REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with confidential information removed. No substantive content or analysis is visible in the provided text.
1 TABLE OF CONTENTS 2 3 1.0 Executive Summary ............................................................................................................. 7 4 2.0 Introduction .................................................................
AI summary The text is a table of contents from a regulatory proceeding document, outlining sections such as forecasting approach, historical energy data, weather, economic information, price data, and sector-specific analyses (residential, commercial). It structures the report's content without discussing specific claims or arguments.
TE: April 29, 2022 Page 2 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with no details provided. It is part of a regulatory proceeding in Nova Scotia.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with confidential information removed. No substantive content or analysis is visible in the provided text.
1 List of Figures 2 3 Figure 1: Historical and Predicted Annual Net System Requirement ............................................ 8 4 Figure 2: Historical and Predicted Annual System Peak ....................................................
AI summary The text lists figures from a regulatory proceeding, covering historical and predicted system requirements, peak demand, heating/cooling degree days (HDD/CDD) trends, weather data, and forecasting methods. Key themes include system reliability, infrastructure planning, and weather impact analysis.
e Assumptions and Load/Peak Modeling Results ................................ 43 30 Figure 28: EV Impact to Energy and Peak Forecasts (cumulative) .............................................. 45 DATE: April 29, 2022 Page 3 of 98 REDACTED...
AI summary The 2022 Load Forecast Report discusses assumptions and load/peak modeling results, including redacted sections and a figure analyzing EV impact on energy and peak forecasts. The document is dated April 29, 2022, and is part of a regulatory proceeding.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with confidential information removed. No substantive content or analysis is visible in the provided text.
98 11 DATE: April 29, 2022 Page 5 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Attachments (Electronic Only) 2 3 Attachment 1: Residential Intensities 4 Attachment 2: Commercial Small General Inten...
AI summary The document is a redacted 2022 Load Forecast Report by NS Power, containing attachments and appendices related to residential, commercial, and industrial demand modeling. Key sections include forecast classes, model inputs, and stakeholder engagement materials, though much content is confidential.
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 Nova Scotia Power Incorporated (NS Power) submits its 10-year Load Forecast to the Nova Scotia Utility and Review Board (NSUARB), outlining energy and peak demand requirements for 2022–2032. The forecast incorporates historical sales data, weather, economic indicators, and technological changes, while acknowledging uncertainties from factors like energy efficiency program effectiveness and electrification trends. NS Power uses Statistically Adjusted End-Use (SAE) models for residential and commercial forecasts.
1 Figure 3: Historic and Forecast Net System Requirement and System Peak Year NSR (GWh) Growth (%) System Peak (MW) Growth (%) 2012 10,475 -12.0 1,882 -13.2 2013 11,194 6.9 2,033 8.0 2014 11,037 -1.4 2,118 4.2 2015 11,099 0.6 2,015 -4.9 20...
AI summary The table presents historical and projected Net System Requirement (NSR) and System Peak data from 2012 to 2032, showing fluctuating growth rates in energy demand and peak load, which informs infrastructure planning and system reliability considerations.
of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with confidential information removed. It likely contains analysis related to electricity demand forecasting for Nova Scotia, though specific details are not disclosed.
1 2.0 INTRODUCTION 2 3 NS Power annually develops a forecast of energy sales and peak demand requirements to 4 assess the effects of end-use and economic factors on the future power system load and 5 load shape. The forecast is a foundatio...
AI summary NS Power produces annual energy forecasts for 2022-2032, reviewed by NSUARB in a 2021 paper hearing with intervenors including the Consumer Advocate, EfficiencyOne, and Synapse Energy Economics. The Board acknowledged NS Power's commitment to forecast improvements but directed further refinements.
positive step toward 24 continuous improvement related to the Load Forecast. While NS Power committed 25 to incorporate numerous recommendations where appropriate, the Board directs NS 26 Power to undertake the agreed upon recommendations....
AI summary The Board directs NS Power to incorporate recommendations for improving the Load Forecast, including re-evaluating low design temperature assumptions and assessing peak-causing conditions. Intervenors recommended this change, which NS Power agreed to. The 2021 Load Forecast Report (M10109) and UARB Decision are referenced.
Page 11 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a 2022 Load Forecast Report with confidential information redacted. It appears to be part of a regulatory proceeding involving energy forecasting, though specific details are omitted due to confidentiality.
1 historic peak temperatures (such as a 1-in-2) in its future load forecasts, as well as 2 add trends in peak weather in the 2022 Load Forecast Report. 3 4 NS Power agreed with the CA’s recommendation on weather normalizing and 5 presentin...
AI summary The Board directs NS Power to improve weather normalization in load forecasts, incorporate forecast accuracy for system peaks, and use load-weighted temperature averages. NS Power agrees but highlights complexity concerns, while the SBA and CA recommend methodological enhancements for accuracy.
may increase the complexity of the model without improving accuracy. However, 24 given the ranges in weather event occurrences throughout the province, the Board 25 directs NS Power to incorporate more weather station data into future load...
AI summary The Board directs NS Power to enhance load forecasts by incorporating more weather station data using a load-weighted approach. NS Power acknowledges intervenors' requests but cites preliminary results from Smart Grid and Water Heating Demand Response projects. The Board mandates reporting these project impacts on load in the 2022 Load Forecast and encourages inclusion of literature sources in future forecasts.
Page 12 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The text is a redacted page from the 2022 Load Forecast Report, indicating confidential information has been removed. The document's purpose and content remain unclear due to redactions.
1 - Evaluate the input variables in the residential model and test over a period of 2 time if alternative inputs make the residential model more robust, considering 3 the following: 4 • Given the recent population growth and housing shorta...
AI summary The Board requests NS Power to evaluate and enhance the residential load forecasting model by re-evaluating input variables like housing completions, retail sales, and income indicators, while considering population growth and supply-chain issues. The Board acknowledges intervenor input and urges NS Power to engage stakeholders early in the 2022 Load Forecast process.
were evaluated and what was incorporated into the 23 forecast early in the process. 24 25 In accordance with the Board’s direction, NS Power revised and enhanced the 2022 Load 26 Forecast in the following manner: 27 28 • The peak design te...
AI summary NS Power revised the 2022 Load Forecast per the Board’s direction, incorporating historic temperature data, warming trends, population-weighted weather analysis, and summaries of Smart Grid/Demand Response projects. System peak accuracy was added to Appendix C, with references to Sections 4.2, 10, and other documentation.
Page 13 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 • A discussion of the economic inputs to the residential model is provided in Section 2 4.3. 3 4 Summary of Stakeholder Consultations 5 6 On Apr...
AI summary NS Power conducted a stakeholder session on April 13, 2022, discussing updates to the 2022 Load Forecast, including impacts of COVID-19, EV forecasts, space heating, peak savings assumptions, and methodology from Energy and Environmental Economics, Inc. Stakeholders included NSUARB, Synapse, EfficiencyOne, and others.
of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted 2022 Load Forecast Report, with confidential information removed. It likely contains analysis related to electricity demand forecasting for Nova Scotia, though specific details are not disclosed.
1 3.0 FORECASTING APPROACH 2 3 NS Power continues to use a set of SAE models for the Residential3 and Commercial4 rate 4 classes, an econometric model for the Small and Medium Industrial classes, and customer 5 surveys and historical data...
AI summary NS Power uses SAE models for residential and commercial classes, econometric models for small/medium industrial classes, and customer surveys/historical data for large industrial classes. The SAE model combines econometric and end-use methodologies, incorporating factors like efficiency trends, population changes, and weather into forecasts.
ural changes are captured in 22 the residential forecast model through the SAE model specifications. Figure 4 shows the 23 general forecast approach used in the SAE models. 24 3 References to the Residential class include Domestic Service...
AI summary The residential forecast model incorporates SAE specifications, with Figure 4 illustrating the general forecast approach used in SAE models. The document includes class definitions for Residential, Commercial, and Industrial categories.
98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document is a redacted section of the 2022 Load Forecast Report, part of a Nova Scotia regulatory proceeding. Key content is confidential and removed, leaving only headers and page numbers visible.
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” sales rather than “accrued” 6 sales. Billed sales refer to the amount of energy billed to customer...
AI summary NS Power uses billed sales data for load forecasting, noting differences from accrued sales. Residential, commercial, and industrial forecasts use historical data from 2012–2021. The Renewable to Retail (RtR) market, established in 2016, allows licensed retailers to sell renewable energy, with a third-party application for Licensed Retail Supplier status pending NSUARB approval.
e 18 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 of energy sales through wind production, with 10 percent serving residential customers, 50 2 percent serving commercial customers, and 30 percent s...
AI summary The 2022 Load Forecast Report adjusts energy sales forecasts by subtracting wind production contributions (10% residential, 50% commercial, 30% industrial). Peak demand remains unchanged due to NS Power's obligation to serve full customer peaks. Weather impacts are analyzed using Heating Degree Days (HDD) and Cooling Degree Days (CDD) based on 10-year temperature data from Shearwater Airport, showing a warming trend.
ecast based on temperatures from one weather station 5 (Shearwater RCS) versus temperatures from a weighted average from the weighted weather 6 stations the metric used was the Mean Absolute Percentage Error (MAPE): 𝑛 100% 𝐴𝑡 − 𝐹𝑡 7 𝑀𝐴𝑃𝐸 =...
AI summary The document discusses the evaluation of load forecasts using the Mean Absolute Percentage Error (MAPE) metric, comparing actual values (At) with forecasted values (Ft) for the 2021 monthly series. The analysis is informed by 10 years of actual data and includes comparisons between weather-dependent customer classes and system peak forecasts.
5 Figure 14 below show forecast MAPE comparisons between model weather dependent 6 customer classes and accrued system peak. 7 8 Figure 14: Forecast Results 9 Class MAPE (1 station) MAPE (multiple stations) Difference Residential 2.58% 2.5...
AI summary The text compares forecast MAPE results between different customer classes and system peak, noting minimal differences that did not impact the 2022 Load Forecast. Economic data from the Conference Board of Canada is used, and the residential model was updated to use household compensation instead of retail sales and disposable income.
.2 2032 40,865 1.0 452 0.1 12‐21 1.4 0.3 22‐32 1.1 0.3 3 4 DATE: April 29, 2022 Page 33 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 19: Industrial Economic Drivers 2
AI summary The 2022 Load Forecast Report includes a section on industrial economic drivers, with data presented in a table format. The report is redacted and contains confidential information.
ase at an average of 2 percent per year, 17 or around the rate of inflation, which results in a flat profile in real terms. Figure 35 shows 18 price forecasts by class. 19 19 M09288, NS Power 2020-2022 Fuel Stability Plan, NSUARB Decision,...
AI summary The document discusses electricity price forecasts and their impact on sales, referencing historical and projected real electricity prices. It notes that prices are increasing at an average of 2% annually, similar to inflation, and mentions the use of price elasticity estimates in SAE models to predict sales changes.
n of the different components for 2019, 2020 14 and 2021 actuals vs forecast and weather normalized totals, and the 2022 forecast. 15 16 Figure 37: Comparison of Forecast to Actuals 17 Year 2019 2020 2021 2022 Forecast Sales 4551 4540 4718...
AI summary The text compares forecasted and actual electricity sales for 2019, 2020, 2021, and 2022, highlighting the impact of weather and non-weather factors, particularly the influence of the COVID-19 pandemic on residential electricity usage and forecasting assumptions.
Page 94 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The text refers to a redacted 2022 Load Forecast Report, which is part of a regulatory proceeding. The report likely contains confidential information related to electricity demand forecasting for the year 2022.
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 the uncertainty in sales and peak load forecasts, influenced by factors like economics, weather, and DSM. A P10/P90 probability analysis using Monte Carlo simulations was developed in 2017 to estimate future load distribution, with sensitivity bands shown in Figure 66, highlighting a range of 360-630 GWh over 10 years due to weather and economic variations.
present actual system totals. 25 26 DATE: April 29, 2022 Page 95 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 66: System Energy Sensitivity 2 3 4 Similarly, a P10/P90 scenario was created fo...
AI summary The text discusses the creation of a P10/P90 scenario for peak demand using random sampling of weather and economic drivers, highlighting that peak variance is mainly driven by weather variation, with DSM scenarios falling mostly within the bands.
2.1% 3,091 4.0% 2,542 2.5% 70 -2.4% 726 11,144 2.2% 2023 4,682 -0.7% 3,135 1.4% 2,548 0.2% 70 0.0% 727 11,162 0.2% 2024 4,711 0.6% 3,143 0.3% 2,583 1.4% 70 0.1% 732 11,240 0.7% 2025 4,713 0.0% 3,142 0.0% 2,608 0.9% 70 -0.3% 732 11,265 0.2%...
AI summary The document provides a table of load forecast data for various years, showing percentages and numerical values related to demand forecasting. The data appears to be part of a 2022 Load Forecast Report Appendix A, specifically Table A2, which outlines coincident peak demand forecasts for NS Power.
March 2 weekday 2021 94 - 1,875 1,968 -4.0 -10 evening 2022 144 - 2,021 2,165 10.0 -13.7 Forecast 2023 146 -4 2,035 2,185 0.9 -13.7 Forecast 2024 146 -12 2,057 2,215 1.4 -13.7 Forecast 2025 152 -24 2,076 2,253 1.7 -13.7 Forecast 2026 154 -...
AI summary The document presents a load forecast report with data spanning from 2021 to 2032, detailing metrics such as load, capacity, and various percentages. The data includes forecasted values and percentages for different years, with some entries marked as 'Forecast'.
to explain the increase in residential load from people working from home during the pandemic and stays in the forecast at a reduced level to recognize permanent changes related to working patterns. Binary shift variables are added to the...
AI summary The text explains how residential load increased during the pandemic due to remote work and how the model accounts for this by incorporating binary shift variables and a moving average to address residual autocorrelation in the forecasting model.
he dependent variable (in this case, sales). To help eliminate this autocorrelation, a moving average, MA, of period 1, MA(1) was added, which estimates the autocorrelation with its the predecessor. Variable Coefficient StdErr T-Stat P-Val...
AI summary The text discusses the use of a moving average (MA(1)) to address autocorrelation in a statistical model where the dependent variable is sales. The model includes various coefficients and statistical values for different variables, such as heating, cooling, and energy efficiency savings, as well as seasonal and event-specific factors.
2022 Load Forecast Report Appendix B Page 4 of 32 Appendix B – Forecast Model Details Residential Model Statistics Model Statistics Iterations 21 Adjusted Observations 120 Deg. of Freedom for Error 106 R-Squared 0.990 Adjusted R-Squared 0....
AI summary This section provides statistical details for a residential load forecasting model, including metrics such as R-squared, adjusted R-squared, AIC, BIC, and other statistical indicators. It outlines model performance and assumptions used in the 2022-2032 residential load forecast reconciliation.
382 Change 2.4% 62.8% 1.2% 10.7% 0.0% 71.8% to load XCool = (Central AC + HP Cool + Room AC) x CoolUseVariable x Coeff Page 6 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix B Page 8 of 32 Appendix B –...
AI summary The text provides details on residential and commercial load forecast models, including variables such as XCool and XOther, which are calculated using specific intensities and coefficients. The data shows changes in load intensity and usage variables over time, from 2022 to 2032.
Forecast Report Appendix B Page 16 of 32 Appendix B – Forecast Model Details General Service Model Statistics Model Statistics Iterations 20 Adjusted Observations 120 Deg. of Freedom for Error 113 R-Squared 0.877 Adjusted R-Squared 0.871 A...
AI summary This section provides statistical details of the General Service Model used in the 2022 Load Forecast Report. It includes model statistics such as R-squared, adjusted R-squared, AIC, BIC, and other diagnostic measures to evaluate the model's performance and accuracy in forecasting demand from 2022 to 2032.
2022 Load Forecast Report Appendix B Page 31 of 32 Appendix B – Forecast Model Details Peak Model Statistics Model Statistics Iterations 1 Adjusted Observations 120 Deg. of Freedom for Error 105 R-Squared 0.974 Adjusted R-Squared 0.971 AIC...
AI summary This section provides statistical details of the peak load forecasting model, including metrics such as R-squared, AIC, BIC, and error statistics. The model has high explanatory power with an R-squared of 0.974, and the forecast error is relatively low at 2.84%.
2022 Load Forecast Report Appendix B Page 32 of 32 Appendix B – Forecast Model Details Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. Although it was...
AI summary This document discusses the 2022 Load Forecast Report, focusing on the peak model fit and forecast comparisons. It highlights the relationship between demand-side management (DSM) and peak demand forecasting, as well as the accuracy of the forecast models used for total energy requirements and system peak demand.
REDACTED 2022 Load Forecast Report Appendix C Page 3 of 9 Appendix C – Forecast Comparison and Accuracy Figure C3: Firm Peak Demand Figure C4 below provides an overview of the energy forecast accuracy. For these calculations, the load of t...
AI summary This section of the 2022 Load Forecast Report compares forecast accuracy over different time horizons. It notes that energy forecast accuracy is less than 2% for a 5-year lead time, but diminishes beyond that. Firm peak load forecasts show higher errors, averaging under 4% for the first 7 years. System peak accuracy averages just over 4% in the first 7-year period.
22 Load Forecast Report Appendix C Page 4 of 9 Appendix C – Forecast Comparison and Accuracy
AI summary This section of the Load Forecast Report provides a comparison of forecasts and an analysis of their accuracy, which is essential for understanding the reliability and effectiveness of the forecasting methodology used in the proceeding.
3.3% 2.4% 2020 1.2% Statistics Lead Time (Years): 1 2 3 4 5 6 7 8 9 10 Page 3 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2022 Load Forecast Report Appendix C Page 5 of 9 Appendix C – Forecast Comparison and Accuracy NSR less...
AI summary The document provides statistical data on forecast accuracy over different lead times, showing average percent error and MAPE for various forecast periods. The data indicates varying levels of accuracy, with higher errors observed as lead time increases.
-3.1% -4.5% -4.4% -10.9% Error: MAPE: 2.1% 1.7% 0.9% 1.9% 2.1% 3.1% 3.8% 5.0% 4.4% 10.9% Page 4 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2022 Load Forecast Report Appendix C Page 6 of 9 Appendix C – Forecast Comparison and...
AI summary The document presents forecast accuracy metrics, including MAPE values and percentage errors, from the 2022 Load Forecast Report. These metrics are used to evaluate the accuracy of load forecasts over different periods.
D 2022 Load Forecast Report Appendix C Page 6 of 9 Appendix C – Forecast Comparison and Accuracy
AI summary This section of the 2022 Load Forecast Report provides a comparison and accuracy assessment of load forecasts, focusing on the data presented in Appendix C.
Figure C5: Firm Peak Forecast Accuracy Firm Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for: for: for: for: for: Issued 2012 2013 2014 2015 2016 2017 2018...
AI summary Figure C5 presents the firm peak forecast accuracy from 2011 to 2021, showing discrepancies between forecasted and actual firm peak values. The data indicates a growing gap between forecasts and actual outcomes, especially from 2016 onwards.
11.2% Statistics Lead Time (Years): 1 2 3 4 5 6 7 8 9 10 Page 5 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2022 Load Forecast Report Appendix C Page 7 of 9 Appendix C – Forecast Comparison and Accuracy Firm Peak Forecast For...
AI summary The document presents statistical data on load forecast accuracy over a 10-year period, with average percent errors and MAPE values indicating decreasing accuracy as the forecast horizon increases.
2022 Load Forecast Report Appendix C Page 8 of 9 Appendix C – Forecast Comparison and Accuracy
AI summary This section of the 2022 Load Forecast Report provides a comparison and accuracy analysis of the load forecast, likely evaluating the precision of predictions against actual data.
Figure C6: System Peak Forecast Accuracy System Peak Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for: for: for: for: for: Issued 2012 2013 2014 2015 2016 2017...
AI summary Figure C6 presents a table comparing forecasted system peak values from 2011 to 2021 with actual values in 2012 and 2021. The data shows discrepancies between forecasts and actual outcomes, indicating potential challenges in forecasting accuracy over time.
13.6% Statistics Lead Time (Years): 1 2 3 4 5 6 7 8 9 10 Page 7 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2022 Load Forecast Report Appendix C Page 9 of 9 Appendix C – Forecast Comparison and Accuracy System Peak Forecast F...
AI summary This section presents a forecast sensitivity analysis from the 2022 Load Forecast Report, discussing accuracy metrics and error percentages across different lead times, highlighting varying levels of forecast precision over time.
REDACTED 2022 Load Forecast Report Appendix D Page 2 of 9 Appendix D – Forecast Sensitivity Analysis Sensitivity Analysis The P10/P90 sensitivity analysis used Monte Carlo simulation for the economic and weather variables. The algorithm us...
AI summary The text describes a sensitivity analysis conducted using Monte Carlo simulation for the 2022 Load Forecast Report. The analysis uses historical weather and economic data to model the impact of variations on load forecasts, with the regression coefficients held constant during the simulation.
D 2022 Load Forecast Report Appendix D Page 3 of 9 Appendix D – Forecast Sensitivity Analysis Figure D1: Distribution of January HDD 5. Oracle’s Crystal Ball runs about 10,000 trials, taking a random set of numbers from the relevant variab...
AI summary The document discusses the use of Oracle’s Crystal Ball for running 10,000 trials in a Monte Carlo simulation to forecast load, incorporating variability in HDD and other variables. It notes challenges in incorporating historical end use variability due to revised data sets and highlights the impact of heat pumps in simulations. Normal distributions are used to represent averages and standard deviations of forecast outputs.
Figure D2 for energy and Figure D3 for peak (both before the impact of DSM). 10th (10%) and 90th (90%) percentiles can easily be obtained from Normal distributions and so they are highlighted in D2. Figure D2: Distribution of Energy (Befor...
AI summary The text discusses probabilistic load forecasting, focusing on energy and peak demand distributions before the impact of demand-side management (DSM). It references figures showing percentile ranges and sensitivity analysis for forecast accuracy.
022 Load Forecast Report Appendix D Page 7 of 9 Appendix D – Forecast Sensitivity Analysis Figure D5: Relative Sensitivity of Peak In terms of the sensitivity of the energy sales forecast to the various input variables, Figures D6 and D7 s...
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 dominant in the long term. Demand-side management (DSM) and other factors significantly outweigh the impact of economic, weather, or end-use changes.
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Reference Report p. 7: “As with any forecast, there is a degree of uncertainty arou...
AI summary The Consumer Advocate has requested clarification on the demand forecast report, specifically regarding uncertainty analysis and the inclusion of a general warming trend. NS Power confirms that only weather and economic uncertainties were considered in their scenario analysis and explains that the warming trend was not included in the analysis or figure provided.
6 Response IR-1: 27 28 (a) Confirmed. Weather (normal) and economics are the only uncertainties considered in 29 Appendix D scenario analysis using Monte Carlo simulations. 30 Date Filed: July 8, 2022 NSPI (CA) IR-1 Page 1 of 2 REDACTED (C...
AI summary NSPI confirms that weather and economic factors are the primary uncertainties in its 10-year energy forecast, using Monte Carlo simulations. Historical data variability is deemed insufficient for scenario analysis, and warming trends are integrated into the 'Model' variable rather than applied post-hoc.
and Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Reference Report p. 10: “Produced in the winter of 2021-2022 and using information 4 ava...
AI summary NSPI clarifies the 2022 load forecast was finalized in April 2022, using economic data from the Conference Board of Canada (Feb 2022) and load/solar data up to 2021. The response directs to NSUARB IR-3 Attachment 1 for additional economic data.
56.5 36.2 27.6 37.9 21.5 2032 73.2 55.0 35.7 26.9 37.5 20.9 3 Date Filed: July 8, 2022 NSPI (CA) IR-5 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569)...
AI summary NSPI confirms the 'DSM captured by end uses' column calculation and explains that their load forecast model relies on aggregated historical DSM data, not detailed appliance or building-level data. The model incorporates past DSM effects on variables like price and appliance efficiency but lacks granular historical records.
appliance level or building shell level DSM data, only annually reported savings at the 25 class level. The treatment of DSM in the forecast is outlined in section 4.6 of the Report. Date Filed: July 8, 2022 NSPI (CA) IR-6 Page 1 of 1 REDA...
AI summary The document discusses the annual reporting of DSM savings at the class level, not appliance or building shell levels, and references section 4.6 of the Report. It also mentions NSPI's responses to the Consumer Advocate's information requests regarding the 10-Year Energy and Demand Forecast (NSUARB M10569).
mVarsNew.Cool_Var 0.830 0.399 2.078 4.02% mVarsNew.Heat_Var 1.375 0.104 13.276 0.00% Date Filed: July 8, 2022 NSPI (CA) IR-7 Page 1 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast R...
AI summary The document references a 10-Year Energy and Demand Forecast from NSPI's 2022 Load Forecast Report (NSUARB M10569), alongside NSPI's responses to Consumer Advocate information requests. It includes technical variables and a NON-CONFIDENTIAL designation.
0.973 AIC 7.870301 BIC 8.241966 F-Statistic #NA Prob (F-Statistic) #NA Log-Likelihood -626.49066 Model Sum of Squares 9,931,770.617 Sum of Squared Errors 240,656.468 Mean Squared Error 2,314.00450 Std. Error of Regression 48.10410 Mean Abs...
AI summary The document includes statistical data from a model analysis and references NSPI's responses to consumer advocate information requests, specifically the 10-Year Energy and Demand Forecast (2022 Load Forecast Report) under NSUARB matter M10569. The content pertains to forecasting methodologies and energy usage patterns.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL Model Statistics Mean Abs. % Err. (MAPE) 2.69% Durbin-Watson Statistic 1.831 Durbin-H Statistic #NA Lj...
AI summary The 2022 Load Forecast Report (NSUARB M10569) discusses NSPI's 10-year energy and demand forecast, highlighting model statistics (MAPE 2.69%) and consistent DSM savings (27 MW annually). The analysis confirms alignment between energy and peak DSM assumptions in the model, ensuring accuracy in forecasting.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Reference Report p. 59: “The COVID-19 impact to the Residential forecast is approxima...
AI summary NSPI clarifies that the 'impact' of COVID-19 on the 2022 residential demand forecast refers to the load variable in the model, not an analysis. No additional analysis was conducted, and none is planned. The response relates to the 2022 Load Forecast Report (NSUARB M10569).
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-10: 2 3 Reference Report p. 71: “Sales in this class have been flat for the last 10 years an...
AI summary NSPI responds to a consumer advocate request regarding the 2022 Load Forecast Report, citing the Conference Board of Canada's economic forecasts for small industrial class sales growth. The response outlines reliance on manufacturing GDP data and references Figure 19 of the report for updated forecasts.
the data series. The series is based on the 29 Conference Board of Canada’s February 5-year forecast plus their 20-year forecast produced 30 in January for the years beyond 2026. Date Filed: July 8, 2022 NSPI (CA) IR-10 Page 1 of 2 REDACTE...
AI summary NSPI provides a 10-year energy and demand forecast based on Conference Board of Canada economic projections, noting historical alignment between manufacturing GDP and sales trends. NSPI asserts no material changes are anticipated in the forecast period.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests REDACTED 1 Request IR-11: 2 3 With reference to the Report pp. 73-74: “One customer makes up over [redacted] of sales 4...
AI summary NSPI responds to Consumer Advocate information requests regarding the 2022 Load Forecast Report, explaining that mid-year data is not included in annual reports but provides requested sales and demand data in attachments. The responses address specific queries about customer sales trends and peak demand metrics.
NSPI (CA) IR-12 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-12 Attachment 1 Page 1 of 1 Jan Feb Mar Apr May Net System Requirement (GWh) 1,242 1,096 1,091 913 836 System Peak (MW) 2,216 2,112 2,024 1,720 1,422 RE...
AI summary The document presents a 10-year energy and demand forecast from NSPI's 2022 Load Forecast Report (NSUARB M10569), including monthly net system requirements and system peak data. It references NSPI's responses to Consumer Advocate information requests, highlighting energy usage patterns and forecasting methodologies.
1 Request IR-13: 2 3 Reference NS Power’s 2021 Rebuttal Evidence (M10109), p. 10, where NS Power 4 acknowledges the CA’s comment that “Data on weather circumstances other than 5 temperature is also available,” but does not appear to respon...
AI summary The request challenges NS Power’s reliance on temperature as the sole weather factor in load forecasting, seeking evidence for this claim and data on other variables like wind speed, precipitation, and cloud cover. It also asks whether these variables are subjective or objective and their impact on forecasts.
temperature (and 4 likely lagged temperature) is the predominant driver, so the impact of other factors may 5 not be statistically significant when combined with temperature. Date Filed: July 8, 2022 NSPI (CA) IR-13 Page 3 of 3 REDACTED (C...
AI summary The analysis highlights temperature (and likely lagged temperature) as the primary driver of energy demand, suggesting other factors may not be statistically significant when combined with temperature. The text references NSPI's 10-Year Energy and Demand Forecast (2022 Load Forecast Report) and responses to consumer advocate information requests.
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Reference Report p. 86 Figure 60 “Weather-Normalized Firm Peak.” For items a-d bel...
AI summary The document outlines NSPI's responses to the Consumer Advocate's information requests regarding the 2022 Load Forecast Report, focusing on weather-normalized sales, peak load calculations, methodology documentation, and data for Figure 60. The request includes detailed workpapers and explanations for adjustments in load forecasting.
used to construct Figure 60. 27 Date Filed: July 8, 2022 NSPI (CA) IR-14 Page 1 of 4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Ad...
AI summary NSPI is responding to the Consumer Advocate's information requests regarding its 10-year energy and demand forecast, part of the NSUARB M10569 proceeding. The forecast is based on the 2022 Load Forecast Report.
t (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL
AI summary NSPI provided responses to the Consumer Advocate's information requests related to the 2022 Load Forecast Report as part of the NSUARB proceeding (M10569). The document is marked non-confidential and pertains to regulatory oversight of load forecasting methodologies.
1 Response IR-14: 2 3 (a) Weather normalized (WN) sales and net system requirement are provided in the figure 4 below: 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WN Sales 9,635 10,362 10,352 10,318 10,023 10,136 10,419 10,262 10,146...
AI summary The response details weather-normalized sales and net system requirements from 2012 to 2021, explaining that weather-adjusted actuals are not used in forecasts but for variance analysis. Forecast models assume normal weather, using regression with HDD/CDD values and day-of-week binaries to estimate baseload and temperature-dependent load.
values by month. Binaries are added for day of week. The baseload is estimated by a 14 constant, with the other variables accounting for the temperature dependent load. 15 Date Filed: July 8, 2022 NSPI (CA) IR-14 Page 2 of 4 REDACTED (CONF...
AI summary NSPI submitted a 10-Year Energy and Demand Forecast (2022 Load Forecast Report) as part of NSUARB M10569, responding to Consumer Advocate information requests. The forecast models load using a constant baseload and temperature-dependent variables, with data analyzed by month and day-of-week binaries.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 DailyEnergy = Constant + b1×HDD13 + b2×HDD0 + b3×Lag1HDD13 + 2 b4×Lag2HDD13 + b5×JanHDD13 + b6×FebH...
AI summary NSPI explains its load forecast model, including the DailyEnergy formula incorporating temperature variables and weather normalization factors. The model allocates weather impact to residential (77%), commercial (15%), and municipal (3%) sectors. The normalization factor increased from 20 MW/°C (pre-2016) to 25 MW/°C post-2016 analysis, with a revised figure provided for 2019.
degree since 2016. The adjustment for the morning 20 peak in 2019 in Figure 60 was inadvertently omitted from the Report and a copy of the 21 revised figure is provided below. 22 Date Filed: July 8, 2022 NSPI (CA) IR-14 Page 3 of 4 REDACTE...
AI summary The document discusses adjustments to energy demand forecasts, including a 2019 morning peak correction and 2021 weather-adjusted sales data. NSPI explains the 2019 adjustment as reflecting differences between morning and evening peak loads, not solely lighting. 2021 data shows actual and weather-adjusted sales across residential, commercial, industrial, and other sectors.
/17/2020 2020 2 1 19 3.2 1383.92 2/18/2020 2020 2 2 19 -4 1574.533 2/19/2020 2020 2 3 19 2.2 1444.416 2/20/2020 2020 2 4 19 -8.9 1715.486 2/21/2020 2020 2 5 19 -5.7 1601.147 2/24/2020 2020 2 1 19 0.6 1304.951 2/25/2020 2020 2 2 19 4 1339.8...
AI summary The text contains a table with dates and numerical data, followed by a reference to a redacted document and a 10-Year Energy and Demand Forecast from 2020, associated with a regulatory proceeding (NSUARB M09707). It also mentions NSPI's responses to information requests.
and Demand Forecast (2020 Load Forecast Report) (NSUARB M09707) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 On page 9 of 137, NS Power reports the 2020 growth in system peak at 8.9% and the prior 4 y...
AI summary The document contains a request from NSUARB to NS Power regarding discrepancies in load forecasts and actual system peak growth, particularly focusing on the 2019 forecast and actual figures, and requesting detailed explanations and supporting data related to lighting load and temperature impacts.
orecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests CONFIDENTIAL (Attachment Only)
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and includes NSPI's responses to information requests from the Consumer Advocate. The content is marked as confidential and is an attachment.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-16: 2 3 Reference Report p. 77. We understand NS Power’s argument to be as follows: Using the...
AI summary NSPI responds to a request regarding the use of a 25 MW/°C demand change estimate in the 2022 Load Forecast Report. NSPI confirms that the estimate is not used in the forecast model and is only used for explaining variance and comparing to forecast values. The response also confirms that the estimate is based on a regression excluding weekend data.
not inputs to the forecast model. Date Filed: July 8, 2022 NSPI (CA) IR-16 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer...
AI summary The document discusses the 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, focusing on weekend peak demand and temperature sensitivity. The forecast model simplified adjustments for all days due to the rarity of weekend peaks and small differences between weekday and weekend demand.
and presenting a more accurate approach to estimating the impact of 31 incremental cold on loads. Therefore, the Board directs NS Power to 32 evaluate improvements to the weather normalization estimate and 33 examine the impact of incremen...
AI summary The Board directs NS Power to improve the weather normalization estimate and examine the impact of incremental cold on loads in temperate ranges where peak loads occur. These updates are to be included in future load forecast reports, preferably in the 2022 Report or the 2023 Report.
fication rebates and 27 funding programs to offset capital investment. 28 29 (iii) Smart Grid Nova Scotia (M10176): A four-year pilot program focused on studying 30 the effects posed by distributed energy resource (DER) penetration on the...
AI summary The text references the Smart Grid Nova Scotia pilot program (M10176), which studies the impact of distributed energy resources (DER) on the electrical grid, and NSPI's 10-Year Energy and Demand Forecast (NSUARB M10569). These initiatives address grid modernization and energy forecasting, supporting DER integration and long-term planning.
y and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and NSPI's responses to EfficiencyOne's information requests. It pertains to demand forecasting and regulatory disclosure processes under Nova Scotia's utility oversight.
ill NS 24 Power follow to determine: 25 (i) the level of ratepayer intervention required; and 26 (ii) whether such intervention is beneficial to ratepayers? 27 Date Filed: July 8, 2022 NSPI (E1) IR-4 Page 1 of 2 10 - Year Energy and Demand...
AI summary NSPI responds to an information request regarding its 10-Year Energy and Demand Forecast, stating it has not conducted a market-wide analysis aligned with the Environmental Goals and Climate Change Reduction Act (EGCCRA). It explains modeling trajectories consider equipment lifetimes and resource constraints, intends to support electrification, and notes the process for considering measures is undetermined.
mand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and NSPI's responses to EfficiencyOne's information requests. It highlights regulatory proceedings involving load forecasting methodologies and data disclosure processes.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Reference: NS Power 2022 Load Forecast, page 37, lines 4-6 and page 38, Figure 22. 4 5 “E...
AI summary NSPI's 2022 Load Forecast shows a 52% commercial electric heating share estimate versus E3's 27%, prompting EfficiencyOne to request modeling assumptions, current share estimates by heat pump type, and reasons for model calibration discrepancies. NSPI refers to NSUARB IR-9 for forecast differences, notes data limitations, and states trajectories are calibrated for similar increases.
l General Intensities and 2022 LFR 28 Attachment 03 General Intensities, Shares tab. 29 (c) The trajectories have been calibrated to show similar increases over the forecast period. Date Filed: July 8, 2022 NSPI (E1) IR-8 Page 1 of 1 10 -...
AI summary The document references a 10-Year Energy and Demand Forecast from NSPI's 2022 Load Forecast Report (NSUARB M10569), including responses to EfficiencyOne's information requests. It discusses general intensities and load forecast trajectories calibrated for similar increases over the forecast period.
of the forecast it was assumed that E1’s proposed demand response measures 16 are incorporated into the larger demand response program that is being developed as part of the 17 IRP Action Plan. Date Filed: July 8, 2022 NSPI (E1) IR-11 Page...
AI summary NSPI incorporates E1's proposed demand response measures into the larger demand response program under the IRP Action Plan, as part of the 10-Year Energy and Demand Forecast (NSUARB M10569). This relates to NSPI's responses to EfficiencyOne's information requests.
10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 With reference to the 2020 Load Forecast Decision Letter (M09707), dated Oc...
AI summary NS Power's 2022 Load Forecast Report addresses the ongoing impact of the COVID-19 pandemic on electricity demand, adjusting the COVID-19 variable to reflect reduced work-from-home effects. The Board directed consideration of near-term and long-term pandemic impacts on electricity sales, with NS Power assuming continued hybrid work models will influence load patterns.
2021 2022 2023-2031 Forecast COVID-19 1 0.75 0.38 0.38 variable 2022 Load September July 2020 October 2020 March 2022 Forecast 2022 COVID-19 1 0.5 0.33 0.16 variable 17 18 Response IR-1: 19 20 Confirmed, the table is correct. The COVID-19...
AI summary The response confirms the inclusion of a 'COVID-19 variable' in the 2022 Load Forecast model to explain unaccounted sales increases in 2020-2021. The variable may remain in future models if historical sales are inadequately explained by other factors. The table's accuracy is affirmed.
nergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary NSPI provided responses to NSUARB information requests regarding the 2022 Load Forecast Report, focusing on energy and demand forecasting. The document is marked non-confidential and relates to regulatory proceedings involving NSPI and NSUARB.
(NHTS) for population travel behavior for personal 25 LDVs. E3 believes the New England profile remains representative of Nova 26 Scotian driving patterns, as through its experience with other jurisdictions it 27 notes that the timing of t...
AI summary E3 uses the New England profile for light-duty vehicles (LDVs) and NREL’s Fleet DNA data for medium-duty vehicles (MDVs), parcel trucks, and buses, citing data from the National Household Travel Survey (NHTS) and NREL. The text references NSPI's 10-Year Energy and Demand Forecast (NSUARB M10569) as part of its response to NSUARB information requests.
nergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary NSPI provided responses to NSUARB information requests regarding the 2022 Load Forecast Report, focusing on energy and demand forecasting. The document is marked non-confidential and relates to regulatory proceedings involving NSPI and NSUARB.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary NSPI provided non-confidential responses to NSUARB information requests regarding the 2022 Load Forecast Report (M10569), focusing on demand forecasting methodologies and data under regulatory review.
ment, assumptions 26 on average vehicle characteristics (fuel consumption, powertrain size, 27 battery size, etc.) are used to develop a representative model of vehicles 28 within the segment. Additional assumptions on utilization (e.g. di...
AI summary The analysis uses average vehicle characteristics and utilization assumptions to model vehicle costs and total cost of ownership (TCO) across powertrain segments. Nova Scotia-specific inputs were sourced from the 2021 and 2022 Load Forecast Reports, including Synapse IR-9 Attachment 1 and NSUARB M10569.
ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary NSPI provided responses to NSUARB information requests regarding the 2022 Load Forecast Report, focusing on energy demand projections and regulatory compliance under the Nova Scotia Utility and Review Board proceeding (M10569).
based on data from other jurisdictions with different levels of supply 25 constraints. This is then used to forecast EV adoption in Nova Scotia 26 under current constraints as well as considering increased availability in 27 dealerships un...
AI summary The analysis forecasts EV adoption in Nova Scotia using jurisdictional data, supply constraints, and policy scenarios (e.g., purchase incentives, ZEV mandates). It incorporates national data, market uncertainties (electricity rates, battery costs), and Dunsky's professional judgment to model adoption under varying conditions.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-3: 2 3 With reference to Section 4.3 Economic Information, page 29 of 98. The application notes 4 t...
AI summary NSPI responds to NSUARB's IR-3 request regarding the 2022 Load Forecast Report, addressing data sources (Conference Board of Canada's 20-year forecast), adjustments to economic data, comparisons with Canadian banks' forecasts, and the definition of 'Household Compensation' in residential economic drivers.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary NSPI provided non-confidential responses to NSUARB information requests regarding the 2022 Load Forecast Report (M10569), focusing on demand forecasting methodologies and data under regulatory review.
1 Response IR-3: 2 3 (a) Please refer to Attachment 1. 4 5 (b) All data and calculations are included in Attachment 1. The economic data used in the 6 forecast is based on the 20-year forecast from the Conference Board of Canada (released...
AI summary The response outlines the use of updated Conference Board of Canada forecasts for economic data, including GDP, employment, and household compensation adjusted for inflation. It compares these forecasts with other banks' data, highlighting the methodology used for the 2022 Load Forecast.
TD 3.5% 2.4% 1.6% 5.4% 2.2% 0.8% Dec-21 BMO 3.2% 2.5% 1.8% 5.2% 1.4% 1.0% Dec-21 Scotiabank 3.6% 2.6% 2.1% 5.4% 2.9% 1.8% Jan-22 18 19 (d) (i) Household compensation can be found in StatCan table 36-10-0224-01 (formerly 20 CANSIM 384-0040)...
AI summary NSPI's response to NSUARB information requests discusses using Statistics Canada (StatCan) data on household compensation for energy forecasting. It explains that 'household income' primarily reflects employment income and excludes median income due to its absence in the Conference Board's forecast model.
CBoC 20 Year Forecast CBoC 5 Year Forecast (Feb) + 20 year forecast Calculated (Average (Average (Average (Average (Average (Average Aggregation) Aggregation) Real (Average (Average Aggregation) Aggregation) Real Aggregation) Aggregation)...
AI summary The text presents economic forecasts from the Conference Board of Canada (CBoC), including 20-year and 5-year projections for housing completions, Gross Domestic Product (GDP), and employment metrics. It aggregates data on housing completions, GDP at basic prices, and provincial employment statistics.
ct (GDP) at Basic Employment, Employment, Provincial Singles, Nova Multiples, Nova Compensation Industry, All Prices by Industry, Total, Nova Nova Scotia, Consumer Price Singles, Nova Multiples, Nova Compensation Industry, All Prices by In...
AI summary The text presents a table of economic data for Nova Scotia, including GDP, employment, compensation, and price indices across industries. It references data sources like the Conference Board of Canada (CBoC) and Statistics Canada (StatCan), focusing on metrics such as the Consumer Price Index (CPI) and industry-specific employment figures.
gy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-4: 2 3 With reference to Section 4.3 Economic Information, page 30 of 98, the application finds...
AI summary NSPI confirms that 'New Construction (number)' in Figure 17 refers to housing completions, not starts. While housing starts were analyzed, they are excluded from the model due to better alignment of completions with new customer additions, as shown in the 2012–2021 cumulative data comparison.
rgy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Date Filed: July 8, 2022 NSPI (NSUARB) IR-4 Page 2 of 2 10 - Year Energy and Demand Forecast (2022 Load Fo...
AI summary NSPI submitted a 10-year energy and demand forecast (2022 Load Forecast Report) in response to NSUARB information requests, marked as non-confidential. The document outlines NSPI's approach to energy and demand planning for regulatory review.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 With reference to Section 4.3 Economic Information, page 31 of 98, the application states th...
AI summary NSPI responds to NSUARB's queries about GDP and employment data sources in their load forecast model, citing the Conference Board of Canada and explaining that data was frozen in February 2022. Discrepancies with Statistics Canada's 2021 data are noted, but NSPI attributes this to frozen forecast inputs.
referenced 27 above, while 2021 differs. Stats Can does make periodic adjustments to historic data, but 28 the forecast inputs for economics were frozen as of February 2022. 29 Date Filed: July 8, 2022 NSPI (NSUARB) IR-5 Page 1 of 2 10 - Y...
AI summary NSPI uses 2013 as the base year for calibrating US EIA intensities to Nova Scotia due to limited provincial commercial energy data, relying instead on employment data from NRCan. Forecast inputs were frozen as of February 2022.
ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-6: 2 3 With reference to Section 4.4 End-Use Intensity Trends, page 35 of 98, the application...
AI summary NSUARB requested details on how EIA data was calibrated for the SAE model in the 2022 Load Forecast Report. NSPI responded by directing to specific attachments containing EIA data, calculations, and formulas used in the forecast, emphasizing year-over-year changes for efficiency and share estimates.
23 These findings are consistent with other recent decarbonization modeling work, such as Canada’s 24 Net Zero Future5, which finds that near-complete electrification of transportation and most 1 https://www.canada.ca/en/services/environme...
AI summary The text discusses decarbonization modeling, emphasizing near-complete electrification of transportation and buildings as a low-cost, commercially viable strategy. It references Canada’s Net Zero Future report and Nova Scotia’s climate goals, citing NSPI’s 10-Year Energy and Demand Forecast (NSUARB M10569) as part of the analysis.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 In matter M10109, Section 4.4 End-Use Intensity Trends, page 32 of 89, the application 4 indi...
AI summary NSPI's response to NSUARB's request explains the discrepancy between heat pump saturation forecasts (55% by 2031 vs. 66% by 2032) due to differing assumptions about 2050 net-zero carbon targets in the two load forecasts.
ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 With reference to Section 4.4 End-Use Intensity Trends, page 37 of 98, the application...
AI summary NSPI explains discrepancies between E3 and NS Power models in load forecasts, attributing differences to data approaches: E3 uses building-level heating demand data, while NS Power aligns with NRCan's Atlantic province-wide data. Near-term intensity trends are similar, but long-term impacts of switching to electric heating diverge.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and NSPI's responses to NSUARB information requests, indicating regulatory engagement around demand forecasting and data disclosure.
tia and was 8 changed from 0.4 in the 2021 forecast to 0.45 in the 2022 forecast. Both intensities increase 9 by 2032 due to the increased share compared to the 2021 forecast. Date Filed: July 8, 2022 NSPI (NSUARB) IR-11 Page 2 of 2 10 - Y...
AI summary The 2022 Load Forecast Report indicates a change in intensity from 0.4 in 2021 to 0.45 in 2022, with both intensities expected to increase by 2032 due to a higher share compared to the 2021 forecast.
d the previous forecast of 58,000 EVs in the 2021 Load Forecast. Please provide a table 6 with the data that was used in each model. 7 8 Response IR-16: 9 10 Please refer to Attachment 1. Date Filed: July 8, 2022 NSPI (NSUARB) IR-16 Page 1...
AI summary The document requests a table with data used in models for forecasting electric vehicle (EV) numbers, referencing a previous forecast of 58,000 EVs in the 2021 Load Forecast. The response directs the requester to Attachment 1 for the data.
Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL
AI summary This document outlines NSPI's responses to information requests from the NSUARB regarding the 2022 Load Forecast Report, focusing on energy and demand forecasts.
86 23 21 18 10 18 2030 31 86 23 21 18 10 18 2031 31 86 23 21 18 10 18 2032 31 86 23 21 18 10 18 11 Date Filed: July 8, 2022 NSPI (NSUARB) IR-19 Page 1 of 1 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NS...
AI summary The document discusses NSPI's response to an information request regarding the use of price elasticity in load forecasting. NSPI explains that it is not aware of publicly available Canadian price elasticities specific to long-term electricity prices and refers to a previous response and an attachment for further details.
and Demand Forecast (2021 Load Forecast Report) (NSUARB M10109) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-10: 2 3 In reference to Section 4.5, Price Data, on page 46 and page 47, the application states tha...
AI summary The NSPI responded to a request regarding the price elasticity used in SAE models, clarifying that it was not solely based on Canadian models. Studies from the 1980s to 2006 show a range of price elasticities for electricity consumption, with a mean of -0.162 in a 1997 U.S. study.
y and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-32: 2 3 With reference to Section 7.1 Small Industrial, page 71 of 98, the application indicates...
AI summary NSPI responds to NSUARB's request regarding the small industrial class sales forecast, explaining that economic growth, as measured by manufacturing GDP from the Conference Board of Canada, is expected to increase from 0.3% annually to 2% annually, leading to a 0.7% annual sales growth forecast. This is partly offset by demand-side management (DSM) impacts.
NSPI (NSUARB) IR-32 Page 1 of 1 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-33: 2 3 With reference to Section 7.2 Medium Indus...
AI summary NSPI responds to NSUARB's IR-33 request regarding the flat load in the medium industrial class since 2014 and projected 0.1% annual growth. The forecast is based on an econometric model using manufacturing employment data from the Conference Board of Canada, which shows a decline until 2021 and a projected increase afterward, partly offset by DSM impacts.
ar in the 2021 forecast, with 23 average annual growth of 0.4 percent over the forecast period. Partly offsetting growth 24 will be the impact of DSM over the forecast period. Date Filed: July 8, 2022 NSPI (NSUARB) IR-33 Page 1 of 1 10 - Y...
AI summary The document discusses NSPI's responses to NSUARB information requests regarding energy and demand forecasts. NSPI explains that actual data for peak demand components is not available, and clarifies that increases in load intensities are due to higher adoption rates of appliances and increased use of electronic devices.
1 -700.803 0 2024 4 37,642.04 1 0 1 2024 4 27,241.27 27,942.07 -700.803 0 2024 5 37,664.75 1 0 1 2024 5 27,258.13 27,958.93 -700.803 0 2024 6 37,687.47 1 0 1 2024 6 27,274.99 27,975.79 -700.803 0 2022 LFR SBA IR-1 Attachment 1 Page 4 of 6...
AI summary The text contains numerical data and references to the 2022 Load Forecasting Report (LFR), Standard Billing Adjustment (SBA), and Interim Report 1 (IR-1) from a regulatory proceeding. It appears to be part of an attachment detailing financial or operational inputs, coefficients, and outputs for residential and small general categories.
2028 5 28,367.26 29,068.06 -700.803 0 2028 6 39,192.78 1 0 1 2028 6 28,392.40 29,093.20 -700.803 0 2028 7 39,226.65 1 0 1 2028 7 28,417.54 29,118.34 -700.803 0 2028 8 39,261.10 1 0 1 2028 8 28,443.12 29,143.92 -700.803 0 2022 LFR SBA IR-1...
AI summary The text contains numerical data tables and a reference to '2022 LFR SBA IR-1 Attachment 1 Page 5 of 6,' indicating a regulatory proceeding document related to load forecasting, billing adjustments, and interim reporting in Nova Scotia.
Small General Inputs Small General Coefficients Small General Outputs Single Family Multi Family Completions Completions Residential Customer (Conference (Conference Customer accounts Board data) Board Data) Count Year Month SmlGenCustNMan...
AI summary The document contains data tables related to small general inputs, coefficients, and outputs, including customer account completions and energy demand forecasts. It references the 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report (NSUARB M10569) and includes NSPI responses to information requests from the Small Business Advocate.
Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL
AI summary The document outlines NSPI's responses to information requests from the Small Business Advocate, based on the 2022 Load Forecast Report, as part of the NSUARB proceeding M10569.
Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 Please refer to page 42, Lines 8-11, of the Filing. 4 5 (a) What led to an increase in...
AI summary The 2022 Load Forecast Report (NSUARB M10569) discusses the increase in forecasted EV adoption in Nova Scotia by 2031, attributing it to the federal government's target of 100% EV sales by 2035. The report also states that short-term supply chain issues related to EV battery production are not expected to significantly impact long-term forecasts.
d Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Please refer to Page 43, Lines 12-22. This section states that it is assumed 70% of...
AI summary The NSPI response to IR-9 discusses the management of EV charging, combining time-based rates and aggregator-based charge management. It clarifies that total kWh load remains unchanged between managed and unmanaged scenarios, but peak demand varies. The forecast assumes no impact on overall consumption, with annual load being the same across all cases.
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-15: 2 3 Please describe in detail how electrification is accounted for in the foreca...
AI summary NSPI explains that electrification is accounted for in industrial class forecasts by adding amounts from Figure 34 outside of models, and that different historical data periods are used for residential, commercial, and industrial classes due to varying timescales, as explained on page 31 of the report.
d Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 Please refer to Page 19, Lines 9-20. 4 5 (a) Why does the weather data start year (...
AI summary NSPI explains that weather data starts in 2012 for residential, small general, and general demand models, aligning with those models' timeframes. Small and medium industrial forecasts are econometric and not weather-sensitive. The peak demand forecast uses a 2012–2021 timeframe, consistent with other models, and the use of longer data series for industrial energy forecasts does not affect peak forecasts.
SBA) IR-17 Page 1 of 1 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-18: 2 3 Understanding these are proprietar...
AI summary NSPI is responding to a request for information about E3’s RESHAPE and EV Load Shaping Tool models used in the 10-Year Energy and Demand Forecast. The response indicates that Figure 27 is not an output of the EV Load Shaping Tool model and directs further inquiry to Synapse IR-9 for details on E3’s EV model.
-0.4% 1.6% 0.9% 0.0% 0.0% 0.1% -1.0% 1.3% Residential Intensities - Inputs to Regression Xheat Inputs Efurn HP Heat Secondary Heat Furnace Fans HeatUse Variable Coefficient Total Xheat 2022 2,193 1,214 492 71 1.04 1.004 4,135 2032 1,118 2,...
AI summary The text presents regression analysis inputs for residential heating intensities, comparing 2022 and 2032 projections. Key metrics include Xheat inputs, Efurn, HP Heat, and Secondary Heat, with percentage changes indicating shifts in load profiles. The data highlights variations in furnace fans, heat use coefficients, and total Xheat values across years.
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Reference Report p. 7: “As with any forecast, there is a degree of uncertainty arou...
AI summary The requestor is asking NS Power to confirm whether Appendix D includes scenario analysis for uncertainties in the 2022 Load Forecast Report, specifically related to weather, economics, and the general warming trend. NS Power confirms that only weather (normal) and economics are considered in the scenario analysis using Monte Carlo simulations.
6 Response IR-1: 27 28 (a) Confirmed. Weather (normal) and economics are the only uncertainties considered in 29 Appendix D scenario analysis using Monte Carlo simulations. 30 Date Refiled: July 22, 2022 NSPI (CA) IR-1 Page 1 of 2 REDACTED...
AI summary The response confirms that weather and economic factors are the sole uncertainties considered in Appendix D's Monte Carlo simulations for the 10-Year Energy and Demand Forecast (NSUARB M10569). It references NSPI's responses to consumer advocate information requests.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 (b) Historical signal around the mean is not significant enough for scenario analysis. 2 Conceptually so...
AI summary NSPI explains that historical signal variations are insufficient for scenario analysis, with uncertainty captured in 20-year averages. The warming trend is integrated into the model's 'Model' variable, not as an external factor. The trend variable reduces forecasted residential and commercial demand by 1% and 0.5% respectively by 2032, as detailed in Appendix B and Figure 9.
the impact of the trend variable is a reduction of 59 GWh in the Residential class (around 20 1 percent by 2032) and 16 GWh in the Commercial class (around 0.5 percent by 2032). Date Refiled: July 22, 2022 NSPI (CA) IR-1 Page 2 of 2 REDACT...
AI summary The text outlines projected energy use reductions: 59 GWh (1%) in residential and 16 GWh (0.5%) in commercial sectors by 2032. It references NSPI's 10-Year Energy and Demand Forecast (NSUARB M10569) and redacted consumer advocate responses.
and Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Reference Report p. 10: “Produced in the winter of 2021-2022 and using information 4 ava...
AI summary NSPI clarifies that the 2022 load forecast was finalized in April 2022, using economic data from the Conference Board of Canada (February 2022) and other inputs like load history, solar data, and EIA data available by January 2022. The response addresses the Consumer Advocate's questions about the forecast timeline and data sources.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and NSPI's responses to information requests from the Consumer Advocate. It pertains to demand forecasting and regulatory proceedings involving NSPI and the NSUARB.
56.5 36.2 27.6 37.9 21.5 2032 73.2 55.0 35.7 26.9 37.5 20.9 3 Date Filed: July 8, 2022 NSPI (CA) IR-5 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569)...
AI summary NSPI confirms that the 'DSM captured by end uses' column in the 2022 Load Forecast Report is calculated by subtracting the 'Res DSM Adjustment' from the 'Total Res DSM' column. The forecast uses a regression model incorporating past DSM activity, price, appliance efficiency, and economic variables. NS Power lacks historical appliance-level DSM data, relying instead on class-level annual savings.
appliance level or building shell level DSM data, only annually reported savings at the 25 class level. The treatment of DSM in the forecast is outlined in section 4.6 of the Report. Date Filed: July 8, 2022 NSPI (CA) IR-6 Page 1 of 1 REDA...
AI summary The document references the treatment of demand-side management (DSM) data in the 10-Year Energy and Demand Forecast, noting annual class-level savings reporting. It cites Section 4.6 of the Report and mentions NSPI's responses to the Consumer Advocate's information requests, with the matter numbered NSUARB M10569.
0.973 AIC 7.870301 BIC 8.241966 F-Statistic #NA Prob (F-Statistic) #NA Log-Likelihood -626.49066 Model Sum of Squares 9,931,770.617 Sum of Squared Errors 240,656.468 Mean Squared Error 2,314.00450 Std. Error of Regression 48.10410 Mean Abs...
AI summary The text includes statistical analysis results from a 10-Year Energy and Demand Forecast (2022 Load Forecast Report) and references NSPI's responses to the Consumer Advocate's information requests. It contains model metrics and a matter number (NSUARB M10569) related to regulatory proceedings.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL Model Statistics Mean Abs. % Err. (MAPE) 2.69% Durbin-Watson Statistic 1.831 Durbin-H Statistic #NA Lj...
AI summary The document discusses statistical metrics of a 10-year energy and demand forecast model (NSUARB M10569), noting a 2.69% MAPE and consistent DSM savings (27 MW/year). NSPI asserts that DSM alignment between energy and peak models ensures the model's validity, citing historical consistency and alignment with energy DSM savings.
(b) No. Both provincial and municipal governments have discussed targets related to 29 affordable housing and population growth, but no concrete policies or programs have been Date Filed: July 8, 2022 NSPI (CA) IR-8 Page 1 of 2 REDACTED (C...
AI summary NSPI states no concrete policies exist for affordable housing, with housing forecasts relying on Conference Board data. New customer load estimates consider electric heating and building efficiency. Population impacts are modeled via economic variables, not explicitly.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Reference Report p. 59: “The COVID-19 impact to the Residential forecast is approxima...
AI summary NSPI clarifies that the 'impact' of COVID-19 on the 2022 residential demand forecast refers to the load variable in the forecast model. No additional analysis of pandemic-related demand impacts was conducted as part of the 2022 forecast, and no further analyses are planned.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-10: 2 3 Reference Report p. 71: “Sales in this class have been flat for the last 10 years an...
AI summary NSPI responds to a consumer advocate request regarding its 2022 load forecast, citing the Conference Board of Canada's economic forecasts as the basis for predicting 0.7% annual growth in small industrial class sales. The response highlights reliance on manufacturing GDP data and requests for updated forecasts or model re-runs.
the data series. The series is based on the 29 Conference Board of Canada’s February 5-year forecast plus their 20-year forecast produced 30 in January for the years beyond 2026. Date Filed: July 8, 2022 NSPI (CA) IR-10 Page 1 of 2 REDACTE...
AI summary NSPI's 10-Year Energy and Demand Forecast (2022 Load Forecast Report) relies on the Conference Board of Canada's 5-year and 20-year forecasts. The report compares energy sales to manufacturing GDP, noting historical alignment and no anticipated material changes in the forecast period. The document is part of NSPI's responses to the NSUARB (M10569).
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests REDACTED 1 Request IR-11: 2 3 With reference to the Report pp. 73-74: “One customer makes up over [redacted] of sales 4...
AI summary NSPI responds to Consumer Advocate information requests regarding sales data for a major customer and mid-year energy demand data from the 2022 Load Forecast Report (NSUARB M10569). NSPI clarifies that mid-year calculations are not included in annual reports but provides Jan-May 2022 energy and peak demand data in Attachment 1.
1 Request IR-13: 2 3 Reference NS Power’s 2021 Rebuttal Evidence (M10109), p. 10, where NS Power 4 acknowledges the CA’s comment that “Data on weather circumstances other than 5 temperature is also available,” but does not appear to respon...
AI summary The request (IR-13) challenges NS Power’s reliance on temperature as the sole weather variable in load forecasting, citing its 2021 Rebuttal Evidence (M10109). It asks for analyses supporting temperature’s dominance, data availability on wind speed, precipitation, cloud cover, and snow cover from Environment Canada, and NS Power’s stance on their objectivity. The text also seeks estimates of these variables’ impact on load forecasts.
cover, precipitation and wind speed. 28 (g) Please provide NS Power’s best estimate (quantitative or qualitative) as to the impact 29 of cloud cover, snow cover, precipitation, and wind speed on loads, including peak 30 loads. Date Filed:...
AI summary NS Power responds to a request about weather variables affecting load, referencing a 10-Year Energy and Demand Forecast. They provide a temperature-load correlation (R²=0.8556) and list objective variables from Environment Canada, with precipitation sometimes populated.
1 Response IR-14: 2 3 (a) Weather normalized (WN) sales and net system requirement are provided in the figure 4 below: 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WN Sales 9,635 10,362 10,352 10,318 10,023 10,136 10,419 10,262 10,146...
AI summary The response provides weather normalized sales and net system requirement data from 2012 to 2021, along with an explanation of the weather adjustment methodology used in forecasting. The forecast models assume normal weather, while historical data includes actual weather variables.
degree since 2016. The adjustment for the morning 20 peak in 2019 in Figure 60 was inadvertently omitted from the Report and a copy of the 21 revised figure is provided below. 22 Date Filed: July 8, 2022 NSPI (CA) IR-14 Page 3 of 4 REDACTE...
AI summary The text discusses an adjustment to the 2019 morning peak load in a 10-Year Energy and Demand Forecast, which was omitted from the original report. The adjustment was attributed to differences between morning and evening peak loads, not solely lighting. Actual and weather-adjusted 2021 sales data are also provided.
October-21 October Normal Total Load Load Load Heating Actual Load from Load From Load From Load From Load from Total Heating from Feb From From Load Load Load Day Actual HDD 0 HDD 13 Mar HDD13 HDD 13 HDD0 lag1 lag2 Load (MWh) Month Day HD...
AI summary The text provides a table with data on load and heating degree days (HDD) for a specific period, including actual load, load from HDD0, HDD13, lag1, lag2, and total heating load in MWh. This data is likely used for energy planning and load forecasting.
/17/2020 2020 2 1 19 3.2 1383.92 2/18/2020 2020 2 2 19 -4 1574.533 2/19/2020 2020 2 3 19 2.2 1444.416 2/20/2020 2020 2 4 19 -8.9 1715.486 2/21/2020 2020 2 5 19 -5.7 1601.147 2/24/2020 2020 2 1 19 0.6 1304.951 2/25/2020 2020 2 2 19 4 1339.8...
AI summary The document contains a 10-Year Energy and Demand Forecast from the 2020 Load Forecast Report, submitted as part of the NSUARB M09707 proceeding. It includes data points and NSPI responses to information requests, though the content is partially redacted.
and Demand Forecast (2020 Load Forecast Report) (NSUARB M09707) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 On page 9 of 137, NS Power reports the 2020 growth in system peak at 8.9% and the prior 4 y...
AI summary This text discusses a request for clarification regarding discrepancies in load forecasts and actual demand in Nova Scotia, specifically focusing on the 2019 and 2020 system peak growth rates. The request seeks explanations for the difference between forecast and actual values, including the impact of lighting load and daylight hours.
orecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests CONFIDENTIAL (Attachment Only)
AI summary The document references the 2022 Load Forecast Report (NSUARB M10569) and NSPI's responses to information requests from the Consumer Advocate. The content is marked as confidential and applies to attachments only.
to peak, but this work remains exploratory. Date Filed: July 8, 2022 NSPI (CA) IR-15 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report CA IR-15 Attachment 1 has been removed due to confidentiality. REDACTED...
AI summary The document refers to a 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, which was filed by NSPI in response to consumer advocate information requests. The report was submitted under the NSUARB matter number M10569 and has been partially redacted due to confidentiality.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-16: 2 3 Reference Report p. 77. We understand NS Power’s argument to be as follows: Using the...
AI summary The Consumer Advocate requests clarification on NSPI's use of the 25 MW/°C demand change estimate in weather normalizing load forecasts. NSPI confirms the estimate is not used in the forecast model, and clarifies that weather normalized values are used for explanation and comparison, not as inputs to the model.
not inputs to the forecast model. Date Filed: July 8, 2022 NSPI (CA) IR-16 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer...
AI summary The document discusses the 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, including the handling of weekend peak demand and temperature sensitivity. It notes that the 2018 weekend peak was the only one in the series, with a sensitivity of 23 MW/°C, and that the analysis for this was not part of the 2022 forecast.
and presenting a more accurate approach to estimating the impact of 31 incremental cold on loads. Therefore, the Board directs NS Power to 32 evaluate improvements to the weather normalization estimate and 33 examine the impact of incremen...
AI summary The Board directs NS Power to improve the weather normalization estimate and examine the impact of incremental cold on loads in temperate ranges where peak loads occur. These updates are to be included in future load forecast reports, preferably in the 2022 Report or the 2023 Report.
Matter No. M10569 In the Matter of Nova Scotia Power’s 2022 Load Forecast Report EVIDENCE OF JOHN D. WILSON ON BEHALF OF THE CONSUMER ADVOCATE Resource Insight, Inc. JULY 29, 2022 TABLE OF CONTENTS I. Identification & Qualifications .........
AI summary The document is part of a regulatory proceeding (M10569) concerning Nova Scotia Power’s 2022 Load Forecast Report. John D. Wilson, representing the Consumer Advocate, provides evidence on load forecast improvements, electrification forecasts, and DSM adjustments (IR-5), referencing the 2021 proceeding.
rovince’s efforts to reduce future carbon 14 emissions. 2 My evidence will discuss these and several other revisions. 15 III. Directives from the 2021 Load Forecast Report Proceeding 16 Q: Please summarize NS Power’s actions in response to...
AI summary NS Power updated its load forecasting methods per 2021 Board directives, including revised peak design temperatures and warming trend incorporation. However, it deferred analyzing incremental cold's impact on peak loads, planning to address this in 2023. John D. Wilson testified about these revisions and recommended including wind speed analysis.
nd the 8 impact of incremental cold on peak loads for the 2023 load forecast. 5 As I will discuss below, 9 I recommend that this analysis also include the effect of wind speed. 10 Q: Is NS Power’s incorporation of a warming trend into the...
AI summary The expert evaluates NS Power's load forecasting approach, noting that while the inclusion of warming trends is reasonable, discrepancies exist between model inputs and outputs. The analysis highlights the impact of HDD and CDD trends on residential and commercial loads, citing specific exhibits.
l Model. Evidence of John D. Wilson • Matter No. M10569 • July 29, 2022 Page 4 1 Figure 2: Residential Model Output for WtXHeat Variable, by Month 11 2 3 Figure 3: Small General Model Output for WtXHeat Variable, by Month 12 4 11 Exhibit N...
AI summary John D. Wilson's evidence in Matter M10569 highlights that NS Power's load forecast uses proprietary software not available in spreadsheet format, limiting analysis of warming trend formulations. He recommends NS Power improve climate change scenario methods in its load forecasting.
5 Q: Is NS Power’s utilization of multiple weather stations in the residential forecast 6 reasonable? 7 A: No. NS Power stated that it was unable to use load-weighted data and used a population- 8 weighted approach instead. Its analysis of...
AI summary NS Power's use of population-weighted weather data instead of load-weighted data for residential energy forecasts is criticized for potentially underestimating peak load sensitivity to weather variability. The analysis did not fully evaluate geographically disaggregated weather station impacts, and NS Power has not yet integrated AMI data or evaluated transmission planning load flows for weighting purposes.
evaluated load flows used 24 for transmission planning as possible inputs, and that it is reviewing how to integrate AMI 25 data in order to provide more granular analysis. 16 NS Power has hourly substation load data 26 from the areas repr...
AI summary NS Power is evaluating load flows for transmission planning and integrating AMI data for granular analysis. John D. Wilson recommended focusing on peak load and using multi-hour average temperatures instead of single-hour measures, along with considering wind speed and cloud cover.
predictive of load levels. 18 10 Mr. Wilson also recommended that the Board should require discussion of other weather 11 measures, including wind speed and cloud cover. 19 12 Q: Has NS Power responded to his recommendation on multi-hour a...
AI summary The text discusses recommendations to use multi-hour average temperatures for weather normalization and peak load forecasting, with NS Power noting a 2021 weather-normalized peak discrepancy. It also highlights that population distribution is not a reliable proxy for hourly load distribution, citing Exhibit N-8 from Matter No. M10109.
it is shown that hourly loads vary uniformly across the province. 18 Exhibit N-8, Evidence of James F. Wilson, 2021 Load Forecast Report, Matter No. M10109, p. 14. 19 Id., pp. 5-6. Evidence of John D. Wilson • Matter No. M10569 • July 29,...
AI summary The document discusses uniform hourly load variation across Nova Scotia, citing a 2021 load forecast report. It references NS Power's response to recommendations about wind and cloud cover impacts, emphasizing temperature as the primary load driver through regression analysis.
wind speed is not much higher than without. Because the p-value for both variables is 19 practically zero, the model results indicate that adding wind speed results in a better model. 20 Table 1: Load Regression Statistics Without Wind Spe...
AI summary A regression model demonstrates that incorporating wind speed improves load prediction accuracy, raising R-square from 85.5% to 86.4%. Wind speed can add up to 2,769 MWh to daily load, suggesting its inclusion in ELCC calculations for wind power resources.
ads and peak load events. If it is not already considered, 7 including wind speed in short-term load forecasts for operational dispatch planning could 8 also improve forecast accuracy. 9 Q: What are your recommendations with respect to the...
AI summary The text recommends improving load forecasting by prioritizing peak loads, using geographically differentiated data, multi-hour temperature averages, and wind speed. It also emphasizes stakeholder engagement in method development. The electrification forecast is deemed credible but requires refinement as policies and technologies evolve.
27 forecast will need to be refined as policies and programs become more specific and as more 28 is known about the performance of technologies in the NS Power service territory. For
AI summary The text emphasizes the need to refine forecasts as policies, programs, and technology performance data in the NS Power service territory become more defined and understood over time.
Evidence of John D. Wilson • Matter No. M10569 • July 29, 2022 Page 10 1 determined.” 27 NS Power should identify the gap between existing electrification measures 2 and trends and those included in its load forecast as necessary to achiev...
AI summary John D. Wilson highlights gaps in NS Power's load forecasting models for electrification, noting insufficient alignment with federal/provincial policy goals. He recommends updating models to account for warming trends, electrification impacts, and more accurate EV charging demand projections during peak loads.
Memorandum TO: NOVA SCOTIA UTILITY AND REVIEW BOARD (NSUARB) FROM: DAVID WHITE DATE: JULY 29, 2022 RE: EVIDENCE RE THE NSPI 2022 LOAD FORECAST (M10569) Introduction For many years Nova Scotia Power, Inc. (NSPI) has filed a load forecast re...
AI summary The 2022 NSPI Load Forecast Report shows a 3.4% overall increase, contrasting with recent forecasts predicting modest declines. The forecast incorporates SAE model results, DSM adjustments, and factors like customer growth, with significant increases post-2024 linked to electrification.
re more fully technologies to control peak space heating and water heating loads. • Explore whether battery storage and solar/battery storage combinations could modify the peak loads. • Provide updates on the water heating load control pro...
AI summary The text outlines initiatives to manage peak loads through battery storage, solar/battery combinations, and DSM program updates. It emphasizes revising pandemic impacts on commercial sales, monitoring DSM savings, and addressing design day temperature changes due to global warming. Heat pumps and water heaters are highlighted as critical for residential energy efficiency.
oad Forecast 6 Energy Forecast In Table 1, we saw the sectorial components of the Nova Scotia load. Here, we will review each of them in sequence, going in the same order as in the forecast report. Major Inputs and Regression Models In add...
AI summary The document discusses Nova Scotia Power's (NSPI) energy forecast methodology, emphasizing economic drivers like household compensation and new construction. It references the Conference Board of Canada's (CBoC) 20-year forecast and notes a 7.3% real-term increase in household compensation and 6.1% growth in new customers. Regression models are used, with a recommendation to annually review driver selection.
stomers increased by 1.3 percent over the forecast period. New customers increased the total residential load by 4.9 percent.3 The choice of drivers seems reasonable but should be reviewed every year. The residential statistical model incl...
AI summary The 2022 Load Forecast Report discusses residential load increases due to new customers and the impact of a COVID-19 binary variable adjusted over time. The model's choice of economic indicators for commercial and industrial sectors is deemed reasonable, though uncertainties in economic forecasts are noted. The methodology for load forecasting and DSM effects are highlighted as areas requiring ongoing review.
iven the need for general consistency within Canada as a whole. We note however the inherent uncertainty of all economic forecasts and also that the future may diverge significantly from the forecast. The forecast now gives more considerat...
AI summary NSPI updated its forecast to reflect climate change impacts, adjusting heating and cooling degree day trends. The residential sector, comprising 45% of load, is projected to grow 6.6% with DSM programs, versus 13% without. Additional weather data had minimal impact and was not incorporated.
stomer load. The residential forecast (which includes the effects of DSM programs) increases by 6.6 percent over the forecast period. Without DSM programs, the increase would be roughly twice as much. NSPI changed the economic drivers in t...
AI summary NSPI's residential load forecast shows a 6.6% increase over the forecast period, significantly reduced by DSM programs. The forecast model uses economic drivers like new construction and household compensation, with the SAE model capturing key load factors such as heat pumps and EVs. Historical DSM savings and other variables influence average customer use calculations.
al DSM savings, a Covid term, and some binary terms for specific months.8 6 Load Forecast Report, Figure 14. 7 Load Forecast Report, Appendix B, p.5. 8 Load Forecast Report, Appendix B, pp.1-7. Synapse Energy Economics, Inc. Evidence Regar...
AI summary The document outlines variables (XHeat, XCool, XOther) used in the NSPI 2022 Load Forecast, detailing their components and net changes. XHeat decreases slightly, XCool increases significantly due to heat pump cooling, and XOther rises slightly from appliance and efficiency trends.
trends. The primary change drivers for XOther are water heat (increased electric heater saturation), reductions in lighting use, and miscellaneous. The net effect is to increase XOther by 1.5 percent. From this one can see that there are m...
AI summary The document analyzes residential energy use factors, noting heating (42%), cooling (2%), and other uses (56%) drive average consumption. Forecasts show slight increases from XHeat (-0.4%), XCool (+1.6%), and XOther (+0.9%), with NSPI applying adjustments for new customers, EVs, solar, RTR markets, and DSM savings. Appendix B provides regression model results and adjustments.
rom Page 5 of Appendix B. The first column shows the SAE regression model results, and the other columns reflect various adjustments to the forecast. 9 Load Forecast Report, Appendix B, pp.6-7. Synapse Energy Economics, Inc. Evidence Regar...
AI summary The document presents a residential load forecast analysis using a SAE regression model, adjusted for factors like EV adoption, solar energy, and demand-side management (DSM). It quantifies load changes from 2022 to 2032, showing increased residential demand and the impact of DSM programs on energy consumption.
integrated the heat pump and hot water end-uses into the intensity calculations and regression model results rather than treating them separately. However, Figure 41 of the report does provide some 16 Id, p. 44 17 Id, pp. 45-47. 18 Id, pp....
AI summary The analysis integrates heat pump and hot water usage into load forecasting models, noting a net 541 GWh increase from heating/cooling but offset by 549 GWh reduction in baseboard heating. The commercial sector's load decreased by 0.58% over the forecast period, with subsector composition detailed. Electric vehicle adoption drives a 10.5% load increase. The SAE model's key drivers require reevaluation.
e. In 2022, the Small General Service group represented 10 percent of the commercial load, the General Service group represented 75 percent, and the Large General Service group represented 15 percent. Our comments focus on the General Serv...
AI summary The text analyzes commercial load distribution, focusing on the General Service group (75% of commercial load) and highlights a projected 3.7% sales decline by 2032, driven by DSM program reductions (-7.3%) and offsetting factors like electrification (+2.4%). It questions the cost-benefit analysis of commercial electrification programs and notes the absence of COVID-19 variables in models, requiring reevaluation.
e included in the commercial regression models. Results to date indicate that 2022 commercial sales are in line with those of 2019.22 This will need to be reevaluated and updated in the next forecast. Large general sales are expected to in...
AI summary The document discusses updated load forecasts for Nova Scotia's commercial and industrial sectors. Commercial sales in 2022 align with 2019 levels, requiring reevaluation. Industrial forecasts show a 2.4% growth rate, down from prior years, with methodology relying on surveys and estimates due to uncertainty around large customer demand.
egory is based on customer surveys and new customer inquiries. Thus, the methodology is different than for the other sectors and should be considered as an informed estimate rather than a calculation. We note too that the survey of the Lar...
AI summary The industrial energy sales forecast for 2022-2032 incorporates survey data and expansion projections, noting pandemic-driven load reductions and a 3.9% overall increase. The methodology is deemed an estimate due to reliance on customer surveys. Uncertainties include major customer operational changes and unclear DSM effects in the industrial sector, prompting a request for NSPI clarification.
clear in the report how much of the commercial and industrial demand savings presented in Figure 36 are contained in the industrial forecast. We ask NSPI to clarify the DSM effects for each sector. Synapse Energy Economics, Inc. Evidence R...
AI summary The text requests NSPI to clarify the breakdown of commercial and industrial demand savings by sector, question the leveling-off of electrification impacts post-2027, and highlight discrepancies in municipal sector energy data. It also notes that DSM adjustments in the SAE model are half of full savings due to historical data inclusion.
t half as much as the full DSM savings. This is because the SAE model already includes the effects of some of those savings in its statistical equations, which are based on historical data and trends. The residential statistical model incl...
AI summary The document discusses adjustments to residential and commercial/industrial DSM savings forecasts, noting methodological concerns due to significant coefficient changes between years. It highlights discrepancies in the C/I adjustment and requests clarification from NSPI on the methodology's robustness.
a reduction of 2 GWH in 2022 to 24 GWH in 2032. For the medium general load, it goes from 17 to 187 GWh, or 7.9 percent of the load in 2032. No explicit adjustments are indicated for other customers. The adjustments discussed in the foreca...
AI summary The forecast discusses load adjustments, noting a significant increase in system peak and the need to adjust DSM savings factors. Adjustments are deemed reasonable but with statistical uncertainties. Increased DSM savings may require upward adjustments.
end-use. The electrification of vehicles (identified as EV) is also a major growth factor that can be mitigated with time- of-charge controls. We also wonder if more can be done with demand response. The interruptible load representing pri...
AI summary The text discusses concerns about peak load growth driven by electric vehicle adoption and industrial demand, urging NSPI to explore time-of-use rates and expanded demand response measures. It highlights the need for updated forecasts incorporating post-2026 demand response programs from the IRP Action Plan and acknowledges adjustments in load forecasting methodology.
f induction cooking is more efficient than current stoves and its possible effects considered. Induction cooking is a new technology that should be evaluated for its effects on energy and peak loads. For the commercial sector, the heat end...
AI summary The text requests NSPI to evaluate commercial heat use impacts on peak loads and refine peak forecasting methods, citing discrepancies between forecasts and actual data. Sensitivity analyses highlight weather and economic factors as key uncertainties, with ongoing efforts to improve forecasting using class-specific and AMI data.
conomic impact variations. Details can be found in Appendix D and generally appear plausible, with temperature being the greatest near-term uncertainty and the economic uncertainty dominating by 2032. The peak load sensitivity used a set o...
AI summary The text highlights the need for a 2032 peak load sensitivity analysis, requests clarification on assumptions in Figure D8, and recommends future analyses incorporating proactive policy actions. It also notes improved alignment between the 2031 forecast and 2020 IRP scenarios.
y the IRP electrification scenarios than was the previous one. This indicates a greater consistency between the forecast and the IRP analysis and addresses an issue raised in our previous evidence. Synapse Energy Economics, Inc. Evidence R...
AI summary The NSPI 2022 Load Forecast shows improved consistency with IRP electrification scenarios, addressing prior issues. Synapse Energy Economics recommended re-evaluating variables like housing completions, retail sales, and income indicators to enhance forecast accuracy. NSPI acknowledges addressing most recommendations but notes remaining considerations.
effects on energy and peak loads (p.20). • We ask NSPI to evaluate commercial heat use more fully as to what is driving it and how the peak impacts could be moderated (p.20). • We ask that NSPI review its peak forecasting methodology in li...
AI summary The text requests NSPI to improve peak load forecasting by evaluating commercial heat use, reviewing methodology, and conducting sensitivity analyses. It also supports NSPI's efforts to enhance forecast transparency. Synapse Energy Economics, Inc. provided evidence on the 2022 load forecast.
Nova Scotia Utility and Review Board IN THE MATTER OF The Public Utilities Act - -and- IN THE MATTER OF NOVA SCOTIA POWER’S 2022 LOAD FORECAST REPORT (M10569) EFFICIENCYONE EVIDENCE FILED July 29, 2022 NOVA SCOTIA POWER 2022 LOAD FORECAST...
AI summary EfficiencyOne submits evidence in the Nova Scotia Utility and Review Board proceeding regarding Nova Scotia Power’s 2022 Load Forecast Report (M10569), covering electrification modeling, demand response plans, and forecasting methodologies. The document outlines sections on heat pump modeling, alternative heating systems, and demand response treatment in the forecast.
...................................................................................................................... 14 LIST OF TABLES Table 1: Comparison of Average Annual Changes in Net System Requirement and System Peak Demand in the...
AI summary Nova Scotia Power submitted its 2022 Load Forecast Report to the NSUARB, while EfficiencyOne requested information and reviewed responses. The report provides a 10-year energy and peak demand forecast for in-province customers, serving as a key input for NS Power's planning and operations.
rket Rules, the Load Forecast 15 report provides a key input to the overall planning, budgeting and operating activities of NS 16 Power to fulfil customer load. Specifically, the Load Forecast: 17 18 • forms the basis, in part, for future...
AI summary The Load Forecast is central to NS Power's planning, budgeting, and operations, informing capital planning, IRP activities, DSM studies, and rate decisions. E1 emphasizes the need for improved accuracy and proposes recommendations focused on electrification and demand response to refine NS Power's forecasts.
as supplemental heat, and gas heating 11 systems as supplemental heat, would have a different impact on load and would likely have a 12 different combination of system investment requirements. 13 14 Given that the 2022 Load Forecast indica...
AI summary The text discusses the impact of heating technologies on load forecasting and grid flexibility, emphasizing the need for peak load management in a net-zero grid. It references the 2022 Load Forecast and the RAP paper 'Heating Without the Hot Air,' which highlights principles for efficient and flexible heat decarbonization.
the forecast it was assumed that E1’s proposed demand response measures are 6 incorporated into the larger demand response program that is being developed as part of the IRP 7 Action Plan.” 24 8 9 As part of its 2023-2025 DSM Plan, E1 subm...
AI summary E1 submitted a 2023-2025 DSM Plan targeting 18 MW of demand response by 2025 but questions NS Power's 50 MW forecast, requesting clarification on the 32 MW gap. E1 also emphasizes the need for consistent treatment of demand response resources in the 2022 Load Forecast.
r Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL
AI summary E1's responses to NS Power's information requests regarding the 2022 Load Forecast Report (M10569). The document addresses energy and demand forecasting methodologies, highlighting interactions between EfficiencyOne and Nova Scotia Power Incorporated.
Year Forecast Energy Savings Forecast Demand Actual Energy Actual Demand Savings Savings Savings 2019 n/a (2019 DSM Plan was not modelled) M09651, E-1, 2019 DSM Evaluation Reports, Existing Residential Program, Table 36, page 65. 2020 M090...
AI summary The document outlines forecasted and actual energy savings from 2019 to 2024, referencing DSM evaluation reports and technical tables. It links forecast data to specific matters (e.g., M09651, M10473) and EfficiencyOne (E-1) appendices, highlighting discrepancies between planned and realized energy efficiency outcomes.
r Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL
AI summary E1's responses to NS Power's information requests regarding the 2022 Load Forecast Report (M10569). The document addresses energy and demand forecasting methodologies, highlighting interactions between EfficiencyOne and Nova Scotia Power Incorporated.
• Rows 260, 261, 269, 270 • Column Z 2024 M10473 E-1(i) Appendix A Attachment 4 2023- n/a 2025 Settlement Plan Measure Level Energy Efficiency Technical Tables • Rows 260, 261, 269, 270 • Column Z 2025 M10473 E-1(i) Appendix A Attachment 4...
AI summary EfficiencyOne (E1) responds to Nova Scotia Power Incorporated (NS Power) information requests regarding the 10-Year Energy and Demand Forecast (2022 Load Forecast Report) in regulatory proceeding M10569. The document includes references to Energy Efficiency Technical Tables and forecast data from 2012–2017.
Filed: September 26, 2022 NON-CONFIDENTIAL 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary The document titled '2022 Load Forecast Report Rebuttal' was filed on September 26, 2022, as part of a regulatory proceeding. It addresses challenges or disputes related to load forecasting methodologies and assumptions in the 2022 report.
Page 3 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 1.0 INTRODUCTION 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market Rules1, 4 the Nova Scotia Power System Operator (NSPSO) is requ...
AI summary The 2022 Load Forecast Report rebuttal discusses NSPSO's annual submission to NSUARB, interventions from advocates and stakeholders, and Synapse's assessment of the forecast's reasonableness amid rising energy demand due to electrification and growth. The Board directed a paper hearing following the report's filing.
Page 4 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary This document presents a rebuttal to the 2022 Load Forecast Report, addressing concerns or providing alternative analyses regarding electricity demand projections in Nova Scotia.
1 Synapse’s evidence further states that NS Power has been responsive to the recommendations 2 Synapse made on the 2021 Load Forecast Report and which were included in the Board’s decision: 3 4 Our report from last year included several re...
AI summary Synapse acknowledges NS Power's responsiveness to 2021 Load Forecast Report recommendations, including addressing housing completions, retail sales variables, and income indicators. The Board's decision incorporated these recommendations, though further evaluation of model variables is needed.
understanding of demand on the load by time of day is 32 achieved. 33 34 We found NSPI to be responsive to these recommendations and are attaching 35 those responses to Synapse IR-43 to this report. We also note that some changes 36 are be...
AI summary The document discusses NSPI's responsiveness to recommendations, with some changes delayed until 2023. It references a rebuttal evidence report and cites Synapse IR-43 as part of the 2022 Load Forecast Report process.
2 Page 5 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 2.0 RESPONSE 2 3 As outlined below, the evidence from Synapse, the CA, and E1, as well as the SBA’s submission 4 are primarily focused on the following issues: 5 6 1. Fur...
AI summary The 2022 Load Forecast Report rebuttal highlights three key areas: electrification's impact on energy/peak demand, new technologies (smart grid, time-variable rates), and weather normalization improvements. NS Power acknowledges the need for further investigation and will incorporate data from initiatives like the Integrated Resource Plan and Smart Grid Nova Scotia into future forecasts.
Page 6 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary The document presents a rebuttal to the 2022 Load Forecast Report, challenging its assumptions or methodologies. Key focus areas include energy demand projections and system reliability considerations, though specific arguments are not detailed in the provided text.
lude 24 them in the 2023 Load Forecast. 25 26 For clarification, weather normalization refers to an analysis of the variance between forecast and 27 actual values, but it is not an input to the forecast. 28 5 M09321, Exhibit N-9, NS Power...
AI summary The text clarifies that weather normalization analyzes variance between forecast and actual values but is not an input to the 2023 Load Forecast. It references NS Power's On-Bill Financing Report (Exhibit N-9) from October 30, 2020, filed in Matter M09321.
Page 7 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 3.0 RECOMMENDATIONS 2 3 Synapse, the CA, SBA and E1 provided additional recommendations for future Load Forecasts. 4 NS Power’s responses to those recommendations are set o...
AI summary The document outlines Synapse's recommendations for improving load forecasts, emphasizing heat pump impact analysis. NS Power responds that it will incorporate new data, including an Itron study, into future forecasts. Key focus areas include heat pump adoption and load forecasting methodologies.
orecast. 6 M10569, Exhibit N-9, Synapse Evidence, July 29, 2022, page 1. 7 M10569, Exhibit N-9, Synapse Evidence, July 29, 2022, pages 23-24. DATE FILED: September 26, 2022 Page 8 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary The document references the 2022 Load Forecast Report Rebuttal, citing Synapse Evidence exhibits (M10569, pages 1, 23-24) and noting the filing date of September 26, 2022. It appears to be part of a regulatory proceeding involving energy forecasting and evidence submission.
2 Page 8 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary The document title indicates a rebuttal to the 2022 Load Forecast Report, suggesting a regulatory proceeding involving electricity demand projections. No substantive content is visible in the provided text, which only includes a page header and classification label.
1 The increased initial cost compared to a regular resistance heater tank and the 2 fact that the unit will cool the space in which it is installed will likely temper 3 uptake of this technology in the near term. 4 5 3.1.3 Recommendation 3...
AI summary The text discusses the limited near-term uptake of a water heating technology due to higher initial costs and cooling effects. NSPI is requested to update the load forecast on water heating load control and SGNS project results. NS Power responds with preliminary data and mentions ongoing analysis, including a joint project with E1.
Forecast. Information arising from the SGNS project is reported to the 31 NSUARB through semi-annual reporting under the SGNS project matter. 8 8 M09519. DATE FILED: September 26, 2022 Page 9 of 25 2022 Load Forecast Report Rebuttal NON-CO...
AI summary The document references the SGNS project's semi-annual reporting to NSUARB under matter M09519, part of the 2022 Load Forecast Report Rebuttal filed on September 26, 2022.
, 2022 Page 9 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 3.1.5 Recommendation 5: 2 3 “We ask that more complete results of the SGNS project regarding 4 battery storage be included in the next load forecast report. We 5 fur...
AI summary The document includes two recommendations and NS Power's responses. Recommendation 5 requests inclusion of SGNS project battery storage results and EV battery peak management analysis. NS Power states data collection is ongoing, with updates reported to NSUARB. Recommendation 6 questions commercial electrification programs' appropriateness; NS Power clarifies no specific programs exist, referring customers to third-party options.
ology is sound. 24 25 3.1.18 Recommendation 18: 26 27 “We ask that NSPI provide a 2032 peak sensitivity analysis similar to what was 28 done for energy in Appendix D.” 29 DATE FILED: September 26, 2022 Page 15 of 25 2022 Load Forecast Repo...
AI summary The text includes a recommendation requesting NSPI to provide a 2032 peak sensitivity analysis similar to the one in Appendix D for energy forecasting.
ation. The 2022 32 evergreen IRP work will assess scenarios and sensitivities evaluating updated 33 electrification profiles coupled with ranges of DSM programming and other peak DATE FILED: September 26, 2022 Page 16 of 25 2022 Load Forec...
AI summary The 2022 Load Forecast Report Rebuttal discusses the 'evergreen IRP work' evaluating updated electrification profiles, DSM programming ranges, and peak management scenarios as part of Nova Scotia Power's integrated resource planning process.
2022 Page 16 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 mitigation strategies, intended to provide the necessary context to assess the 2 current forecast with consideration for changes in policy/technology. 3 4 3.2 Consume...
AI summary The Consumer Advocate (CA) recommends that NS Power analyze wind speed's impact on peak loads in its 2023 forecast and focus weather analysis on peak load. NS Power agrees, committing to include multi-hour temperature averages and wind speed analysis in the 2023 Load Forecast Report.
on 31 weather with respect to peak load for the 2023 Load Forecast Report. The impact 11 M10569, Exhibit N-8, Evidence of John Wilson, July 29, 2022, pages 2-12. DATE FILED: September 26, 2022 Page 17 of 25 2022 Load Forecast Report Rebutt...
AI summary NS Power responds to recommendations regarding weather normalization methods, stakeholder engagement for load forecasting, and addressing electrification gaps. It confirms stakeholder review of weather data methods and plans to incorporate future electrification data into load forecasts.
outlined in Section 2, refining the impact of electrification on the load 29 forecast will be undertaken in future forecasts through the inclusion of new 30 learnings and data. 31 DATE FILED: September 26, 2022 Page 18 of 25 2022 Load Fore...
AI summary The text references refining the impact of electrification on load forecasts through future forecasts incorporating new data and learnings. It is part of the '2022 Load Forecast Report Rebuttal' filed on September 26, 2022, as a non-confidential document.
line loss determination model. NS Power should report on its 32 progress on a quarterly basis until the project is complete to the 33 satisfaction of the Board.” 34 DATE FILED: September 26, 2022 Page 19 of 25 2022 Load Forecast Report Reb...
AI summary NS Power clarifies that line loss by rate class is not used in the system load forecast but is relevant to rate setting. The Small Business Advocate (SBA) urges NSPI to clarify how SGNS project data, particularly on EV load and peak contributions, is incorporated into the Load Forecast. NS Power states SGNS data is being analyzed and will be considered for future Load Forecast updates.
Page 20 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL
AI summary The document title indicates a rebuttal to the 2022 Load Forecast Report within a regulatory proceeding. No substantive content is visible in the provided text, which is marked as non-confidential and appears on page 20 of 25.
22 Page 24 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 4.0 CONCLUSION 2 3 The requirement to file the Load Forecast Report is an annual requirement under the provisions of 4 the Nova Scotia Wholesale and Renewable to Retail...
AI summary NS Power has submitted the 2022 Load Forecast Report, highlighting continuous improvements in transparency and accuracy with input from Synapse. The report reflects ongoing efforts to refine forecasting methodologies and explain underlying factors. NS Power requests the Board's acceptance of the report.
ce was filed by the CA, EOne, and Synapse on July 29, 2022. On August 12, 2022, EOne responded to IRs from the NS Power. NS Power filed its Rebuttal Evidence on September 26 ,2022. 2022 Load Forecast NS Power uses two discrete elements for...
AI summary The 2022 Load Forecast by NS Power incorporates Statistically Adjusted End-Use (SAE) models and Demand Side Management (DSM) adjustments. Near-term growth is attributed to customer expansion and higher electricity use, while mid-to-long term growth stems from electric heating, EV adoption, and infrastructure projects. NS Power expects DSM initiatives and solar installations to offset some growth, projecting 0.3% annual NSR increases through 2032.
forecasted that NSR will experience an average annual increase of 0.3 percent between 2023 and 2032. With respect to system peak, NS Power forecasts annual growth of 1.6 percent between 2023 and 2032. These forecasts result in a projected...
AI summary NS Power forecasts 0.3% annual NSR growth and 1.6% system peak growth from 2023-2032, projecting 5.7% higher NSR and 28.7% higher peak demand by 2032 compared to 2021. Table 1 compares forecasted vs actual energy and peak demand changes from 2017-2022, highlighting significant 2022 growth discrepancies.
-0.6% 2018 0.8% 3.5% 6.0% 2.7% 2017 1.0% 0.6% -0.03% -4.4% The load forecast is a foundational input in relation to NS Power’s overall planning, budgeting, and operating activities, including generation requirements, capital program, fuel...
AI summary The document emphasizes the critical role of load forecasting in NS Power's planning and operations, noting the Board's past concerns about forecast accuracy. NS Power revised the 2022 Load Forecast by incorporating warming trends and EV adoption models, with 2021 variances attributed to weather (184 GWh) and the pandemic (63 GWh). The Board directed improvements in forecasting methodology.
ear actual 2 Based on Table A2 in 2022 and 2021 Load Forecast reports and in Table A3 in each annual report from 2015 to 2020 Document: 298717 -3- However, the significant increase in Load for 2022 is mainly attributed to the application o...
AI summary NS Power attributes a 2022 load increase to E3's estimated peak model, planning to validate it with AMI data. Intervenors praised forecast improvements but raised concerns about heat pump adoption, EV growth, new demand-reducing technologies, and peak management via Smart Grid Nova Scotia (SGNS) project outcomes.
EV forecast, investigation of new technologies to reduce energy and peak demand, and the management of peak through the Smart Grid Nova Scotia (SGNS) project results and direct control water heaters. The CA filed evidence prepared by John...
AI summary The Consumer Advocate (CA) submitted evidence from John Wilson of Resource Insight Inc., recommending NS Power improve climate change scenario analysis, enhance weather station integration, refine peak load forecasting models, address modeling errors in residential energy models, and adjust assumptions about heat pump usage. Wilson also requested quarterly updates on the line loss determination model.
me framework to assessing the value of different DR programs and an effective load carrying capacity (ELCC) factor be applied. Document: 298717 -4- In its evidence to the Board, Synapse noted continued improvement in the Load Forecast, how...
AI summary Synapse submitted evidence to the Board highlighting improvements in NS Power's Load Forecast but recommending revisions, including re-evaluating economic variables, investigating heat pump impacts, updating EV and battery storage analyses, and clarifying DSM adjustments. Synapse also requested quantification of ETS demand moderation, peak load sensitivity analysis, and methodological reviews for peak forecasting.
mpacts on peak. Synapse asked that NS Power review its peak forecasting methodology and provide a peak sensitivity analysis as well as the assumptions and calculations used. NS Power Reply Submission In its Reply, NS Power addressed the co...
AI summary Synapse requested NS Power to review its peak forecasting methodology and provide sensitivity analyses. NS Power agreed to incorporate multi-hour weather data, refine electrification impact assessments, and evaluate hybrid demand response scenarios while integrating findings from recent studies and projects.
adjustment to EV load shapes. A request for the information on the benefits and costs of commercial electrification programs was denied as NS Power has not performed such an analysis. Board Findings The Board notes that NS Power has agreed...
AI summary The Board acknowledges NS Power's agreement to intervenors' Load Forecast recommendations but mandates further implementation, including detailed analysis of electrification impacts, economic model evaluations, and stakeholder engagement. NS Power must also refine weather-normalized calculations and consider Canadian-specific elasticity data in future forecasts.
May 2, 2022 [email protected] Jennifer Ross Manager, Regulatory Strategy Nova Scotia Power Inc. 1223 Lower Water Street P. O. Box 910 Halifax, NS B3J 2W5 Dear Ms. Ross: M10569 - Nova Scotia Power Inc. (NS Power) – 2022 Load Forecast...
AI summary The Board approved NS Power's confidentiality undertaking for its 2022 Load Forecast Report, subject to objection rights. The matter will proceed via paper hearing with specified filing deadlines. Panel members include Richard Melanson and Julia Clark, with Crystal Henwood as Regulatory Affairs Officer.
NSUARB-NSPI Matter No. Ml0569 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: The Public Utilities Act, R.S.N.S., 1989, c. 380 as amended - and - IN THE MATTER OF: An Application re Nova Scotia Power Incorporated's 2022 Load Forecas...
AI summary The Small Business Advocate intervenes in a proceeding concerning Nova Scotia Power's 2022 Load Forecast Report, representing small business interests. The notice includes contact details for the intervenor.
2020 M10569 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: The Public Utilities Act, R.S.N.S. 1989, c.380 as amended IN THE MATTER OF: Nova Scotia Power Incorporated’s 2022 Load Forecast Report NOTICE OF INTERVENTION OF: Efficiency...
AI summary EfficiencyOne intervenes in Nova Scotia Power's 2022 Load Forecast Report proceeding under the Public Utilities Act, asserting a direct interest as the electricity efficiency franchise holder. They intend to participate in the hearing and provide contact details for their counsel, The Breton Law Group.
Matter: M10569 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: The Public Utilities Act, RSNS 1989, c.380, as amended - and - IN THE MATTER OF: NSUARB Matter No. M10569 – NSPI’s 2022 Load Forecast Report NOTICE OF INTERVENTION OF: H...
AI summary Heritage Gas Limited seeks intervention in a proceeding under the Public Utilities Act regarding NSPI’s 2022 Load Forecast Report. Heritage Gas is a Nova Scotia-based natural gas distributor requesting participation in the regulatory process. The notice outlines contact details for their representative, Michael Johnston.
M10569 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 (2022 Load Forecast Report) NON-CONFIDENTIAL IN...
AI summary The Nova Scotia Utility and Review Board requests Nova Scotia Power Inc. to address the impact of COVID-19 on future electricity sales in its 10-year energy forecast, referencing a 2020 decision letter (M09707). The request emphasizes considering near-term and long-term effects on electricity demand.
ber 20, 2020, the 3 Board directed NS Power to consider the near-term and long-term effects of COVID-19 on future 4 electricity sales. On pages 59-60 of 98 of the application, NS Power states: 5 The COVID-19 variable that was added in 2020...
AI summary The Nova Scotia Utility and Review Board directed NS Power to assess short- and long-term impacts of COVID-19 on electricity sales. NS Power adjusted the pandemic variable in its forecasts, reducing its impact from 2020 to 2022, assuming hybrid work models would persist. The Board questions whether the modified variable in Appendix B should be applied in future forecasts.
2022 2023-2031 Forecast COVID-19 1 0.75 0.38 0.38 variable 2022 Load September July 2020 October 2020 March 2022 Forecast 2022 COVID-19 1 0.5 0.33 0.16 variable 13 14 Request IR-2: 15 With reference to the 2020 Load Forecast Decision Lette...
AI summary The Board directed NS Power to use additional EV forecast sources following the 2020 Load Forecast Decision (M09707). NS Power relied on Dunsky Energy Consulting's 2020 study as the most recent input. The request (IR-2) asks to compare Dunsky's EV forecast with E3’s, specifying input variables and a side-by-side analysis.
e specify the input variables used in each forecast, 24 identifying Nova Scotian or Canadian specific data, and provide a side-by-side comparison 25 of the two forecasts. 26
AI summary The text requests detailed specification of input variables in forecasts, identification of Nova Scotia or Canadian-specific data, and a comparative analysis of two forecasts. It emphasizes transparency in forecasting methodologies and data relevance to the region.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 2 of 10 1 Request IR-3: 2 With reference to Section 4.3 Economic Information, page 29 of 98. The application notes that 3 “Economic and other provincial statistics used in the...
AI summary The document contains two regulatory requests (IR-3 and IR-4) seeking clarification on economic data usage in load forecasting. IR-3 asks for data sources, adjustments, and definitions related to the Conference Board of Canada's 20-year forecast and 'Household Compensation.' IR-4 requests confirmation that housing completions data aligns with residential customer counts.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 3 of 10 1 a) Please cite the sources for the GDP estimates used in the forecast model. 2 b) Please confirm the year that the values are chained to. 3 i. Please confirm that the...
AI summary The UARB requests clarification on GDP estimates, employment data updates, EIA data calibration, and the realism of heat pump adoption forecasts in NSP's application. Questions focus on data sources, methodology alignment with Statistics Canada, and justification for modeled scenarios.
n why NS Power considers this modeled 24 scenario realistic? 25 26 Request IR-8: 27 In matter M10109, Section 4.4 End-Use Intensity Trends, page 32 of 89, the application indicates 28 that NS Power expects heat pump saturation to increase...
AI summary The request (IR-8) in matter M10109 asks NS Power to explain the discrepancy between two forecasts of heat pump market saturation (55% by 2031 vs. 66% by 2032) and provide evidence for the updated prediction.
dence used to predict the level of market saturation and explain the 31 difference in findings between the forecast provided in matter M10109 and the current 32 forecast. 33
AI summary The text references using evidence to predict market saturation and explains discrepancies between the forecast in matter M10109 and the current forecast, focusing on forecasting methodology differences.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 4 of 10 1 Request IR-9: 2 With reference to Section 4.4 End-Use Intensity Trends, page 37 of 98, the application states that 3 “E3 saturation estimates had a lower starting poi...
AI summary The UARB requests clarification from NSP regarding model adjustments in load forecasting, discrepancies between E3 and NSP models, and changes in heat pump saturation and intensity figures. Questions focus on model assumptions, forecast revisions, and data alignment across different planning periods.
wer’s application in 24 matter M10109. 25 b) Please explain why the heating intensity and cooling intensity has increased from the 2021 26 forecast provided in Figure 13 of NS Power’s application in matter M10109. 27 28 Request IR-12: 29 W...
AI summary The text requests explanations for increases in heating and cooling intensity forecasts from NS Power's 2021 data in matter M10109 and asks for historical energy usage data from 2016–2021 to clarify projected energy usage trends per house.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 6 of 10 1 i. Please provide a copy of the data used in the mode and the source for driving 2 habits. 3 b) If residential sales are expected to be elevated due to increased work...
AI summary The UARB requests data on residential driving habits, expanded tables for PV impact and electrification forecasts, explanations for price elasticity choices, and reasons for declines in DSM savings forecasts. Requests focus on data transparency, methodological consistency, and alignment with Canadian standards in load forecasting.
Request IR-22: 30 With reference to Section 5.0 Residential Sector, page 59 of 98, the application states, “The 31 residential average use model is specified using a SAE model structure and the customer count 32 forecast is based on foreca...
AI summary The UARB requests clarification on whether the residential forecast in the application uses housing starts or completions as stated in conflicting sections of the document, referencing specific pages and the SAE model structure for forecasting.
er count 32 forecast is based on forecast housing starts”. Please confirm whether the residential forecast 33 uses housing starts as stated on page 59, or completions as stated on page 30. 34
AI summary A participant questions the methodology of the residential forecast, asking whether it uses housing starts (as stated on page 59) or completions (as stated on page 30), highlighting a potential discrepancy in the data basis for projections.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 7 of 10 1 Request IR-23: 2 With reference to Section 5.0 Residential Sector, the application discusses the model used to 3 determine the residential sales forecast. 4 a) Please...
AI summary The UARB requests clarifications on residential electricity sales forecasts, including rationale for excluding substitute prices, multi-unit housing trends, weather-adjusted sales metrics, COVID-19 impact variations, and EV penetration rate assumptions. These requests focus on forecasting methodologies, energy usage patterns, and affordability factors affecting residential sector projections.
mall Industrial, page 71 of 98, the application indicates that “sales 28 in this class have been flat for the last 10 years and are expected to grow by 0.7 percent annually” 29 because of economic growth. 30 a) Please explain in detail, th...
AI summary The text raises questions about the anticipated 0.7% annual sales growth for the 'mall Industrial' class despite flat performance over the past decade and a discrepancy between the 2021 Load Forecast (0.6% growth) and the current estimate. It seeks clarification on economic factors driving growth and the basis for the updated forecast.
y 33 over the forecast period, given the 2021 Load Forecast had estimated growth of 0.6 34 percent annually over this period, and yet sales growth has been flat since 2011. 35
AI summary The text highlights a discrepancy between the 2021 Load Forecast projecting 0.6% annual growth over the forecast period and the actual flat sales growth observed since 2011, indicating a potential gap between projected and realized energy consumption trends.
Document: 295519 (P-194) Date Filed: June 9/22 UARB (NSPI) Pg. 9 of 10 1 Request IR-33: 2 With reference to Section 7.2 Medium Industrial, page 72 of 98, the application indicates that the 3 load in this class has been flat since 2014 and...
AI summary The UARB requests detailed explanations from NS Power regarding load growth projections, peak demand data, energy efficiency assumptions, and factors driving post-2022 energy requirement growth. Requests focus on economic activity drivers, load forecast discrepancies, energy efficiency intensity values, and unaccounted growth factors.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 1 of 16 1 Questions regarding the NSPI “2022 Load Forecast Report” of April 29, 2022 2 Request IR-1: 3 Report Tables 4 a. Please provide in electronic spreadsheet format all t...
AI summary The Board requests detailed data and clarifications on NSPI's 2022 Load Forecast Report, including spreadsheet formats for tables, historical sales data, and explanations of billed vs. accrued sales differences. The focus is on data transparency and methodology validation for load forecasting.
improve the fit.” Please quantify the nature of the improved 28 fit. 29 c. Please identify the effect if the industrial forecast used the same period as the residential 30 and commercial forecasts (that is, January 2012 to December 2021)....
AI summary The document contains requests for detailed weather data (temperature, HDD/CDD) and methodological transparency related to forecasting, including spreadsheet formats, calculation details, and climate resource citations. It seeks clarification on data sources, time periods, and forecasting approaches for energy demand modeling.
Regarding the weighing of weather station data and the comparisons between the two 15 approaches which is presented as the Mean Absolute Percentage Error (MAPE)? Were 16 other comparison approaches investigated? Why was the MAPE method sel...
AI summary The document contains requests for information regarding economic modeling methods, data sources, and assumptions used in forecasts, part of a regulatory proceeding involving Synapse (NSPI). Questions focus on forecasting methodologies, variable construction, inflation adjustments, and scenario selection.
he 2 regression? 3 i. Please provide the inflation adjustments used to convert to constant dollars. 4 j. Regarding economic forecasts, identify the scenario/case used for this analysis and the 5 reasons for choosing it. 6 k. Please indicat...
AI summary The regulatory body is requesting detailed information on inflation adjustments, economic forecasts, data sources, and customer heating trends from Nova Scotia Power. They seek specific data on residential and commercial end-use intensities, adjustments made to align with billing data, and forecasts related to heating and heat pump usage.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 7 of 16 1 d. Please explain in more detail the reasoning behind the continued work-from-home impacts 2 at 2023 levels. 3 e. The report cites increased electric heating load as...
AI summary The document contains regulatory requests for detailed explanations on work-from-home impacts, electric heating load growth, building efficiency regulations, and commercial sector energy usage forecasts. Questions focus on quantifying load changes, calculation methodologies, and potential regulatory impacts on energy efficiency metrics.
current forecast (Figure 42) remains fairly level through 2032 24 whereas the previous one showed a decline. 25 26 Request IR-19: 27 Small General Service (Section 6.1). 28 d. Please explain and quantify the specific reasons for the differ...
AI summary The document outlines requests (IR-19 to IR-24) seeking explanations and quantifications for discrepancies between current and 2021 sales forecasts across service classes (residential, industrial) and factors influencing forecast trends, such as hospital redevelopment and efficiency improvements.
r the differences from the 2021 22 forecast. 23 b. Please explain and quantify the factors behind the 0.1 percent growth rate. 24 25 Request IR-24: 26 Other Industrial (Section 7.3). 27 a. Please explain and quantify the specific reasons f...
AI summary The document outlines regulatory requests (IR-24 to IR-27) seeking explanations for forecast discrepancies, load changes, system losses, and net system requirements. It focuses on data validation, reliability of projections, and sector-specific load contributions from industrial, municipal, and other sectors.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 10 of 16 1 h. Please provide the data and calculations behind the peak share values in Figures 61 and 2 62 including the actual load values as well as the percentages. Please...
AI summary The document contains regulatory requests for data and explanations related to peak demand analysis, AMI coverage, DSM impacts, and sensitivity analysis variables. Requests include clarifying peak share calculations, AMI coverage levels, loss levels, DSM differences, and the rationale for selected sensitivity analysis variables.
ther and economic variables were 28 selected for the sensitivity analysis. 29 c. Please provide the actual statistical distributions used for those variables and the how they 30 were determined. 31 d. What other variables were considered b...
AI summary The text requests clarification on variables used in a sensitivity analysis, including their statistical distributions, excluded variables, and a correction to a figure reference in the 2020 IRP Comparison. It highlights procedural inquiries about methodology and documentation accuracy.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 11 of 16 1 b. For overall consistency with the forecast period please provide values for 2032 in 2 Figure 68. 3 4 Request IR-32: 5 Appendix A: Forecast 6 a. Please provide in...
AI summary The document contains requests for detailed data and explanations from NSPI regarding their forecast, residential model, and small general service model parameters, including historical changes, statistical models, and collaboration with E1.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 12 of 16 1 c. Please provide in electronic spreadsheet format the calculations used to produce the 2 commercial sales forecast. 3 d. For the XHeat variable please provide the...
AI summary The document contains a series of requests for detailed data and explanations related to various models and forecasts, including commercial sales forecasts, statistical models, and factors driving changes in variables like XHeat and XCool. These requests aim to ensure transparency and thoroughness in the Integrated Resource Plan (IRP) and other forecasting methodologies.
m driver for this forecast, 27 whether any other variables were considered, and why they were rejected. 28 29 Request IR-37: 30 Appendix B: Medium Industrial Model (pp 22-24) 31 a. Please provide in electronic spreadsheet format the data a...
AI summary Request IR-37 seeks data and model parameters for the Medium Industrial Model in Appendix B, including an explanation of why Manufacturing Employment was selected as the long-term forecast driver and why alternative variables were rejected.
33 b. Please explain why Manufacturing Employment was chosen as the long-term driver for 34 this forecast, whether any other variables were considered, and why they were rejected.
AI summary The text requests an explanation for selecting 'Manufacturing Employment' as the long-term forecast driver, inquiring if other variables were considered and why they were rejected. It focuses on forecasting methodology and variable selection criteria in regulatory analysis.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 13 of 16 1 Request IR-38: 2 Appendix B: Combined Model for Commercial and Industrial DSM Coefficient (pp 25-26) 3 a. Please provide in electronic spreadsheet format the data a...
AI summary The document contains regulatory requests (IR-38 to IR-41) directed at Synapse (NSPI), seeking detailed data, methodological explanations, and justifications for models related to demand-side management (DSM) coefficients, peak forecasts, and forecast accuracy. Requests focus on transparency in statistical models, variable selection, and data normalization.
dix D: Forecast Sensitivity Analysis 29 a. Please provide the model outputs in electronic form. 30 b. Please provide the full set of Oracle input data in electronic format so that it can be 31 replicated. 32 c. Please provide the historica...
AI summary The document requests detailed data and model outputs for a forecast sensitivity analysis, including electronic copies of model outputs, Oracle input data, historical data for Monte Carlo analysis, and source data for Figure D8. The emphasis is on transparency and replicability of forecasting methodologies.
forecasts used to develop the probability 33 distributions for each variable used in the Monte Carlo analysis. 34 d. Please provide the source data and calculations for Figure D8. 35
AI summary The text requests source data and calculations for Figure D8, which involves forecasts used to develop probability distributions for variables in a Monte Carlo analysis. The focus is on transparency and verification of the analytical methodology employed.
Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 14 of 16 1 Request IR-42: 2 Nova Scotia Power Electrification Support Overview by E3 3 a. Please provide the full report produced by E3 on this issue. 4 b. Please provide any...
AI summary The document includes two requests. IR-42 seeks E3's report on electrification support, while IR-43 mandates NS Power to improve load forecasting by evaluating peak conditions, enhancing weather normalization, and including system peak forecast accuracy as per the October 27, 2021 Board decision.
d firm peak presented in the Load Forecast Report. NS Power is directed to 22 incorporate this into future load forecast reports. 23 iv) The CA stated that it is more common to use load-weighted averages of temperatures from 24 multiple we...
AI summary The Board directed NS Power to incorporate more weather station data using a load-weighted approach in future load forecasts, despite NS Power's concerns about increased complexity. The CA recommended this method, and NS Power agreed but noted potential accuracy issues.
1 M10569 2 3 NOVA SCOTIA UTILITY AND REVIEW BOARD 4 5 IN THE MATTER OF: The Public Utilities Act, R.S.N.S. 1989, c.380, as amended 6 7 - and - 8 9 IN THE MATTER OF: 2022 Load Forecast Report 10 11 12 13 NON-CONFIDENTIAL INFORMATION REQUEST...
AI summary NS SBA requests non-confidential information from Nova Scotia Power Inc. regarding the 2022 Load Forecast Report under the Public Utilities Act. Responses are due July 8, 2022, with contact details provided for E.A. Nelson Blackburn, Q.C.
Page 1 of 5 1 Request IR-1: Please provide all inputs and outputs to the customer count models for all classes 2 relying upon such a model in excel format like those provided in Attachments 5-9. 3 4 Request IR-2: Please refer to page 52 of...
AI summary The document contains five requests (IR-1 to IR-5) seeking data on customer count models, electrification program targets, load forecasting assumptions, system peak growth rationale, and EV adoption forecasts. Requests focus on transparency around electrification strategies, program incentives, and load modeling methodologies.
for in the Commercial classes. 25 26 Request IR-15: Please describe in detail how electrification is accounted for in the forecasts for 27 Industrial classes. 28 29 Request IR-16: Please refer to Page 18, Lines 13-16. Why does residential...
AI summary The text contains three requests (IR-15 to IR-17) questioning how electrification is modeled in industrial forecasts, discrepancies in historical data timelines across rate classes, and inconsistencies between weather data periods and peak demand forecasting timelines. The focus is on forecasting methodology and data alignment.
e peak demand forecast not use the same time frames as the load forecast? 38 Does having different time frames impact the accuracy of the peak forecast relative to the 39 load forecast? 40
AI summary The text raises two questions about forecasting methodologies: whether peak demand forecasts use the same time frames as load forecasts, and whether differing time frames affect the accuracy of peak forecasts relative to load forecasts.
M10569 – SBA IRs – June 16, 2022 Page 4 of 5 1 Request IR-18: Understanding these are proprietary models, please provide a detailed 2 description of the methodology used in E3’s RESHAPE and EV Load Shaping Tool models 3 to determine load s...
AI summary The document includes requests for detailed methodologies of E3's EV load shaping models and questions about NS Power's EV forecast, including validation against past technology adoption and adjustments made to the forecast.
ogy that has reached broader market 21 penetration/saturation at this time? 22 c) Please describe in detail any adjustments NS Power made to the forecast in Figure 27 23 before using it as a driver in its forecast model. M10569 – SBA IRs –...
AI summary The text includes questions about market penetration/saturation and adjustments NS Power made to a forecast model. It references matter number M10569 from a regulatory proceeding, focusing on forecasting methodology and demand-side management considerations.
1 M#10569 2 3 NOVA SCOTIA UTILITY AND REVIEW BOARD 4 5 6 IN THE MATTER OF: The Public Utilities Act 7 8 – and – 9 10 IN THE MATTER OF: NOVA SCOTIA POWER INCORPORATED - 2022 Load Forecast 11 Report 12 13 14 15 16 17 18 19 INFORMATION REQUES...
AI summary The Nova Scotia Utility and Review Board is handling a proceeding under the Public Utilities Act, involving Nova Scotia Power Incorporated's 2022 Load Forecast Report. The Consumer Advocate has requested information from Nova Scotia Power, with responses due by July 8, 2022, focusing on regulatory compliance and forecasting methodology.
Date Filed: June 16, 2022 CA (NSPI) Page 1 of 11 1 Request IR-1: 2 3 Reference Report p. 7: “As with any forecast, there is a degree of uncertainty around actual future 4 outcomes. In electricity forecasting, much of this uncertainty is du...
AI summary The document contains regulatory requests (IR-1 and IR-2) questioning NS Power's forecasting methodology, specifically regarding uncertainty analysis, inclusion of warming trends in load forecasts, and clarification of 2021 load forecast timelines. Requests focus on scenario analysis, sensitivity to climate factors, and transparency in energy load projections.
ime, this forecast covers the period of 2022-2032.” 28 29 a. Please clarify when the 2021 load forecasts were finalized. 30 31 b. For the major sources of information (economic inputs, load history, etc.) please clarify 32 the date of the...
AI summary The text contains requests for clarification on load forecasts (2021-2032), data sources used (economic inputs, load history), and geographic load data disaggregation. It seeks details on the Conference Board of Canada's economic data and NS Power's geographic load data availability.
d event, to determine whether charging demand may be different during peak load 27 events. 28 29 Request IR-5: 30 31 Reference Report pp. 57-58 regarding the DSM adjustments, please provide workpapers in 32 electronic format with working f...
AI summary The text includes two information requests related to demand-side management (DSM) adjustments and a 2022 load forecast report. It asks for detailed workpapers on DSM coefficient calculations and seeks confirmation of data relationships in the residential load forecast table.
at NS Power is aware of suggesting that 31 the higher number of new customers may relate to pandemic-driven migration patterns. 32 Please also elaborate on related discussion found on p. 61. 33 34 b. Does NS Power have reasons to expect th...
AI summary The text includes questions directed at NS Power regarding its forecasting models for residential and commercial customers, including factors like pandemic-driven migration, new construction efficiency, and population growth impacts. It requests explanations on how these variables are incorporated into the models.
residential property. 40 41 d. Please explain how the forecast increase in population and customers is reflected in the 42 commercial (General Service) models. 43 44 45 46
AI summary The text raises a question about how projected population and customer growth are incorporated into commercial (General Service) models, though no specific details or analysis are provided in the excerpt.
Date Filed: June 16, 2022 CA (NSPI) Page 4 of 11 1 Request IR-9: 2 3 Reference Report p. 59: “The COVID-19 impact to the Residential forecast is approximately +100 4 GWh in 2022 …” 5 6 a. Does “impact” refer to an estimated change in deman...
AI summary The document contains three regulatory requests (IR-9, IR-10, IR-11) querying NS Power about demand forecasting impacts from COVID-19, economic forecast sources, and load model updates. Requests focus on clarifying methodology, data sources, and confidence in projected demand growth.
nputs and any other inputs that can be updated? If so, provide the 34 updated forecasts. 35 36 Request IR-11: 37 38 With reference to the Report pp. 73-74: “One customer makes up over [redacted] of sales within 39 this class, and after a d...
AI summary The text requests updated forecasts and specific sales data for a customer whose activity significantly impacts a sales class. It references a report noting the customer's sales recovery in 2021 after declines in 2019-2020, and seeks 2021 sales additions and 2022 YoY comparisons for January-May.
Date Filed: June 16, 2022 CA (NSPI) Page 5 of 11 1 Request IR-12: 2 3 Reference report p. 83 regarding the January 2022 peak, please provide monthly energy and peak 4 demand data for January – May 2022 in spreadsheet format. 5 6 Request IR...
AI summary The text includes two requests: IR-12 seeks monthly energy and peak demand data from January–May 2022, while IR-13 challenges NS Power’s reliance on temperature as the sole weather factor in load forecasting, requesting supporting analyses and data on wind, precipitation, cloud cover, and snow variables from Environment Canada.
he demand 45 change of 25 MW/°C, the 2021 actual system firm peak of 1,875 MW is weather normalized to 46 2,002 MW, which is 71 MW lower than the 2021 forecast system firm peak of 2,073 MW. This
AI summary The text discusses weather normalization of system firm peak demand, showing the 2021 actual peak of 1,875 MW normalized to 2,002 MW, which is 71 MW lower than the 2021 forecast of 2,073 MW. This highlights a discrepancy between actual and projected demand under different temperature conditions.
er plans to complete the Board directed 30 updates to the load forecast report; for each task, please provide (i) the estimated time 31 to complete the task, (ii) identify any prerequisite tasks, and (iii) identify whether the 32 task is n...
AI summary The Board directed updates to the load forecast report, requesting task timelines, prerequisites, and necessity. Request IR-18 asks to confirm a temperature trend in the forecast and explain its reflection in Table A1, including HDD and CDD values by year.
Date Filed: June 16, 2022 CA (NSPI) Page 8 of 11 1 c. If the trend in HDD and CDD values by year was not applied to the energy requirement 2 forecast in Table A1, please provide the forecast also including the trend in HDD and 3 CDD values...
AI summary The document contains a series of questions requesting clarification on the application of heating and cooling degree day (HDD/CDD) trends to energy forecasts, reconciliation with temperature data in tables, and the impact of temperature trends on DSM adjustments. It also questions the methodology for incorporating annual evening minimum temperatures into peak demand forecasts.
ii. If the annual evening minimum temperature is not used in the peak forecast, 27 please explain why not and the significance of the discussion of this metric in 28 the Report. 29 30 iii. Please provide workpapers demonstrating all steps...
AI summary The text includes requests for clarification on peak forecast methodology, specifically the use of annual evening minimum temperature, workpapers for forecasting steps, and explanations regarding the LRS peak model's non-adoption by NS Power. It also asks for data on other customer classes and analysis of LRS model applicability.
dentify the pros and cons of adopting the LRS peak model 43 immediately. 44 45 c. Please identify each aspect of the load forecast in which the LRS data could be applied. 46
AI summary The text requests an analysis of the pros and cons of immediately adopting the LRS peak model and asks to identify aspects of the load forecast where LRS data could be applied, focusing on forecasting methodology and data application.
dders in each of 31 January, August, September and October. 32 33 b. Has NS Power identified any drivers (e.g., time spent at home) that would account for 34 any of these variations from the general function of heating, cooling, other appl...
AI summary The text contains questions about NS Power's load forecasts, including discrepancies between past predictions (declining/flat trends) and a new forecast predicting increasing energy demand. It also asks about factors driving variations in energy use patterns across specific months.
EfficiencyOne Information Requests to Nova Scotia Power, Inc. (NS Power) In the Matter of Nova Scotia Power Incorporated’s 2022 Load Forecast Report (M10569) IN THE MATTER OF: THE PUBLIC UTILITIES ACT - and - IN THE MATTER OF: Nova Scotia...
AI summary EfficiencyOne has submitted non-confidential information requests to Nova Scotia Power Inc. (NS Power) regarding its 2022 Load Forecast Report under the Public Utilities Act. Responses are due by July 8, 2022, with details provided for contact and submission.
for the fact that the full nameplate capacity will not be available at all times, and this value will 24 be reassessed as more information is gathered through the implementation of DR programs”. Date Filed: June 16, 2022 E1 (NS Power) Page...
AI summary EfficiencyOne (E1) submitted information requests to Nova Scotia Power Inc. (NS Power) regarding the 2022 Load Forecast Report (M10569). The text notes that full nameplate capacity may not always be available and will be reassessed as data from demand-response (DR) programs is gathered.
n Requests to Nova Scotia Power Inc. (NS Power) In the Matter of Nova Scotia Power Incorporated’s 2022 Load Forecast Report – M10569 NON-CONFIDENTIAL
AI summary This document pertains to requests submitted to Nova Scotia Power Inc. (NS Power) regarding their 2022 Load Forecast Report, referenced as Matter M10569. The proceeding involves a non-confidential review of the report, which outlines NS Power's projected electricity demand for 2022.
ion Requests to Nova Scotia Power Inc. (NS Power) In the Matter of Nova Scotia Power Incorporated’s 2022 Load Forecast Report – M10569 NON-CONFIDENTIAL
AI summary The document pertains to a regulatory proceeding involving Nova Scotia Power Inc.'s 2022 Load Forecast Report, identified by matter number M10569. The text is labeled as non-confidential and outlines the context of the proceeding.
3, lines 12-13 24 “The peak impact assumes that 70 percent of charging is managed by NS Power (including 25 smoothing through vehicle-grid integration) and 30 percent of charging is unmanaged.” Date Filed: June 16, 2022 E1 (NS Power) Page...
AI summary EfficiencyOne (E1) has requested information from Nova Scotia Power Inc. (NS Power) regarding their 2022 Load Forecast Report. The report includes an assumption that 70% of electric vehicle charging will be managed by NS Power through vehicle-grid integration, while 30% will be unmanaged.
Blackburn Law July 29, 2022 VIA EMAIL Ms. Crystal Henwood Regulatory Affairs Officer/Clerk Nova Scotia Utility and Review Board 1601 Lower Water Street, 3rd Floor Halifax NS B3J 3S3 Dear Ms. Henwood: Re: M10569 - Nova Scotia Power Inc. ("N...
AI summary The Small Business Advocate (SBA) acknowledges improvements in NSPI's 2022 Load Forecast Report, including updated peak temperature assumptions and EV forecasts, but notes that electrification changes may inaccurately signal system growth. The SBA also questions how data from the Smart Grid Nova Scotia Project is incorporated into the forecast.
ustomer load increased via electrification is appropriate to include in NSPI planning, there is little certainty in this area. As an illustration of the magnitude of the electrification activity that 2 Ml0109, Document.Number 84757, Decisi...
AI summary The SBA highlights uncertainty in NSPI's planning for electrification load increases, noting that transportation electrification growth could offset DSM plan savings. It recommends clarifying SGNS data's role in load forecasting and evaluating incentive structures' impact on EV adoption. Using smoothed adoption rates may lead to insufficient incentives for electrification.
M10569 - 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) NSPI Information Requests to EfficiencyOne NON-CONFIDENTIAL
AI summary Matter M10569 involves a 10-year energy and demand forecast (2022 Load Forecast Report) and NSPI's information requests to EfficiencyOne. The document is marked non-confidential, indicating it may be publicly accessible.
October 31, 2022 [email protected] Jennifer Ross Manager, Regulatory Strategy Nova Scotia Power Inc. 1223 Lower Water Street Halifax, NS B3J 3S8 Dear Ms. Ross: M10569 – Nova Scotia Power Inc. – 2022 Load Forecast Report – (P-194) On...
AI summary Nova Scotia Power Inc. submitted its 2022 Load Forecast Report, triggering a regulatory proceeding with interventions from advocates, industry groups, and EfficiencyOne. Synapse Energy Economics reviewed the filing, and evidence was exchanged through August–September 2022, with NS Power submitting rebuttal evidence in September.
ce was filed by the CA, EOne, and Synapse on July 29, 2022. On August 12, 2022, EOne responded to IRs from the NS Power. NS Power filed its Rebuttal Evidence on September 26 ,2022. 2022 Load Forecast NS Power uses two discrete elements for...
AI summary The 2022 Load Forecast by NS Power incorporates SAE models and DSM adjustments, projecting 0.3% annual NSR growth (2023-2032) due to customer growth, EV adoption, and infrastructure projects, offset by DSM and solar. Filing timeline includes CA, EOne, Synapse, and NS Power submissions.
forecasted that NSR will experience an average annual increase of 0.3 percent between 2023 and 2032. With respect to system peak, NS Power forecasts annual growth of 1.6 percent between 2023 and 2032. These forecasts result in a projected...
AI summary NS Power forecasts 0.3% annual NSR growth and 1.6% system peak growth between 2023-2032, projecting 5.7% higher NSR and 28.7% higher peak demand by 2032 compared to 2021. 2022 forecasts show 2.2% NSR growth and 10% peak demand growth, contrasting with 2021 actuals of 1.7% NSR and -4.0% peak demand.
-0.6% 2018 0.8% 3.5% 6.0% 2.7% 2017 1.0% 0.6% -0.03% -4.4% The load forecast is a foundational input in relation to NS Power’s overall planning, budgeting, and operating activities, including generation requirements, capital program, fuel...
AI summary The document highlights the critical role of load forecasting in NS Power’s planning and the Board’s concerns over its accuracy. Revisions to the 2022 Load Forecast incorporated warming trends and EV modeling, while 2021 variances were attributed to weather and the pandemic. The Board’s 2021 decision (M10109) emphasized stakeholder engagement and forecast improvements.
ear actual 2 Based on Table A2 in 2022 and 2021 Load Forecast reports and in Table A3 in each annual report from 2015 to 2020 Document: 298717 -3- However, the significant increase in Load for 2022 is mainly attributed to the application o...
AI summary NS Power attributes the 2022 load increase to E3's estimated peak model, planning to validate it using AMI data. Intervenors praised forecast improvements but raised concerns about heat pump adoption, EV forecasts, new technologies, and SGNS project outcomes for peak demand management.
me framework to assessing the value of different DR programs and an effective load carrying capacity (ELCC) factor be applied. Document: 298717 -4- In its evidence to the Board, Synapse noted continued improvement in the Load Forecast, how...
AI summary Synapse's evidence highlights recommendations for improving NS Power's load forecasting, including re-evaluating economic variables, assessing heat pump impacts, updating EV and storage analyses, and clarifying DSM adjustments. It also requests methodological reviews and sensitivity analyses for peak load forecasting.
mpacts on peak. Synapse asked that NS Power review its peak forecasting methodology and provide a peak sensitivity analysis as well as the assumptions and calculations used. NS Power Reply Submission In its Reply, NS Power addressed the co...
AI summary NS Power agreed to improve peak load forecasting by incorporating multi-station weather data, analyzing electrification impacts, and refining EV adoption rate assumptions. It will also evaluate hybrid DR scenarios and integrate findings from the Itron Report and SGNS project into future reports.
adjustment to EV load shapes. A request for the information on the benefits and costs of commercial electrification programs was denied as NS Power has not performed such an analysis. Board Findings The Board notes that NS Power has agreed...
AI summary The Board denied a request for information on commercial electrification benefits and costs due to NS Power's lack of analysis. It directed NS Power to implement intervenor recommendations, improve the Load Forecast model, evaluate economic inputs, and engage stakeholders early in the 2023 Load Forecast process.
he elasticity used in the SAE model to exclude elasticities calculated from non- winter peaking utilities or to apply elasticities from Canadian studies of Canadian electric utilities; - Evaluate the input variables in the residential mode...
AI summary The document outlines requests to refine modeling approaches for residential demand forecasting, including re-evaluating housing data, income metrics, and EV adoption rates. It emphasizes improving model accuracy through updated inputs, aligning with Statistics Canada data, and addressing infrastructure communication gaps.
Disclaimer: These summaries were generated by AI from the filings they describe. We take care to make them accurate, but errors are possible - and they aren't advice. Only the filings themselves are the record: if you're relying on something here, confirm it against the source documents or the Nova Scotia Energy Board's own record. Full disclaimer →