, Synapse IR-38 Attachment 1, Appendix B and Synapse IR-39 Attachment 1 Appendix D Synapse IR-45 Attachments 1, 2 and 3 1 Date Filed: June 19, 2024 NSPI (Synapse)...
AI summary The document references filings related to the 2024 Load Forecast Report, including Synapse attachments and responses by NSPI. It mentions NSUARB matter M11689 and electronic submission of non-confidential materials.
EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-2 Attachment 1 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse I...
AI summary NSPI responds to NSUARB's IR-3 requests regarding the 2024 Load Forecast Report, addressing billed/accrued sales differences, regression period impacts on model accuracy, and timeframe consistency for industrial forecasts. The response cites statistical relevance issues when using shorter timeframes for industrial data.
stical relevance as shown 24 by high P-values (Small Industrial), or the model loses statistical relevance as shown by a 25 reduced adjusted R squared value (Medium Industrial). Date Filed: June 19, 2024 NSPI (Synapse) IR-3 Page 1 of 1 RED...
AI summary The text presents statistical analysis of load forecasting models for different industrial sectors, noting that model relevance is affected by P-values and adjusted R-squared values. Tables show accrued and billed sales data from 2014–2023 across residential, industrial, and general demand categories, as part of the 2024 Load Forecast Report.
253 257 247 245 254 261 253 257 264 257 Medium Industrial 471 476 463 463 472 461 468 476 483 473 Residential variance 0.8% 0.4% -1.3% -0.2% -0.8% 0.5% 0.5% -0.9% 0.6% -1.0% Small General variance 0.7% 0.1% -1.1% -1.0% -0.2% 0.0% -0.4% -0....
AI summary The text presents load forecast variance data for different customer classes, including residential, small general, general demand, small industrial, and medium industrial sectors. It references the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests, indicating regulatory analysis of energy usage patterns and forecasting methodologies.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-4: 2 3 Weather Data (Section 4.2, pp 17-22) 4 5 (a) Refer to the following statement on page 17: “18°C is assumed to be...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, focusing on HDD/CDD calculation methodologies, temperature data, and forecast assumptions. Requests include explanations of 18°C thresholds, internal/solar heat gains, and spreadsheet data for historical and forecasted HDD/CDD values across Nova Scotia weather stations.
nd. Please identify 25 the years represented on the x-axis for Figure 8. 26 27 (f) Please provide the data and calculations behind the HDD and CDD forecast in Figure 28 9. 29 Date Filed: June 19, 2024 NSPI (Synapse) IR-4 Page 1 of 3 REDACT...
AI summary The document contains requests for clarification on figures 8 and 9 from a 2024 Load Forecast Report submitted by NSPI to NSUARB (M11689). It seeks data on x-axis years for Figure 8 and HDD/CDD forecast calculations for Figure 9, reflecting regulatory scrutiny of load forecasting methodologies and energy usage patterns.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (g) Please provide the hourly peak loads and temperature data for ten years as referenced 2 on page 21. 3 4 (h) Please identify whi...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, explaining HDD/CDD calculations and providing data in attachments. They note that building heat gains are not considered in these metrics.
efer to Attachment 1, tab HDDCDDTrend. 25 26 (g) Please refer to Attachment 1, tab PeakTemp. 27 1 https://climate.weather.gc.ca/historical_data/search_historic_data_e.html Date Filed: June 19, 2024 NSPI (Synapse) IR-4 Page 2 of 3 REDACTED...
AI summary NSPI provides responses to Synapse Information Requests as part of the 2024 Load Forecast Report (NSUARB M11689). References are made to Attachment 1 for data on HDDCDDTrend and PeakTemp, with graph details indicating 10-year average years.
1 Request IR-5: 2 3 Economic Information (Section 4.3, pp 23-29) 4 5 (a) Please provide in electronic spreadsheet format all economic and provincial statistical 6 data used both in developing the models and the forecasts for the historical...
AI summary The request (IR-5) asks NSPI to provide economic data sources, explain model variables, discuss alternatives to housing completions, clarify population changes, and justify the use of specific GDP/employment metrics in commercial and industrial models.
Please discuss why manufacturing GDP and manufacturing employment were 29 chosen for the industrial models. Were other drivers considered? Why were they not 30 chosen? Date Filed: June 19, 2024 NSPI (Synapse) IR-5 Page 1 of 4 REDACTED (CON...
AI summary The text requests an explanation for selecting manufacturing GDP and employment as key drivers in industrial load forecasting models, asking if other factors were considered. It references NSPI's responses to Synapse in the context of the 2024 Load Forecast Report (NSUARB M11689).
1 (d) Figure 16 is the comparison of economic indicators to bank forecasts. Assuming the 2 reference should be to Figure 11, population increased in 2022 and 2023 was due mainly 3 to immigration from other provinces and from outside the co...
AI summary The text discusses population growth in Nova Scotia from 2021 to 2033, driven by immigration, leading to increased housing completions and customer counts. Population and customer data are presented in a table showing annual changes, with projections indicating continued growth through 2033.
0.2% 538,212 0.7% 2032 1,135 0.2% 541,371 0.6% 2033 1,138 0.3% 544,269 0.5% 2034 1,141 0.3% 546,929 0.5% 10 11 (e) The non-manufacturing GDP and non-manufacturing employment variables have been 12 used in previous forecasts and provided co...
AI summary The document discusses the use of non-manufacturing GDP and employment variables in load forecasts, citing consistent model fit statistics. Manufacturing GDP and employment were selected for industrial models due to better overall model statistics, while exports and other variables showed poorer results. This relates to forecasting methodology in the 2024 Load Forecast Report (NSUARB M11689).
1 (g) Labour shortages and increased automation were not considered explicitly, but any 2 significant factors that would impact the employment or GDP variables would be included 3 in the underlying forecast provided by the Conference Board...
AI summary The text discusses economic forecasting methodologies, including the use of a 10-year model for industrial energy demand, statistical relevance of variables, CPI adjustments, and reliance on the Conference Board of Canada's (CBoC) likely future scenarios. It highlights potential model limitations and data sources.
f Canada’s view of the most likely future. 17 18 (k) Bank forecasts are checked for discrepancies in the near-term GDP and employment 19 forecasts, which are provided in Attachment 1. Date Filed: June 19, 2024 NSPI (Synapse) IR-5 Page 4 of...
AI summary The document references Canada's economic outlook, emphasizing checks on bank forecasts for GDP and employment discrepancies. It includes a 20-year CBoC forecast and is part of NSPI's 2024 Load Forecast Report, filed on June 19, 2024.
CBoC 20 Year Forecast (Dec 2023) Calculated (Average (Average (Average Aggregation) Aggregation) Aggregation) (Average Aggregation) (Average Aggregation) (Average (Average Housing Housing Current accounts Real Gross Domestic Real Gross Dom...
AI summary The text presents a 20-year economic forecast by the Conference Board of Canada (CBoC), including metrics like housing completions, GDP, employment, and consumer price indices for Nova Scotia, with data aggregated across various sectors and timeframes.
23124 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-5 Attachment 1 Page 2 of 4 Bank Source GDP Emp Housing 2024 2025 2024 2025 2024 2025 BMO Provincial Economic Outloo 0.9% 1.6% 0.8% 1.0% 10000 8000 RBC P...
AI summary The document presents economic and demographic data from 2013–2016, including GDP growth, employment trends, and housing completions, sourced from BMO, RBC, TD, and National Bank. This data is part of Synapse's 2024 Load Forecast Report, used for energy demand projections.
2034 2024 502021 7021 1083.24 29,718 6,826 0.040017 -5000 2025 508635 6614 1101.81 18,571 6,614 0.040426 Change in Res Customers Change in Population Housing Completions 2026 514805 6169 1113.59 11,784 6,169 0.040693 2027 520500 5695 1120....
AI summary The text presents numerical data tables covering projected customer changes, population shifts, housing completions, and household size trends from 2024 to 2034. It references a confidential '2024 Load Forecast Report' by Synapse, indicating analysis of energy demand forecasting and demographic factors influencing residential energy usage.
2.524167 2002 2.480833 2.60 2003 2.463333 2004 2.443333 2.50 2005 2.408333 2.40 2006 2.373333 Household Size 2007 2.344167 2.30 2008 2.324167 2009 2.3075 2.20 2010 2.296667 2011 2.279167 2.10 2012 2.259167 2.00 2013 2.234167 2014 2.215 1.9...
AI summary The 2024 Load Forecast Report (NSUARB M11689) includes NSPI's responses to Synapse Information Requests, presenting historical and projected electricity consumption data by household size from 2002 to 2034. The data highlights trends in energy usage patterns and load forecasting methodologies.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-6: 2 3 Residential End-Use Intensity Trends (Section 4.4, pp 30-50) 4 5 (a) Please provide in electronic spreadsheet for...
AI summary NSPI provided data from NRCan and U.S. EIA for residential and commercial models, detailing adjustments to align end-use intensities with NRCan reports and NS Power billing data. Attachments include raw data and modeling specifics.
households have lighting Date Filed: June 19, 2024 NSPI (Synapse) IR-6 Page 1 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The document references a 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It is part of a regulatory proceeding involving Nova Scotia Power Inc. and the Nova Scotia Utility and Review Board, focusing on energy forecasting and data disclosure.
8 9 (c) Please refer to 2024 LFR Attachment 01. On the tab labeled “Calibration,” individual end- 10 use consumption based on NRCan data is added together to give a household view of Date Filed: June 19, 2024 NSPI (Synapse) IR-6 Page 2 of...
AI summary NSPI's 2024 Load Forecast Report uses NRCan data for individual end-use consumption, aligns it with NS Power's billing records via a scaling factor, and applies this factor to annual end-use intensity values for calibration.
res tab). Because only 26 the electric heating component is forecast the E3 graphs cannot be reproduced, but 27 graphical representations of the heating components are as follows: Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 5 of 12...
AI summary The text includes a reference to the 2024 Load Forecast Report (NSUARB M11689) and mentions NSPI's responses to Synapse Information Requests. It also notes that the E3 graphs cannot be reproduced due to the electric heating component forecast.
779 116,394 2031 495,055 57,839 164,347 332,105 35,322 403,197 105,111 2032 495,055 54,587 167,510 346,064 37,590 418,912 94,404 2033 495,055 51,501 170,560 359,242 39,756 433,753 84,312 2034 495,055 48,779 173,270 371,695 41,693 447,782 7...
AI summary The text provides numerical data related to load forecasting and refers to the 2024 Load Forecast Report (NSUARB M11689), along with a mention of the E3 numbers in Attachment 1 (HP Stock tab). It also references a table estimating heat sources for new customers and notes that supplementary heating is estimated to be approximately 35 percent.
2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 2 3 The largest variances were predominantly in the winter months (Nov-Mar), 4 indica...
AI summary The 2024 Load Forecast Report (NSUARB M11689) indicates significant variances in winter months (Nov-Mar), likely due to unaccounted heating load. NSPI provided responses to Synapse Information Requests, and the report includes updated forecast models with increased heating intensities and reduced variance.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Residential Electric Vehicles (EV) (Section 4.4, pp 36-41) 4 5 (a) Please provide NSPI’s rationale and all data t...
AI summary NSPI has been asked to provide detailed information regarding its assumptions and data supporting EV load forecasts in the 2024 Load Forecast Report. The request includes details on EV scenarios, underlying data for figures, and explanations of load shaping tools and methodologies used.
22 method. The graph below shows an example of LDV weekly driving patterns 23 expressed as the probability that a driver is at a given location or is driving. Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 4 of 8 REDACTED (CONFIDENTIAL...
AI summary The document discusses the 2024 Load Forecast Report (NSUARB M11689) and includes NSPI's responses to Synapse Information Requests. It references a graph illustrating LDV weekly driving patterns and mentions the date filed as June 19, 2024.
and demonstrative of the potential of specific use cases for managed charging as 29 detailed in M11621. They were not used to develop peak load impacts of EVs. 30 Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 6 of 8 REDACTED (CONFIDEN...
AI summary The document references the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It mentions managed charging use cases detailed in M11621 but notes that they were not used to assess EV peak load impacts.
2024 Load Forecast Report Synapse IR-9 Attachment 1 Page 1 of 3
AI summary The text references the 2024 Load Forecast Report, specifically Synapse IR-9 Attachment 1, which is part of a regulatory proceeding. The document appears to be a technical attachment related to load forecasting, but no further details are provided.
ON REMOVED) 2024 Load Forecast Report Synapse IR-10 Attachment 1 Page 1 of 2 PV Forecast Based on 10% growth
AI summary The document references a 2024 Load Forecast Report and includes a PV Forecast based on 10% growth, likely related to energy planning and forecasting in Nova Scotia.
30 system adequacy. Date Filed: June 19, 2024 NSPI (Synapse) IR-16 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Response...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report, addressing topics such as TVP elasticities, population growth, household size, economic indicators, EV inputs, and communication with large customers. These responses are part of the NSUARB M11689 proceeding.
Itron’s experience in energy forecasting. As outlined on page 53 of the report, price 20 elasticity does not have a large impact on sales in our model compared to other components. 1 Bohi, Douglas R., and Mary Beth Zimmerman, “An Update on...
AI summary The document discusses Itron's energy forecasting experience, noting that price elasticity has a limited impact on sales compared to other model components. It references several studies on energy demand behavior and includes a 2024 Load Forecast Report filed by NSPI in response to Synapse Information Requests.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The document outlines NSPI's responses to Synapse Energy Economics' information requests regarding the 2024 Load Forecast Report, which was part of the NSUARB M11689 proceeding. The report is non-confidential and provides insights into load forecasting for Nova Scotia.
EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-19 Attachment 1 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse...
AI summary The 2024 Load Forecast Report (NSUARB M11689) has been filed, along with NSPI's responses to Synapse Information Requests. The report is part of the regulatory process and includes non-confidential information related to load forecasting.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-20: 2 3 Residential Sector (Section 5) 4 5 (a) Please provide the inputs and calculations used to produce the adjustment...
AI summary The document outlines a series of information requests related to the 2024 Load Forecast Report, focusing on residential sector sales forecasts, the impact of the COVID-19 variable, and the methodology used in creating specific figures. The requests aim to clarify modeling approaches, data sources, and changes in estimates since the previous forecast.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 Small Industrial (Section 7.1). 4 5 (a) Please explain and quantify the specific reasons for the differences fro...
AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report. The responses explain that load migration to RTR and changes in electrification load growth are key factors affecting forecast differences. Sales are expected to remain flat until 2027 due to RTR migration, after which they will increase with electrification growth.
ation of load to the RTR participant. Date Filed: June 19, 2024 NSPI (Synapse) IR-27 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFI...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report. Key points include a decrease in forecasted new project load by 93 GWh, a 99% representation of sector load by survey responses, and a +1.8% aggregate load change. Past forecasts were found to have overstated new project growth.
Comm Growth tab (grossed to system level using an 25 average loss factor of 7.6 percent). DSM is the cumulative DSM amount using a blended 26 average coefficient of 49 percent. 27 Date Filed: June 19, 2024 NSPI (Synapse) IR-32 Page 7 of 8...
AI summary The document discusses the 2024 Load Forecast Report, including the calculation of unexplained forecast differences and references to attachment materials. It outlines forecasting uncertainty and mentions the use of average loss and blended coefficients in DSM calculations.
rison 7 between top-down (current) and bottom-up (experimental, and main part of that section) 8 peak residential forecast an apples-to-apples comparison. A no-DSM adjustment would Date Filed: June 19, 2024 NSPI (Synapse) IR-33 Page 3 of 5...
AI summary The document discusses the 2024 Load Forecast Report and NSPI's responses to Synapse Information Requests. It mentions the assessment of model robustness using statistical metrics and the comparison of peak residential forecasts with and without DSM adjustments.
NA Ljung-Box Statistic 20.84 Prob (Ljung-Box) 0.6482 Skewness -0.105 Kurtosis 3.096 Date Filed: June 19, 2024 NSPI (Synapse) IR-33 Page 4 of 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Respo...
AI summary The document includes statistical analysis from a load forecast report, with values such as the Ljung-Box statistic, Jarque-Bera statistic, and related probabilities. It is part of a regulatory proceeding involving NSPI and Synapse, and is associated with NSUARB M11689.
1 Request IR-36: 2 3 Appendix A: Forecast 4 5 (a) Please provide in electronic format the specific calculations used to create the values 6 in Tables A1, and A2. If this information has already been provided in electronic 7 format in one o...
AI summary The response to Request IR-36 provides references to the 2024 Load Forecast Report for the calculations used in Tables A1 and A2. It also explains that the Interruptible Contribution to Peak (PHP) forecast is expected to remain steady at 147MW, with historical data showing significant differences between actual and forecast values.
t 24 included a binary for the month of September which was not included in the 2024 forecast 25 as it was not statistically relevant (T-stat of 1.979 and P-stat of 5.03 percent). Date Filed: June 19, 2024 NSPI (Synapse) IR-38 Page 2 of 2...
AI summary The document notes that a binary variable for September was excluded from the 2024 load forecast due to its lack of statistical relevance, as indicated by a T-stat of 1.979 and a P-stat of 5.03 percent.
Regression Sales Results Out of Monthly Model (kWh / HH) Year AContrib2Sales.AnnualAvgUse 2014 10,826.21 2015 10,953.36 2016 11,043.57 2017 11,027.94 2018 11,393.38 2019 11,650.83 2020 10,739.14 2021 11,266.11 2022 12,405.73 2023 12,626.60...
AI summary The document presents a regression sales results table showing annual average usage in kWh per household from 2014 to 2034, followed by a redacted section of the 2024 Load Forecast Report, Synapse IR-38, Attachment 1, Page 5 of 9.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (c) All calculations are completed within Metrix ND, the forecasting software used by NS 2 Power. Input values, coefficients and ou...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report. Calculations were performed using Metrix ND software, and attachments contain input values, coefficients, and model components. A binary variable for May 2023 was added to improve model fit, increasing the adjusted R-squared value from 0.908 to 0.912.
0 2,617,120.01 2033 16.87 0.00 0.00 0.00 0.00 0.00 7.80 0.00 0.00 2,644,332.48 2034 17.07 0.00 0.00 0.00 0.00 0.00 7.80 0.00 0.00 2,679,214.86 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-39 Attachment 1...
AI summary The text presents a table with financial data and load forecast information, including load indices for heating, cooling, and other factors, as well as dates and acronyms related to forecasting methods. The document is part of a regulatory proceeding and includes confidential information that has been redacted.
r GenIndices Heating GenIndices Cooling GenIndices Others XHeat XCool XOther Feb 18 May 20 Jun 20 22-Oct 22-Sep Covid 23-May ARMA
AI summary The text presents a table with various indices related to heating, cooling, and other factors, along with dates and an ARMA model. The content appears to be technical data used for analysis, possibly related to energy generation or demand forecasting.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-40: 2 3 Appendix B: Small Industrial Model 4 5 (a) Please provide in electronic spreadsheet format the data and the stat...
AI summary NSPI responded to Synapse's information request regarding the 2024 Load Forecast Report, explaining that the Small Industrial model calculations were done using Metrix ND and that the model specification has not changed since 2023, with Nova Scotia’s Manufacturing GDP still being the main driver.
se changes 11 improved the model fit, with an adjusted R-squared value using the old variable of 0.976 12 compared to an adjusted R-squared value of 0.982 using the new variables. Date Filed: June 19, 2024 NSPI (Synapse) IR-43 Page 2 of 2...
AI summary The text discusses improvements in a model's fit, with an adjusted R-squared value increasing from 0.976 to 0.982 when new variables were introduced. The document is part of a 2024 Load Forecast Report filed by NSPI (Synapse) on June 19, 2024.
.6 22,234.1 42,166.5 6,270.5 478,215.3 30.0 664.2 2034 12 259,811.8 16,801.1 153,845.5 22,356.9 42,478.3 6,379.0 501,672.7 31.0 674.3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to S...
AI summary NSPI responds to Synapse Information Requests regarding the 2024 Load Forecast Report. The responses include references to attachments for data related to forecast sensitivity analysis, Oracle input data, historical data, and calculations for Figure D8. The data in Appendix C is not weather normalized.
ario and New England, are 24 forecasting a shift of their peaks to winter with expected increases in electric space heating 25 and EV load in response to carbon reduction targets. Date Filed: June 19, 2024 NSPI (Synapse) IR-50 Page 1 of 1...
AI summary The document includes requests and responses related to load forecasting and municipal electric utility obligations. NSPI refers to Synapse IR-29 for municipal load forecasts and provides Attachment 1 for end-use peak share data.
(10,000 per forecast year) are produced. Then to highlight uncertainty around an output 29 (e.g, peak forecast), the lower 10th percentile value (P10) is highlighted in graphs and tables Date Filed: June 19, 2024 NSPI (Synapse) IR-53 Page...
AI summary The document discusses the 2024 Load Forecast Report, highlighting the use of percentile values (P10, P50, P90) to represent uncertainty in load forecasts. It notes that deterministic forecasts are used except in the sensitivity analysis section, with P50 values aligning closely with deterministic numbers.
N-8Evidence of Synapse (BCC)
26 passages
Evidence Regarding Nova Scotia Power’s 2024 Load Forecast Evidence RE: M11689 Prepared for the Nova Scotia Utility and Review Board July 11, 2024 AUTHORS Ben Havumaki Kenji Takahashi Aidan Glaser Schoff 485 Massachusetts Avenue, Suite 3 Ca...
AI summary This document provides evidence on Nova Scotia Power’s 2024 load forecast, including forecast comparisons, sector and DSM program overviews, and references to Board directives from prior proceedings. It outlines the context for the forecast review and incorporates recommendations from previous evaluations.
..................................................................................7 1.5. Recommendations from the Previous Forecast Review .....................................................8 2. ENERGY FORECAST .............................
AI summary The document outlines energy and peak demand forecasting methodologies, analyzing residential, commercial, industrial, and municipal sectors. It discusses DSM effects, sensitivity analysis, and provides recommendations for improving forecast accuracy and alignment with demand-side management strategies.
2027 2028 2029 2030 2031 2032 2033 2034 Source: Synapse from Figure 1 in NSPI’s 2024 Load Forecast Report (2024 Load Forecast) and responses to Synapse IR-1
AI summary The text presents a series of years (2027–2034) alongside a source citation referencing Synapse’s work in NSPI’s 2024 Load Forecast Report and responses to Synapse IR-1, indicating data related to long-term energy demand projections.
-57 -3 -61 -699 2034 Forecast 5,298 3,151 2,258 213 775 11,695 Source: Figure 55 from 2024 Load Forecast The historical trend for firm peak demand shows a general increase, as shown in Figure 2 below. We plotted the actual rather than the...
AI summary The 2024 load forecast indicates a 14.6% increase in firm peak demand (323 MW), primarily driven by electrification. Electric vehicles (EVs) are identified as the largest contributor to this growth. The forecast shows slower growth rates post-2024, with historical trends reflecting increasing demand. Adjustments to modeled values from SAE are detailed in Figure 61/Table 2.
2027 2028 2029 2030 2031 2032 2033 2034 Source: Synapse from Figure 2 from 2024 Load Forecast and responses to Synapse IR-1
AI summary The text presents a load forecast timeline spanning 2027–2034, sourced from Synapse's 2024 Load Forecast and responses to Synapse IR-1. It appears to outline projected energy demand metrics over the period, referencing prior forecasting work.
281 -37 -68 4 115 -145 2,670 147 2,851 mitigation) Source: Figure 61 from 2024 Load Forecast NSPI provides the percent error and mean absolute percent error for the firm peak forecast in Figure C5 of Appendix C. In aggregate, the average p...
AI summary NSPI's 2024 load forecast shows under-forecasting trends, with residential load growth driven by EVs and electrification. The forecast's accuracy is questioned due to consistent under-forecasting, while sectoral energy use trends highlight residential dominance. Synapse Energy Economics provides analysis on these issues.
024 Load Forecast 5 embedded in forecast variables but instead to subtract these out at 100 percent over the relevant period of time. 3 Table 4. DSM Program savings versus forecast adjustments
AI summary The text discusses adjusting load forecasts by subtracting demand-side management (DSM) program savings at 100% over the relevant period, rather than embedding them in forecast variables. Table 4 compares DSM program savings against forecast adjustments, highlighting the methodology for incorporating program impacts into load forecasting.
24.7 51% 2033 71.3 43.4 7.7 31.7 28.2 39.6 22.9 51% 2034 69.1 44.0 7.8 30.7 28.6 38.4 23.2 51% Source: Synapse from Figure 35 from 2024 Load Forecast Recommendations and Considerations We ask that NSPI explore the benefits of increasing DS...
AI summary The document references a 2024 load forecast and recommends NSPI increase DSM levels. It also outlines Board directives from Matter 11108, including implementing IRP, AMI, and reviewing carbon emission assumptions.
ion that NSPI would continue to explore all realistic scenarios relating to hydrogen production and incorporate these into the load forecast modeling. 4 NSUARB Decision in Matter 11108, page 6. Synapse Energy Economics, Inc. Evidence Regar...
AI summary NSPI is directed to explore hydrogen production scenarios for load forecasting and address significant variance between 2023 forecasts and actual 2022 Net System Requirement. The NSUARB Decision in Matter 11108 emphasized examining factors like cooling demand, heat pump adoption, and residential electricity drivers. NSPI's responsiveness to these directives is noted with some exceptions.
d Forecast 8 2. ENERGY FORECAST In this section, we begin with a review of NSPI’s modeling methodology, and then consider in turn the modeling approaches and results for each of the sectors. 2.1. Major Inputs and Regression Models In addit...
AI summary NSPI's energy forecast relies on economic projections from the Conference Board of Canada (CBoC), but the Board directed NSPI to evaluate alternative inputs. NSPI's 2024 load forecast compares CBoC data with major Canadian banks' forecasts, highlighting uncertainties in long-term projections. Sensitivity analyses are recommended to address these uncertainties.
2025, to the extent that such forecasts exist, and also, more generally, with the reliability of the CBoC’s forecast further into the future. Sensitivity analyses are useful for looking at this issue. To estimate growth in new customers, t...
AI summary The text evaluates the reliability of CBoC forecasts and discusses residential load forecasting methods, noting inconsistencies in how existing and new customer load increases are presented. It highlights the need for regular review of forecasting drivers and clearer labeling of statistical data.
esidential forecast, including the load-reducing effects of DSM programs, increases by 2.3 percent over the forecast period, from 2024 to 2034. Without DSM programs, the increase would be 9.1 percent. The two largest contributors to the in...
AI summary The residential load forecast from 2024–2034 shows a 2.3% increase with DSM programs, versus 9.1% without them. Key drivers include new customers (7.0%) and EV load (6.0%), offset by solar PV and DSM. The forecast uses a regression-based SAE model incorporating factors like heating, cooling, and time-fixed effects, with XHeat influenced by heating degree days, income, and equipment efficiency.
ilding size. The major change drivers for XHeat are electric (resistance) heat, heat pumps, and, to a lesser extent, secondary heat. The net change over the forecast period is a 12.5 percent increase. The XCool variable is the product of t...
AI summary The document details load forecast variables XHeat, XCool, and XOther, driven by factors like heating technologies, cooling saturation, and appliance efficiency. XHeat increases 12.5%, XCool surges 57.8% due to heat pump cooling, while XOther declines 2.1% from reduced lighting and TV use. Residential energy use is 45% heating, 4% cooling, 53% other, with existing customers seeing 5.6% higher heating loads over the forecast period.
rs, average heating load increases by 5.6 percent, average cooling load increases by 2.2 percent, and the average load associated with other end uses increases by 1.1 percent over the forecast period. The output of the SAE model is an aver...
AI summary The text outlines projected load increases for residential heating, cooling, and other end uses, and describes NSPI's method for forecasting residential consumption using the SAE model, including adjustments for new customers, EV load, PV generation, RTR sales, and DSM impacts.
nd response for water heaters has concluded.24 NSPI indicates that the data collected from these pilots will be used to inform future load forecasts. While Synapse 30 2024 Load Forecast, page 35. Synapse Energy Economics, Inc. Evidence Reg...
AI summary The document discusses NSPI's 2024 load forecast, noting the exclusion of heat pump water heaters despite their potential energy savings and peak load reduction. Synapse Energy Economics recommends modeling them as a separate end-use technology due to projected electric water heater growth and energy efficiency benefits. Electric vehicles are also identified as a separate load growth area.
ill continue to monitor 46 2023 Load Forecast, Appendix B, page 6 and 2024 Load Forecast, Appendix B, page 8. 47 2024 Load Forecast, page 58. 48 NSUARB Decision in Matter 11108, page 6. Synapse Energy Economics, Inc. Evidence Regarding Nov...
AI summary The document discusses concerns regarding Nova Scotia Power’s (NSPI) 2024 Load Forecast, particularly the reliance on housing completions as a proxy for customer growth. It highlights potential issues with this method, such as the ongoing housing shortage affecting the correlation between housing starts and customer growth. Recommendations are made for NSPI to validate this proxy and consider alternative forecasting methods.
wth. Recommendations and Considerations NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. Other The value of price elasticity has...
AI summary NSPI is advised to validate the use of new home construction as a proxy for customer growth and to conduct a literature review on price elasticity. The Board has raised concerns about NSPI's modeling of work-from-home impacts due to the pandemic, and Synapse requests more empirical justification for NSPI's approach.
ngoing COVID consumption changes and the modeling results obtained as a result of this approach. Specifically, NSPI should explain why it has forecast a 49 Board Decision in Matter M10569, page 5. 50 Response to Synapse IR-18(d). 51 NSUARB...
AI summary The document critiques NSPI's approach to modeling the enduring effects of COVID-19 on residential electricity consumption, questioning the reduction in the impact of the pandemic on load forecasts and suggesting alternative econometric strategies for more accurate modeling.
at enduring COVID-related impacts from August 2022 and onward are 65 percent lower than those same effects from July 2020 through July 2022. However, this assumption appears to lack empirical support. Moreover, given that the load forecast...
AI summary The document discusses the need for NSPI to reassess its load forecasting model for the residential sector, particularly regarding the impact of COVID-19 and demographic factors. It also highlights discrepancies between current and previous commercial sector load forecasts and notes that the statistical models for the General Service subsector are satisfactory.
fic solar or DSM projections. Overall, the forecast appears reasonable given the inherent uncertainties, but it does raise the following issues for consideration: Recommendations and Considerations Given that significant changes to the loa...
AI summary The 2024 load forecast for Nova Scotia Power (NSPI) is deemed reasonable but highlights the need for NSPI to address uncertainties related to EV and solar penetration, as well as the impacts of RTR and hybrid heating. The forecast also indicates that the industrial sector will remain relatively stable, with small and medium subsectors showing slight growth and decline, respectively.
0.4 percent annually as load migrates to RTR providers. The Large (also called Other) category represents approximately two-thirds of the industrial load, with load projected to remain essential flat. The forecast projects increased electr...
AI summary The industrial load forecast projects a 30 GWh increase by 2034 due to electrification, with DSM savings at 57 GWh and RTR resources at 59 GWh. The forecast assumes current major customer operations and raises questions about the potential for greater industrial electrification, savings, and the impacts of real time rates and increased RTR.
EVs are a substantial contributor to peak growth, though diminished in expected peak contribution relative to last year’s load forecast projections. We would like to see a more complete evaluation of the options to control this growth in t...
AI summary The document discusses the impact of electric vehicles (EVs) on peak load growth and critiques NSPI's use of an ELCC factor from a 2019 study for demand response forecasting. It highlights concerns about the methodology and suggests a need for more comprehensive analysis and updated projections.
lausible, although there are many uncertainties, and some aspects need refinement. There should be more discussion of the underlying factors causing peak growth and what can be done to mitigate it. 4. SENSITIVITY ANALYSIS The forecast Repo...
AI summary The text discusses the importance of sensitivity analysis in load forecasting, highlighting the impact of various factors such as hydrogen production facilities, battery adoption, and weather/economic drivers on peak load. It emphasizes the need to consider multiple scenarios, particularly for uncertain resources like heat pumps, EVs, and DSM, to ensure accurate and robust forecasting.
end uses that pose uncertainties about their future adoption rates, in particular heat pumps, EVs, DSM, and demand response, we highly recommend that NSPI develop a few different scenarios (e.g., low Synapse Energy Economics, Inc. Evidence...
AI summary The text recommends that NSPI develop multiple load forecast scenarios, including low, reference, and high cases, to account for uncertainties in technologies like heat pumps, EVs, and DSM. It also highlights concerns about under-forecasting of firm peak load and suggests evaluating the possibility of higher-than-forecast peaks.
d-use scenarios, NSPI should also evaluate the possibility of a higher than forecast peak in light of the systematic under-forecasting of peak that is noted above. Recommendations and Considerations For the major resources and end uses tha...
AI summary The document recommends that NSPI evaluate higher-than-forecast peak demand scenarios, develop multiple scenarios for uncertain resources like heat pumps and DSM, and conduct sensitivity analyses using new technologies. It also asks NSPI to explore increasing DSM levels and improve modeling of heat pump impacts on energy and peak load.
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 ELCC a...
AI summary The text outlines recommendations for NSPI to investigate time-of-use rates, analyze portfolio ELCC, evaluate technologies like thermal storage and heat pumps, quantify electrification impacts, develop scenarios for uncertain resource adoption, and conduct sensitivity analyses to mitigate projected peak load increases.
N-9Rebuttal Evidence - NSPI
19 passages
Nova Scotia Utility and Review Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended M11689 2024 Load Forecast Report NS Power Rebuttal Evidence September 6, 2024 NON-CONFIDENTIAL 2024 Load Forecast Report Rebut...
AI summary NS Power submitted a rebuttal to the 2024 Load Forecast Report under the Public Utilities Act, R.S.N.S. 1989, c.380, as amended. The document is part of regulatory proceedings (M11689) and addresses energy usage forecasting methodologies.
.1.21 Recommendation 21.............................................................................................................. 16 27 2.1.22 Recommendation 22..............................................................................
AI summary The document outlines recommendations (21, 22) and subsections under the Consumer Advocate (Recommendations 1-3) related to a 2024 Load Forecast Report Rebuttal Evidence. The filing date is September 6, 2024, with the document labeled 'Non-Confidential' and on Page 2 of 25.
Page 2 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential 32 2.2.4 Recommendation 4................................................................................................................ 20 33 2.2.5 Recommendation...
AI summary The document outlines the structure of the 2024 Load Forecast Report Rebuttal Evidence, including sections on recommendations, the Small Business Advocate's input, and a conclusion. It is part of a regulatory proceeding filed on September 6, 2024, addressing load forecasting methodologies and stakeholder responses.
Page 3 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary The document is titled '2024 Load Forecast Report Rebuttal Evidence' and is marked as non-confidential. It appears to be part of a regulatory proceeding, though no further details or arguments are provided in the text.
1 1.0 INTRODUCTION 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules 1, 4 the Nova Scotia Power System Operator (NSPSO) is required each year to provide the Nova Scotia 5 Utility and Review B...
AI summary The Nova Scotia Power System Operator submitted a 2024 Load Forecast Report to the NSUARB, prompting a Hearing Order. Interventions were filed by advocates and EfficiencyOne, with NS Power responding to information requests. Synapse praised the report's improved expository quality and forecasting methodology.
reports have changed considerably over time. The 2024 27 Load Forecast Report (Report) continues the trend of improvement in expository 28 quality by providing additional information. 3 29 1 Nova Scotia Wholesale and Renewable to Retail El...
AI summary The 2024 Load Forecast Report improves expository quality with additional information. It references the NSUARB Hearing Order (M11689) and Synapse Evidence submitted in the proceeding. The report's methodology and supporting documentation are central to the regulatory analysis.
1 // 2 3 We support NSPI’s ongoing efforts to improve the transparency and accuracy of 4 the load forecast. There is still more to do; but overall, NSPI’s Report is very well 5 done and satisfactorily explains the underlying factors drivin...
AI summary The text supports NSPI's efforts to enhance load forecast transparency and accuracy, acknowledging progress but noting ongoing improvements needed. NS Power has adopted some intervenor recommendations but faces constraints in others. Integration of data from initiatives like Demand Response and Smart Grid Nova Scotia pilots is discussed, with AMI data integration expected to evolve over time as models adapt to granular data.
l heat pump water heaters as a separate end-use 27 technology in the next load forecast. 8 28 29 NS Power Response: 30 31 Please refer to the response to Synapse Recommendation 18. 32 7 Synapse Evidence, 2024 Load Forecast Report (M11689),...
AI summary The document references Synapse's 2024 Load Forecast Report (M11689), which includes heat pump water heaters as a separate end-use technology. NS Power directs attention to their prior response to Synapse Recommendation 18, indicating ongoing discussion about load forecasting methodologies and technology classification.
Page 9 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary The document title indicates a rebuttal submission related to the 2024 Load Forecast Report, part of a regulatory proceeding. The 'Non-Confidential' designation suggests the content is publicly accessible, though no further details are provided in the excerpt.
1 the 2024 Load Forecast report, this is an input to the forecast. Other than household size, 2 demographics are not taken into account explicitly, but the impact of any other changes in the 3 underlying demographics (average age for insta...
AI summary The Board recommends NSPI reassess its load forecast model's treatment of work-from-home behavior and household demographics, and address sensitivities in EV/solar penetration and hybrid heating impacts. NS Power acknowledges the need to re-evaluate the model's statistical validity but notes current limitations in data variables.
d the 28 impacts of the RTR and hybrid heating, NSPI should address the range of 29 possibilities (sensitivities) in these domains to produce a more robust forecast. 15 30 14 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2...
AI summary NS Power emphasizes the importance of the 2024 Load Forecast Report for planning and operations, noting year-over-year load variances. It urges NSPI to address sensitivities in RTR and hybrid heating impacts for robust forecasting, citing Synapse's report (M11689).
asis for the planning and 4 overall operating activities to serve customer load.” 16 Despite year-over-year variances from load 15F 5 forecast to load actuals, the load forecast continues to provide a reliable basis on which to plan and 6...
AI summary The document discusses the reliability of load forecasts despite variances, emphasizing sensitivity analysis over scenario planning. It requests NSPI to assess solar and DSM impacts on the Large General Service forecast and industrial electrification effects. NS Power notes existing DSM inclusion and solar impact assumptions, with future updates if projects are identified.
Page 13 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary The document titled '2024 Load Forecast Report Rebuttal Evidence' is submitted as non-confidential evidence in a regulatory proceeding, likely addressing challenges or disputes related to the 2024 load forecast.
Page 14 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary The document titled '2024 Load Forecast Report Rebuttal Evidence' is on page 14 of 25 and marked as non-confidential. It appears to address challenges or counterarguments related to load forecasting for 2024, though specific claims or data are not detailed in the provided text.
Page 15 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential 1 lack of forecast data (EIA does not provide estimates for either due to the small number) mean that 2 adjustments in the expected efficiency of water heaters and...
AI summary NS Power responds to recommendations in the 2024 Load Forecast Report, referencing Synapse's analysis. It addresses electrification impacts, scenario development for uncertain resources (heat pumps, EVs, DSM, demand response), and potential peak under-forecasting, citing Synapse's evidence (M11689).
ure, seeking testimony from 29 NS Power and Eastward Energy, and invite comments from other key stakeholders, 30 including those involved in the propane and fuel oil markets. 33 31 31 Consumer Advocate Evidence, 2024 Load Forecast Report (...
AI summary The Consumer Advocate submitted evidence related to the 2024 Load Forecast Report (M11689), seeking testimony from NS Power and Eastward Energy while inviting stakeholder input, particularly from propane and fuel oil market participants.
Page 18 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary The document references the 2024 Load Forecast Report Rebuttal Evidence, indicating a regulatory proceeding involving analysis of load forecasting methodologies and potential challenges to the report's findings.
h for Routine D062 [are] interrelated with Routines D004 and D061. NS 28 Power stated that population growth and economic growth in Nova Scotia were the 29 primary causes of these Routines exceeding their budgets. Based on recent 30 increa...
AI summary Nova Scotia Power attributes the exceedance of routine budgets to population and economic growth, and plans to review forecasting methodologies incorporating housing starts, population growth, and electrification trends. Evidence is cited from the 2024 Load Forecast Report and the Consumer Advocate's submission.
1 2025 ACE Plan application, the Board directs NS Power to provide details on 2 changes to the D004, D061 and D062 Routine budget estimation methods that were 3 assessed and provide explanations as to why they were, or were not, applied. 4...
AI summary The Board directs NS Power to provide details on changes to budget estimation methods for D004, D061, and D062 routines and to examine material cost increases. The SBA recommends using insights from the Smart Grid Nova Scotia pilot project in load forecast models.
94266NSUARB (NSPI) IR-1 to IR-26
13 passages
M11689 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: THE PUBLIC UTILITIES ACT - and - IN THE MATTER OF: AN APPLICATION by NOVA SCOTIA POWER INCOPROATED (NS Power) 2024 Load Forecast Report INFORMATION REQUESTS To: Nova Scotia Powe...
AI summary The Nova Scotia Utility and Review Board has issued an information request to Nova Scotia Power Incorporated (NS Power) regarding its 2024 Load Forecast Report. Responses are due by June 19, 2024, with contact details provided for submission. The request is part of regulatory oversight under the Public Utilities Act.
Document: 313458 Date Filed: 05/28/24 UARB Page 1 1 Request IR-1: 2 Figure 3: Historic and Forecast Net System Requirement and System Peak shows that System 3 Peak growth from 2023 to 2024F is -3.7% and the NSR growth from 2024F to 2025F i...
AI summary The document contains four requests questioning NS Power's load forecasting methodology, including discrepancies in NSR and system peak trends, HDD/CDD anomalies in summer months, and changes in forecasting equations. It challenges NS Power's assumptions about load growth, climate trends, and the impact of cooling technologies on energy demand.
discuss. 26 27 Request IR-4: 28 In Figures 7 and 8, the values applied in the equations and the resulting R2 have changed from 29 the 2023 Load Forecast Report. 30 a) Please explain the changed values in the equation. 31 b) Please briefly...
AI summary Request IR-4 seeks clarification on changes in equations and R2 values from the 2023 Load Forecast Report, specifically in Figures 7 and 8. The request asks for an explanation of altered equation values and a brief discussion on the impact of the changed R2 and model fit.
ing R2 have changed from 29 the 2023 Load Forecast Report. 30 a) Please explain the changed values in the equation. 31 b) Please briefly discuss the change in R2 and fit of the model.
AI summary The text requests clarification on changes in R2 values from the 2023 Load Forecast Report, asking for explanations of the equation changes and a discussion of the model's fit. It focuses on statistical model adjustments and their implications for load forecasting accuracy.
Document: 313458 Date Filed: 05/28/24 UARB Page 2 1 Request IR-5: 2 Page 23 of the Report indicates that NS Power expects Nova Scotia’s annual population growth 3 peaked in 2023. 4 a) Why does NS Power consider that the trend of population...
AI summary The document contains requests for clarification on NS Power's population growth projections, housing forecast adjustments, and changes in economic driver data. Questions focus on alignment with provincial targets, data sources, and impacts of housing initiatives on energy demand forecasts.
Document: 313458 Date Filed: 05/28/24 UARB Page 3 1 Request IR-8: 2 Figure 16: Economic Forecast Comparison employs data from three of Canada’s Big 5 banks and 3 National Bank. Board Staff find this table provides a useful comparison again...
AI summary The document contains four requests (IR-8 to IR-11) questioning NS Power's economic forecast data sources, adoption rates for emission goals, heat pump saturation assumptions, and verification of installation figures. It seeks clarification on omitted bank data, employment projection adjustments, uptake rate timelines, and evidence for 100% heat pump saturation by 2050.
Document: 313458 Date Filed: 05/28/24 UARB Page 4 1 c) The report notes that there are increased heating hours from more customers working 2 from home. Has there been an increase in cooling intensity to reflect greater work from 3 home emp...
AI summary The document contains a series of requests from Board Staff to NS Power, seeking clarifications on load forecasting assumptions, electrification projections, and energy efficiency data. Topics include heating/cooling intensity, water heater saturation, EV charging assumptions, PV impact revisions, and commercial/industrial electrification forecasts.
Industrial Electrification Forecasts (cumulative) present values that 31 are different from the 2023 Load Forecast Report. Please explain the factors that have changed 32 from the 2023 report, resulting in lower estimates.
AI summary The text requests an explanation for discrepancies between the 2023 Load Forecast Report and updated Industrial Electrification Forecasts, specifically why the latter presents lower estimates. It seeks clarification on factors contributing to the change in projected values.
Document: 313458 Date Filed: 05/28/24 UARB Page 5 1 Request IR-17: 2 Page 52 of the Report discusses the price impact to sales through imposed price elasticities and 3 explains that the SAE models use a price elasticity of -0.15. In matter...
AI summary The UARB requests clarification from NS Power on three issues: (1) why different price elasticities were not applied to rate classes despite evidence from Appendix C; (2) discrepancies in population growth forecasts between the 2023 Load Forecast Report and the Conference Board of Canada; and (3) the basis for the structural index in the Report. These questions challenge NS Power's modeling approaches and data reconciliation.
wth against deaths and projected 22 emigrations? 23 24 Request IR-19: 25 On page 60, the Report notes that the structural index, which considers building shell efficiency, 26 is based on estimates from 2013. This appears to be stale data....
AI summary Two requests challenge NS Power's use of 2013 data for building efficiency estimates and seek clarification on discrepancies between the 2023 Load Forecast Report and updated figures for building characteristics and structural indices.
ex; the forecast for BSE Heat EIA (New 31 England), BSE Heat NS decreased from the figures presented in the 2023 Load Forecast Report, 32 while Floor Area increased. Please explain the changes.
AI summary The text requests an explanation for the decrease in the forecast for BSE Heat EIA (New England) and BSE Heat NS compared to the 2023 Load Forecast Report, despite an increase in Floor Area. The inquiry focuses on reconciling these discrepancies in energy demand projections.
Document: 313458 Date Filed: 05/28/24 UARB Page 6 1 Request IR-21: 2 Section 6.0 examines the Commercial Sector and indicates that a detailed breakdown of the two 3 commercial SAE models are in Attachments 6 and 7. Page 65 explains that th...
AI summary The document contains five requests (IR-21 to IR-25) questioning NS Power's modeling assumptions, including outdated economic data, NSR trends, RTR impact, heating load forecasts, and data discrepancies. Requests focus on model accuracy, forecasting methodology, and alignment with current economic and energy trends.
aff note that this table is different from Figure 67 in the 2023 29 Load Forecast Report which presented system peak data for the same event. Please explain the 30 reason for the discrepancies. Document: 313458 Date Filed: 05/28/24 UARB Pa...
AI summary The text requests clarification on discrepancies between a table and Figure 67 in the 2023 Load Forecast Report regarding system peak data. It also asks NS Power to explain their projection of only two hydrogen facilities by 2034 and to provide a demand table for these facilities.
94285BCC-Synapse (NSPI) IR-1 to IR-54
20 passages
2024 M11689 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: The Public Utilities Act - and - IN THE MATTER OF: Nova Scotia Power Incorporated (NS Power) 10 - Year Energy and Demand Forecast (2024 Load Forecast Report) NON-CONFIDENTI...
AI summary Synapse Energy Economics Inc. submitted a non-confidential information request to Nova Scotia Power Inc. regarding its 2024 Load Forecast Report under the Public Utilities Act. The request seeks data related to the 10-Year Energy and Demand Forecast, with responses due by June 19, 2023.
Date Filed: May 29, 2024 Synapse (NSPI) Page 1 of 24 1 Request IR-1: 2 Report Tables 3 a. Please provide in electronic spreadsheet format all tables and graphs with identification of 4 the data source(s) that appear in the load forecast re...
AI summary The document outlines requests for electronic spreadsheet data, including load forecasts, historical sales data, and billed versus accrued sales comparisons. It specifies detailed data requirements for customer sales, system load, and unmetered sales, emphasizing transparency and accuracy in reporting.
ear. 28 b. The report notes that it uses monthly billed sales data from January 2014 to 29 December 2023 for the residential and commercial energy forecasts, and sales data from 30 January 2014 to December 2023 for Small Industrial, and fr...
AI summary The document requests clarification on data periods used for energy forecasts (residential, commercial, and industrial sectors) and details on HDD/CDD calculation methodologies, including temperature data and spreadsheet formats for analysis. It also seeks explanations on how internal and solar heat gains influence these calculations.
r the same period. 14 d. Please provide the calculations used to construct the data values in Figure 7: HDD Trend 15 from the Historic Annual HDD shown in Figure 6. Please identify the years represented 16 on the x-axis for Figure 7. 17 e....
AI summary The text contains a series of data requests related to heating degree days (HDD), cooling degree days (CDD), economic models, and housing completions. It asks for calculations, spreadsheet data sources, and explanations of variables like work-from-home trends, with references to prior regulatory decisions.
Date Filed: May 29, 2024 Synapse (NSPI) Page 3 of 24 1 term,” please discuss in detail any alternatives to housing completions considered and the 2 relative merits of each alternative considered. 3 d. Regarding Figure 16 and the historical...
AI summary The document outlines regulatory requests for clarifications on population projections, economic drivers for industrial/commercial sectors, inflation adjustments, and data sources for residential energy use. Questions focus on forecasting methodologies, economic scenario selection, and data validation.
Date Filed: May 29, 2024 Synapse (NSPI) Page 7 of 24 1 systems and separately for electric resistance water heaters and heat pump water 2 heaters. 3 d. What are the existing or proposed standards for improving hot water efficiency? 4 e. Pl...
AI summary The document outlines requests for information regarding NSPI's programs for heat pump water heaters, load control strategies, impact evaluations, and EV scenario assumptions. It seeks data on program offerings, load management impacts, jurisdictional comparisons, and EV forecasting methodologies.
ntial model more robust, considering the following: 24 o Given the continued population growth in Nova Scotia and ongoing housing 25 shortage, re-evaluate the use of housing completions for the near-term. 26 o Consider incorporating househ...
AI summary The text requests re-evaluation of housing completions, demographic factors, and economic data in energy models, along with updates to EV adoption rates and infrastructure communication. It also seeks clarification on price elasticity assumptions and data sources for electricity pricing models.
b. Please provide the source data and calculations behind the results in Figure 37. 25 c. The report states concerning the COVID-19 variable that, “The impact of the variable to 26 the Residential forecast is approximately +100 GWh in 2024...
AI summary The text requests clarification on energy forecasting methodologies, specifically the impact of the COVID-19 variable on residential sales, the basis for expecting its decreasing magnitude, and changes in econometric approaches since prior forecasts. It seeks source data, interpretation of modeled effects, and analysis supporting future expectations.
t support this expectation. 34 e. Has the econometric approach to modeling the ongoing impacts of COVID-19 changed 35 since the previous load forecast? If so, please explain in detail.
AI summary The text raises a question about whether the econometric approach to modeling the ongoing impacts of COVID-19 on load forecasts has changed since the previous forecast, requesting a detailed explanation if so.
Date Filed: May 29, 2024 Synapse (NSPI) Page 13 of 24 1 f. Has the estimated impact of ongoing changes associated with COVID-19 changed since 2 the previous load forecast? If so, please explain in detail. 3 g. Please provide the inputs and...
AI summary The document includes regulatory requests for detailed explanations on load forecast assumptions, home size growth, EV charging behavior impacts, and commercial sector load effects from COVID-19. Questions focus on methodology, data inputs, and regulatory implications for energy efficiency and rate design.
lease explain and quantify the impacts of COVID on the 2022 and 2023 loads. 27 b. Please explain and quantify the ongoing effects of COVID in the commercial forecast. 28 c. Please explain and quantify the specific reasons for the differenc...
AI summary The text requests Nova Scotia Power (NSP) to explain and quantify the impacts of COVID-19 on 2022/2023 load forecasts, ongoing effects in commercial forecasts, and reasons for discrepancies between current and prior forecasts, including reduced sales growth projections.
ous forecast. 31 32 Request IR-23: 33 Small General Service (Section 6.1). 34 a. Please explain and quantify the specific reasons for the differences from the previous 35 forecast.
AI summary The text references a request (IR-23) asking for an explanation and quantification of differences in the Small General Service forecast compared to previous forecasts, focusing on Section 6.1 of the document.
Date Filed: May 29, 2024 Synapse (NSPI) Page 14 of 24 1 b. Please identify and quantify in detail the specific components in the forecast model that 2 are causing the increase starting about 2025 as shown in Figure 45. 3 4 5 6 7 Request IR...
AI summary The document contains regulatory requests (IR-24 to IR-26) seeking detailed explanations for forecast discrepancies in energy demand, including EV load impacts, DSM program effects, and solar generation growth. Requests focus on quantifying changes in model variables (XHeat, XCool, XOther) and differences between current and previous forecasts for General Service, Large General Service, and Small Industrial categories.
tify the specific reasons for the differences from the previous 33 forecast. 34 b. Please explain why the sales forecast appears to increase in its growth rate after about 35 2026. 36 Date Filed: May 29, 2024 Synapse (NSPI) Page 15 of 24 1...
AI summary The document outlines requests for clarification on forecast discrepancies, industrial load changes, municipal energy needs, system losses, and net system requirements from Nova Scotia Power Incorporated (NSPI), emphasizing the need for detailed explanations and data verification.
ted in the forecast? 29 30 Request IR-31: 31 Net System Requirement (Section 9). 32 a. Please provide the source data and the calculations used to produce the values in 33 Figure 53. 34 b. Please provide the source data and the calculation...
AI summary Request IR-31 seeks source data and calculations for Net System Requirement figures (53, 54, 55) under Section 9, focusing on methodology and data transparency for system planning.
gure 69 between 24 the Residential coincidental peak load with and without DSM. Please also explain the 25 relationship between these differences and the DSM values in Figure 35 and the demand 26 response values in Figure 56. 27 f. How is...
AI summary The text includes several requests for information related to sensitivity analysis, forecasting, and modeling in an integrated resource plan. It asks for data and explanations regarding the impact of demand-side management, model robustness, and the variables used in forecasts and residential models.
in electronic spreadsheet format the data and the statistical model 21 parameters and the full results used to produce the model coefficients and statistics tables. 22 b. Please describe the method and data used to weigh the X variable. 23...
AI summary The document contains requests for detailed data and model parameters related to forecasting, including statistical models, load values, and sensitivity analysis. These requests aim to ensure transparency and accuracy in the forecasting process.
13 a. Please provide the model outputs in electronic form. 14 b. Please provide the full set of Oracle input data in electronic format so that it can be 15 replicated. 16 c. Please provide the historical data and forecasts used to develop...
AI summary The text outlines several information requests related to modeling, data replication, regulatory directives, and peak demand analysis. These requests pertain to providing detailed outputs, historical data, forecasts, and summaries of regulatory targets and generation mix.
Date Filed: May 29, 2024 Synapse (NSPI) Page 22 of 24 1 Electric Vehicles (Section 4.4, p. 40): 2 a. Please provide the percent of all vehicle sales, and electric vehicles as a percent of total 3 vehicle stock for each year through 2050. 4...
AI summary The document contains several requests for information related to electric vehicle adoption, peak load forecasting, municipal load differences, end-use peak shares, and forecast sensitivities. These requests are part of an ongoing regulatory proceeding involving Nova Scotia Power Incorporated.
hroughout the report is a 50/50 or 90/10 31 forecast. Please describe the rationale for selecting the type of forecast. If it is neither 32 50/50 or 90/10, please describe how the forecast compares to 50/50 or 90/10 forecast 33 methodologi...
AI summary The text requests an explanation of the rationale for selecting a 50/50 or 90/10 forecast methodology and asks for a comparison of forecast uptakes to various optimal scenarios, including socially optimal, least-cost, and most carbon-abating uptakes. It also inquires about changes in optimizing variables over time and compliance with regulatory requirements.