Topic:"Energy Efficiency Resource Assessment Model" in M10569

NFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 10: Peak Period Minimum Temperatures 2 3 4 5 The other factor included in the peak forecast temperature is a trend similar to the one 6 included in the annual HDD calculations...

AI summary The 2022 Load Forecast Report discusses trends in peak period minimum temperatures and heating degree days (HDD), noting an increase in minimum temperatures at a rate of 0.15 degrees per year. It references climate change scenarios and research from New England to support these projections.

in the forecast. 12 Research on trends in New England8 points to general warming patterns both in terms of 13 average temperatures as well as minimum and maximum temperatures. 14 7 https://climateatlas.ca/map/canada 8 Overall Warming with...

AI summary The document references research on warming trends in New England, highlighting general increases in average, minimum, and maximum temperatures from 1900 to 2020. This information is presented in the context of a 2022 Load Forecast Report, though much of the content is redacted.

Page 25 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 The weather in 2021 was noteworthy as it had the lowest number of HDD in over 40 years 2 (3484, or 8 percent less than the 10 year average), as...

AI summary The 2022 Load Forecast Report discusses the impact of weather on electricity demand, noting that 2021 had significantly lower HDD than the 10-year average. The report also addresses the shift from using a load-weighted approach to a population-weighted approach for incorporating weather station data into load forecasts.

2022 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 In the commercial models, non-manufacturing gross domestic product (GDP) and non- 6 manufacturing employment continue t...

AI summary The 2022 Load Forecast Report discusses the use of economic drivers in forecasting load demand, including GDP and employment data for residential, commercial, and industrial sectors. The industrial models use longer regression timescales to improve the relevance of economic variables and model fit.

.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.

Page 34 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document presents the 2022 Load Forecast Report, which includes confidential information that has been redacted. The report likely outlines projected electricity demand for the year 2022, though specific details are not visible due to redaction.

1 4.4 End-Use Intensity Trends 2 3 In addition to economic data, the SAE model also uses end-use data, in the form of 4 saturations and efficiencies, from NRCan and the US Energy Information Agency (EIA). 5 NRCan data for the residential s...

AI summary The SAE model uses end-use data from NRCan and the EIA to develop end-use intensity trends, adjusting for consistency with actual billing data. Heat pump usage is growing due to its efficiency and environmental benefits.

may not be able to fully convert. 16 DATE: April 29, 2022 Page 36 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 21: E3 Commercial Space Heating Saturation 2 3 4 E3 saturation estimates had a...

AI summary The document discusses the comparison of commercial space heating saturation estimates between E3 and NS Power models, noting that E3 estimates started lower and NS Power models were adjusted to align with E3 by around 2040, resulting in a slightly lower trajectory.

OVED) 2022 Load Forecast Report REDACTED 1 Figure 22: Commercial Space Heating Saturation Comparison 2 3 4 5 E3 also estimates peak impacts associated with increased electric space heating in both the 6 residential and commercial sectors....

AI summary E3 estimates peak impacts from increased electric space heating in residential and commercial sectors, noting that the SAE model underestimates peak demand compared to E3's building stock model. Adjustments are made to account for this discrepancy, with 25% and 20% already captured in residential and commercial classes, respectively.

Page 41 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 used plug-in hybrid vehicles. It is estimated that there were approximately 950 EVs in the 2 province as of the end of 2021. The EV forecast has...

AI summary The 2022 Load Forecast Report estimates that Nova Scotia will have over 75,000 EVs on the road by 2031, driven by provincial and federal targets. This includes light-duty, medium-duty, and heavy-duty vehicles, with the forecast updated to reflect a 30% EV sales target by 2030 and a 100% target by 2035.

e 42 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 charging. E3 provided estimates of total load and normalized per-vehicle electric vehicle 2 load shapes over the course of the year based on E3’s E...

AI summary The 2022 Load Forecast Report discusses E3's estimation of total load and normalized per-vehicle electric vehicle load shapes in Nova Scotia using a bottom-up modeling approach. The report includes a figure showing EV load and peak demand contribution based on these estimates.

ure 27 shows the EV load 7 and peak demand contribution estimated from the load shapes provided by E3: 8 9 Figure 27: E3 EV Mileage Assumptions and Load/Peak Modeling Results 10 Vehicle Avg Avg kW/vehicle Avg kWh/year Type km/year12 on Pea...

AI summary The text presents data on electric vehicle (EV) load and peak demand contributions estimated by E3, based on average mileage and energy consumption assumptions. It highlights the impact of managed versus unmanaged EV charging on peak demand, noting that managed charging reduces the average peak contribution compared to entirely unmanaged scenarios.

urrently in the pilot stage, 12 Based on the NRCan 2009 Canadian Vehicle Survey Summary Report, https://oee.nrcan.gc.ca/publications/statistics/cvs/2009/appendix-1.cfm?graph=11 DATE: April 29, 2022 Page 43 of 98 REDACTED (CONFIDENTIAL INFO...

AI summary The document discusses the impact of electric vehicles (EVs) on Nova Scotia's grid, noting that data is being collected through the Smart Grid Nova Scotia (SGNS) Project. The project includes the installation of EV smart chargers and vehicle-to-grid smart chargers to study the effects of EV charging and utility-managed charging events.

thout mitigation measures, which 16 assumes an average of 1.3 kW/vehicle, or around 20-30 percent charging on peak 17 depending on the mix of vehicle and charger types. 18 13 Smart Grid Nova Scotia Project, NS Power Application, December 5...

AI summary The text discusses the impact of electric vehicles (EVs) on energy and peak load forecasts, assuming an average of 1.3 kW per vehicle and 20-30% charging during peak hours, depending on vehicle and charger types. It references a 2019 application by NS Power related to the Smart Grid Nova Scotia Project.

stallations, while 12 the actual number was 1,628, for a cumulative total of 4,022 (33 MW) by the end of the 13 year15. As of 2021, the average installed capacity is approximately 8.3 kW for residential 14 customers and 33 kW for non-resid...

AI summary The text discusses the installation of solar systems in Nova Scotia, noting that the actual number of installations by the end of the year was 1,628, with a cumulative total of 4,022 (33 MW). It also provides data on average installed capacity and estimated annual net metering solar generation.

INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 29: PV Impact to Energy (cumulative) 2

AI summary The document contains a redacted section from the 2022 Load Forecast Report, specifically Figure 29, which discusses the cumulative impact of photovoltaic (PV) systems on energy.

Page 48 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The 2022 Load Forecast Report provides an analysis of projected electricity demand, incorporating factors such as weather patterns, economic trends, and energy efficiency initiatives. The report includes redacted information and is part of a regulatory proceeding.

1 Battery peak estimates are based on the 5 kW batteries utilized in the SGNS project. 2 3 The impacts of technologies related to direct load control (DLC) of heating and hot water 4 loads are discussed in Section 10. 5 6 Intensities 7 8 F...

AI summary The text discusses battery peak estimates from the SGNS project and outlines residential end-use intensities, including categories like heating, cooling, and lighting. It also mentions the modeling of PV and EV outside of regression analysis and references sections and appendices for further details.

of 2 electricity while providing benefits to the system (such as through the interruptible rider). 3 4 Figure 34: Commercial and Industrial Electrification Forecasts (cumulative) 5 Coinc. SmGen GenDemand LrgGen SmInd MedInd LrgInd Year Pea...

AI summary The text presents a table showing forecasts for commercial and industrial electrification from 2022 to 2032, with data on electricity generation and demand across different sectors. The data includes cumulative values for various categories such as small generation, generation demand, and large industrial consumption.

e 55 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The 2022 Load Forecast Report provides an analysis of electricity demand trends, incorporating factors such as heating and cooling degree days, and outlines the projected load requirements for the year.

Page 58 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 5.0 RESIDENTIAL SECTOR 2 3 The Residential sales forecast is generated as the product of a residential average use 4 forecast and a customer cou...

AI summary The residential sector's load forecast is based on average use and customer count projections. Growth in residential sales between 2019 and 2020 was influenced by increased home working due to COVID-19, while 2021 saw impacts from warm weather and ongoing pandemic effects. Figure 37 compares forecast and actual sales data for 2019, 2020, 2021, and 2022.

1 6.0 COMMERICAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General 4 rate classes. Like the residential model, the commercial SAE models express monthly sales 5 as a function of heating, coolin...

AI summary The Commercial SAE model forecasts energy use for the Small General and General rate classes in Nova Scotia, factoring in heating, cooling, and other loads. The model uses end-use intensity projections, GDP, employment, and weather data. The impact of the pandemic on commercial sales was moderate, with a rebound expected in 2022.

quirements) on many commercial sectors including 23 retail and restaurants. Schools have largely remained open since the fall of 2020, except 24 for several universities that chose to deliver classes online for the 2020/2021 school year. 2...

AI summary The document discusses the impact of the COVID-19 pandemic on commercial energy sales in Nova Scotia, noting a significant drop in 2020 and 2021, followed by an expected rebound in 2022. It also compares commercial energy sales to GDP and employment indicators, showing a similar decline in all three metrics during the pandemic.

Page 74 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document presents the 2022 Load Forecast Report, which includes confidential information that has been redacted. The report likely discusses electricity demand projections for the year 2022.

FIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 67: System Peak Sensitivity 2 3 4 This analysis provides a potential range of outcomes for the 2022 Load Forecast. Energy 5 is most sensitive to Economics over the...

AI summary The 2022 Load Forecast Report discusses the sensitivity of energy and peak demand to factors such as economics and temperature. It compares the 2021 and 2022 Load Forecasts with the 2020 IRP cases, noting similarities in outcomes despite new analyses on space heating and EV adoption. The report also mentions stakeholder discussions regarding assumptions about incentives affecting EV and heat pump uptake.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 the values represent a slow ramp-up of uptake of these technologies that would allow 2 interim sales targets as well as the target of net zero emissions by 20...

AI summary The 2022 Load Forecast Report outlines projections for energy demand, considering factors such as the uptake of electric vehicles (EVs) and electrification scenarios. The report reflects updated federal EV sales targets and discusses potential changes in policy and regulation over the next decade.

IRP Mid Electrification 11,332 2,085 11,363 2,375 IRP Low Electrification 11,302 2,078 10,809 2,094 12 27 Peak temperature of -13.7 compared to -15 for the other forecasts. DATE: April 29, 2022 Page 98 of 98 REDACTED (CONFIDENTIAL INFORMAT...

AI summary The text includes a table with forecast values for energy requirements and load forecasts, including different electrification scenarios and temperature data. It references a 2022 Load Forecast Report and an appendix with forecast values from NS Power.

Appendix A – Forecast Values 1.1 Table A1: Energy Requirement – 2021 NS Power Forecast Energy Forecast

AI summary This section presents Appendix A, which includes Table A1 from NS Power's 2021 energy forecast. The table outlines forecasted energy requirements, providing data relevant to future planning and resource allocation.

Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix B Page 1 of 32 Appendix B – Forecast Model Details 2022 NS Power Load Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report...

AI summary The residential average use SAE model incorporates heating, cooling, and other end uses, factoring in variables such as HDD, CDD, household income, and price. It also includes a term for DSM savings and an ARMA component for modeling time-series data.

2022 Load Forecast Report Appendix B Page 9 of 32 Appendix B – Forecast Model Details Commercial Model Detail Small General Service Small General Service is projected using an SAE average use model and a sales forecast is generated as the...

AI summary The document details the Small General Service load forecast model used in the 2022 Load Forecast Report. It uses an SAE average use model with variables for heating, cooling, and other uses, incorporating price elasticity, GDP, employment, and monthly HDD and CDD data. Adjustments were made for rate class changes, model fit, and pandemic-related billing issues, with an ARMA process added to improve accuracy.

monthly forecast average use sales model is then estimated as: SmlGen_AvgUsem = b1× XHeatm + b2× XCoolm + b3× XOtherm + MBin.Mar + MBin.Sep + MBin.Dec + MBin.Aft13 + MBin.May20 + MBin.Jun20 + SMA(1) Page 8 of 31 REDACTED (CONFIDENTIAL INFO...

AI summary The text presents a statistical model for estimating monthly forecast average use sales, incorporating variables such as heating, cooling, and other use factors, along with binary indicators for specific months and a seasonal moving average. The model includes coefficients, standard errors, t-statistics, and p-values for each variable.

Report Appendix B Page 12 of 32 Appendix B – Forecast Model Details Small General Model Fit Small General 2022-2032 Reconciliation The Small General Demand customer forecast model is constructed like the residential model (including heat p...

AI summary This section of the report discusses the Small General Demand customer forecast model, which is structured similarly to the residential model and includes heat pump programs within the SAE model. Adjustments for commercial and industrial growth programs, PV, and DSM are made outside the regression. The XHeat, XCool, and XOther variables use a flat scaling factor for easier comparison of regression coefficients.

7.1% 0.3% -5.0% 0.0% -0.9% 1.5% Small General Avg Use = XHeat + XCool + XOther + Binaries + ARMA Small General Input Variables – XHeat Intensities Econ + Struct Regression Heating HeatUse Coeff Scaling Factor Total XHeat (kWh) Variable 202...

AI summary The text provides a detailed breakdown of input variables used in the small general load forecast model, specifically focusing on XHeat and XCool. It includes intensity values, economic and structural factors, and regression coefficients for the years 2022 and 2032, with a calculation example showing how growth rates are determined.

1.1% 7.0% 0.0% 0.0% -8.2% XOther = (Ventilation+ Water Heat + Cook + Refrigeration + Light + Office + Misc) x OtherUseVariable x Coeff x Scaling Factor Page 13 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report App...

AI summary The General Service rate class model estimates monthly billed sales based on heating, cooling, and other use variables, incorporating factors like end-use intensity projections, price elasticity, and seasonal adjustments. The model includes binaries for specific events and an ARMA process for seasonal moving average.

vicem = b1× XHeatm + b2× XCoolm + b3× XOtherm + MBin.Feb18 + MBin.May20 + MBin.Jun20 + SMA(1) Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.641 0.035 18.178 0.00% MStructGen.WtXCool 0.325 0.048 6.807 0.00% MStructGen.WtXO...

AI summary The text presents a statistical model equation and its coefficients, including variables related to heat, cooling, and other factors, along with their standard errors, t-statistics, and p-values. The model is part of a load forecast report and includes a seasonal moving average component.

2022 Load Forecast Report Appendix B Page 21 of 32 Appendix B – Forecast Model Details Small Industrial Model Statistics Model Statistics Iterations 1 Adjusted Observations 144 Deg. of Freedom for Error 131 R-Squared 0.858 Adjusted R-Squar...

AI summary This section provides model statistics for the Small Industrial Load Forecast model, including metrics such as R-squared, adjusted R-squared, AIC, BIC, and various error statistics. The model has 144 adjusted observations and shows a high level of fit with an R-squared of 0.858. Some statistical values such as F-statistic and Durbin-H statistic are marked as not available.

20544.320 1307.234 15.716 0.00% MBin.Aft16 -1766.069 271.183 -6.512 0.00% MEcon.ManEmp 635.788 36.151 17.587 0.00% Page 22 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix B Page 24 of 32 Appendix B – Fo...

AI summary This section provides statistical details for the Medium Industrial Model used in the 2022 Load Forecast Report. It includes metrics such as R-Squared, AIC, BIC, and various error measures, indicating the model's performance and reliability.

2 Load Forecast Report Appendix B Page 27 of 32 Appendix B – Forecast Model Details Combined Model Statistics Model Statistics Iterations 1 Adjusted Observations 120 Deg. of Freedom for Error 111 R-Squared 0.853 Adjusted R-Squared 0.842 AI...

AI summary This section presents statistical details of a load forecast model, including metrics such as R-Squared, Adjusted R-Squared, AIC, BIC, and various error measures, providing insights into the model's accuracy and reliability.

mber of days and hours in the month by expressing heating and cooling load requirements on an average MW load basis: HeatAvgMWm = HeatLoadm/ Daysm /24 CoolAvgMWm = CoolLoadm/ Daysm /24 Page 27 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOV...

AI summary The text describes a method for calculating average heating and cooling load requirements on an MW basis and how peak-day weather conditions are integrated into the forecast model. It also outlines the calculation of base load variables to account for non-weather sensitive load components.

: OtherAvgMWm = OtherLoadm/ Daysm /24 Binary mBin.Year20Plus is used to account for the impact of COVID-19 starting in 2020. Variable Coefficient StdErr T-Stat P-Value mVarsNew.Cool_Var 1.013 0.408 2.484 1.46% mVarsNew.Heat_Var 1.444 0.104...

AI summary This section presents statistical data from a load forecast model, including coefficients, standard errors, t-statistics, and p-values for various variables. The model accounts for the impact of the COVID-19 pandemic starting in 2020, and includes monthly variables for different types of load, such as cooling, heating, and other loads.

Appendix D – Forecast Sensitivity Analysis Figure D4: Peak Forecast (Residential, Commercial and Small and Medium Industrial) The asymmetry in this figure, seen as the off-centre median, is explained by the bias introduced by plotting the...

AI summary The document discusses the asymmetry in peak forecast data, attributing it to the use of the MAX function in selecting the highest monthly Peak HDD. It notes that the Monthly HDD has become more influential than Peak HDD in 2022 due to year-round residential heating impacts, leading to a steeper peak demand curve influenced by E3 electrification scenarios.

 E3 generates forecast of Trip data Charger & EV Demographics Driver Charging attributes Costs & Tariffs transportation load shape based on simulations of EV driving and charging behavior, using travel 1. EV Driving & Charging Simulation...

AI summary The text describes a process for forecasting transportation load shape based on simulations of EV driving and charging behavior using travel survey data. It includes inputs such as vehicle type, charging access, and cost, and outputs like normalized load shapes and charging session statistics.

mVarsNew.Jan_Other 1.290 0.093 13.799 0.00% mVarsNew.Feb_Other 1.236 0.101 12.266 0.00% mVarsNew.Mar_Other 1.377 0.084 16.369 0.00% mVarsNew.Apr_Other 1.570 0.047 33.165 0.00% mVarsNew.May_Other 1.543 0.035 43.547 0.00% mVarsNew.Jun_Other...

AI summary The text presents monthly data with variables (e.g., mVarsNew.Jan_Other) and model statistics (R-squared: 0.976, AIC: 7.87) related to demand-side management (DSM) savings (AnnualSavings.DSMDemSavings: 0.290). The analysis includes statistical parameters from a model assessing energy efficiency and DSM performance.

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.

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 NSPI provides data on variables affecting energy loads, including temperature, precipitation, and wind speed, in response to a request about their impact on peak loads. A graph shows an 85.6% correlation between temperature and load (R²=0.8556). NSPI classifies variables like temperature and wind speed as objective, while others (e.g., visibility) are sometimes subjective.

Year Month Day Energy HDD13 HDD0 JanHDD13 FebHDD13 MarHDD13 AprHDD13 OctHDD13 NovHDD13 DecHDD13 AugCDD18 JulCDD18 SepCDD18 JunCDD18 Sun Sat Mon Fri LagHDD13 Lag2HDD13Bad XMissing YMissing 2018 1 1 36,263.18 23.75 10.75 23.75 0 0 0 0 0 0 0...

AI summary The text presents a table with energy usage data and various heating and cooling degree day (HDD and CDD) metrics for January 2018. The data includes daily energy consumption, HDD13, HDD0, and CDD18 values, along with missing data indicators. This information may be used for energy demand analysis and forecasting.

0.897 2,844.58 634.04 0 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 1 Page 56 of 57 Variable Coefficient StdErr T-Stat P-Value Units Definition CONST 19044.52 56.624 336.331 0.00% Constant term Daily.HDD13 232....

AI summary The text presents a statistical table with variable coefficients, standard errors, T-statistics, and P-values, likely from a regression analysis. The variables include daily heating and cooling degree days (HDD and CDD) and day-of-week indicators, suggesting an analysis of energy usage patterns.

ariable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 424.273 232.686 -97.305 177.016 43.995 Summary Jan 2021 heating load 374,860 MWh Normal heating load 416,593 MWh 2021 Variance to Normal (41,732) MWh

AI summary The document provides data on heating load for January 2021, showing a variance of -41,732 MWh compared to the normal heating load of 416,593 MWh. Variable coefficients and HDD (Heating Degree Days) values are also presented.

2019 Daily.JanHDD18 367.681 31.909 11.523 0.00% Jan 1070974.494 Daily.FebHDD18 564.454 29.56 19.095 0.00% Feb 965284.2748 Daily.MarHDD18 514.938 39.382 13.075 0.00% Mar 987543.4722 Daily.AprHDD18 528.892 42.286 12.508 0.00% Apr 808646.4045...

AI summary The text provides a table with daily HDD and CDD values for various months in 2018, along with associated numerical data. The data appears to be related to heating and cooling degree days, which are commonly used in energy analysis and utility planning.

Daily.Dec 21504.333 520.882 41.284 0.00% REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 2 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 403.966 232.686 -97.305 177.016 43.995 Summary Feb 202...

AI summary The text provides heating load data for February 2021, showing a total heating load of 344,558 MWh, which is 22,790 MWh less than the normal heating load of 367,348 MWh. The data includes variable coefficients and HDD values.

9389 2 28 2.9 15.1 6096 3511 -278 3022 635 12985 29 0 0 0 1621 673 2294 2 29 3.2 15.2 6148 3541 -314 2671 751 12798 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 3 of 15 Variable Coefficients Month HDD13 H...

AI summary The document provides data on heating load for March 2021, showing a total of 282,880 MWh, which is below the normal heating load of 304,810 MWh, resulting in a variance of -21,930 MWh. It also includes variable coefficients and other statistical data.

2022 LFR CA IR-14 Attachment 2 Page 4 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 180.936 232.686 -97.305 177.016 43.995 Summary Apr 2021 heating load 120947 MWh Normal heating load 161906 MWh 2021 Varinace to Normal -409...

AI summary The text provides data on heating load for April 2021, showing a heating load of 120,947 MWh, which is below the normal heating load of 161,906 MWh, resulting in a variance of -40,959 MWh.

657 844 0 1048 85 2634 4 26 0.0 6.9 1256 1615 0 1246 293 4409 27 0.0 6.39 1156 1486 0 642 260 3545 4 27 0.0 6.9 1243 1599 0 1228 310 4380 28 0.0 1.55 280 360 0 1131 160 1930 4 28 0.0 6.9 1252 1610 0 1216 305 4384 29 0.0 4.08 737 948 0 274...

AI summary The text presents a series of numerical values and coefficients, likely related to energy modeling or forecasting. The numbers appear to represent variables such as HDD (heating degree days) and their corresponding coefficients, which may be used in calculating energy demand or other related metrics. The document is redacted, indicating that it contains confidential information.

of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 0 232.686 -97.305 177.016 43.995 Summary May 2021 heating load 50551 MWh Normal heating load 54000 MWh 2021 Varinace to Normal -3449 MWh

AI summary The document provides heating load data for May 2021, showing a total of 50,551 MWh, which is 3,449 MWh below the normal heating load of 54,000 MWh. The variance is attributed to factors such as HDD (Heating Degree Days) and lagged variables.

2022 LFR CA IR-14 Attachment 2 Page 6 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 CDD18 0 232.686 -97.305 358.84 Summary Jun 2021 heating load 7323 MWh Normal heating load 6995 MWh 2021 Varinace to Normal 328 MWh

AI summary The document provides variable coefficients for different heating and cooling metrics, along with a summary of June 2021 heating load, normal heating load, and the variance between them. These figures are used for analysis in a regulatory proceeding.

June-21 June Normal Total Load from Load Load Load Load Load Heating Month Load From Load From Load From Load from from Total Heating Actual from Feb From From Load Load from Load Day Actual HDD 0 Actual HDD 13 Actual CDD 18 HDD13 HDD 13 H...

AI summary The text presents a table with load data, including heating degree days (HDD) and cooling degree days (CDD), for a specific period. It includes columns such as 'Load from Month,' 'Load from Day,' and 'Total Heating Load,' indicating a focus on energy consumption analysis over time.

0.0 0.00 4.61 0 0 0 0 0 1655.15 1655 6 28 0.0 0.1 0.4 0 23 0 0 0 129 152 29 0.0 0.00 0.00 0 0 0 0 0 0 0 6 29 0.0 0.0 0.0 0 9 0 0 0 4 13 30 0.0 0.00 0.00 0 0 0 0 0 0 0 6 30 0.0 0.0 0.9 0 0 0 0 0 305 305 REDACTED (CONFIDENTIAL INFORMATION RE...

AI summary The text provides data on heating load for July 2021, showing a significant deviation from the normal heating load. The actual heating load was 12,226 MWh, compared to the normal of 22,598 MWh, resulting in a variance of -10,373 MWh. Variable coefficients and other metrics are also presented.

0.0 0.0 1.4 0 0 0 0 0 750 750 31 0.0 0.00 3.23 0 0 0 0 0 1784 1784 8 31 0.0 0.0 0.9 0 0 0 0 0 486 486 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 9 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag...

AI summary The document provides data on heating load in September 2021, showing a significantly lower load of 3294 MWh compared to the normal heating load of 6039 MWh, with a variance of -2745 MWh. Coefficients for variables such as HDD13, lag1, and CDD18 are also listed.

0 0 40 28 0.00 0.00 0.00 0 0 0 0 0 0 0 9 28 0.0 0.4 0.16 0 98 0 0 0 53 151 29 0.00 0.00 0.00 0 0 0 0 0 0 0 9 29 0.0 0.3 0.29 0 58 0 0 0 97 155 30 0.00 0.00 0.00 0 0 0 0 0 0 0 9 30 0.0 0.8 0.32 0 195 0 0 0 107 302 REDACTED (CONFIDENTIAL INF...

AI summary The text provides data on heating load for October 2021, showing a total of 30,150 MWh, which is significantly lower than the normal heating load of 49,475 MWh, resulting in a variance of -19,325 MWh. Variable coefficients and HDD (Heating Degree Days) values are also presented.

Date Year Month Day HourEnding Temp Load 12/1/2016 2016 12 4 18 7.9 1416.985 Chart Title 12/2/2016 2016 12 5 18 3.8 1334.853 2500 12/5/2016 2016 12 1 18 -0.4 1575.374 12/6/2016 2016 12 2 18 -0.7 1520.778 2000 12/7/2016 2016 12 3 18 4.6 154...

AI summary The text presents a dataset containing dates, temperatures, and load values over a period from December 2016 to December 2017, along with a chart title and a linear regression equation. It appears to be related to energy load management and temperature trends.

Date Year Month Day HourEnding Temp Load 1/1/2016 2016 1 5 18 Chart Title 1/4/2016 2016 1 1 18 -2.2 1589.985 2500 1/5/2016 2016 1 2 18 -8.2 1718.745 1/6/2016 2016 1 3 18 2.6 1525.711 2000 1/7/2016 2016 1 4 18 2.2 1485.471 1/8/2016 2016 1 5...

AI summary The text presents a dataset containing dates, temperatures, and load values over time, along with a chart title and a linear regression equation. The data appears to be related to energy load forecasting and analysis.

Date Year Month Day HourEnding Temp Load 2/1/2016 2016 2 1 18 6.8 1336.218 2/2/2016 2016 2 2 18 0.3 1383.221 2/3/2016 2016 2 3 18 0.1 1526.842 2/4/2016 2016 2 4 18 8.3 1277.663 2/5/2016 2016 2 5 18 2.6 1416.688 2/8/2016 2016 2 1 18 -5.1 16...

AI summary The provided text is a table containing historical data on temperature and load for specific dates in February 2016 and February 2017. It includes columns for date, year, month, day, hour ending, temperature, and load. This data may be used for analyzing load patterns and their correlation with temperature.

/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.

fact, and the variance is driven by individual behaviours, temperature and weather in the 17 days before, patterns that may be related to specific days of the week (for example, more Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 2 of 3...

AI summary The text discusses energy and demand forecast trends, noting differences between morning February/March peaks and evening December/January peaks. It attributes the variance to factors like lighting load and temperature sensitivity, estimating a difference of around 150 MW and a temperature sensitivity of approximately 30 MW per degree Celsius.

-13.7 0 2,291 2,101 2027 2326 2,133 -13.7 0 2,326 2,133 2028 2361 2,170 -13.7 0 2,361 2,170 2029 2398 2,207 -13.7 0 2,398 2,207 2030 2434 2,243 -13.7 0 2,434 2,243 2031 2479 2,289 -13.7 0 2,479 2,289 2032 2532 2342 -13.7 0 2,532 2,342 REDA...

AI summary The text includes a 10-year energy and demand forecast from the 2022 Load Forecast Report (NSUARB M10569) and mentions NSPI responses to Consumer Advocate Information Requests. The content is partially redacted and marked as confidential.

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 references a 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, submitted as part of a regulatory proceeding (NSUARB M10569). It also notes that NSPI has responded to information requests from the Consumer Advocate, though the specific content of the report and responses are redacted due to confidentiality.

10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Reference: NS Power 2022 Load Forecast, page 15, lines 3-8. 4 5 “As...

AI summary NSPI responds to EfficiencyOne's request for E3's stock rollover models by directing them to NSUARB IR-2 and Synapse IR-42. The 2022 Load Forecast Report references E3's work on electrification scenarios to meet emissions goals, emphasizing no assumptions about regulatory changes. Appendix E includes an E3 presentation.

ction targets. 26 27 (c) Other heating system technologies are not included in this model as heat pumps have greater 28 efficiency than resistive heating and can be widely deployed. Date Filed: July 8, 2022 NSPI (E1) IR-5 Page 1 of 1 10 -...

AI summary NSPI responds to EfficiencyOne's information requests regarding heat pump deployment data and the 2022 Load Forecast Report. Data on residential heat pump types is unavailable, with an assumption that most are ductless. The model excludes other heating technologies due to heat pumps' efficiency and deployability.

15 16 (e) Yes, NS Power views Electric Thermal Storage (ETS) as an effective backup for Air-Source 17 Heat Pump systems when ETS is sized appropriately to carry the heating load. 18 Date Filed: July 8, 2022 NSPI (E1) IR-9 Page 3 of 4 10 -...

AI summary NSPI confirms Electric Thermal Storage (ETS) can effectively back up air-source heat pumps when appropriately sized. However, it expresses uncertainty about hybrid systems combining heat pumps with fossil fuels in meeting 2050 Net Zero targets. The forecast assumes consistent weather and does not model unaccounted factors, deeming their 10-year impact unlikely.

NSPI (NSUARB) IR-2 Page 1 of 6 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 2 3 4 E3’s modeling is based on its PATHWAYS stock rollover mo...

AI summary NSPI responds to NSUARB's information request regarding a 10-Year Energy and Demand Forecast, detailing E3's PATHWAYS stock rollover model. The model assumes 100% EV adoption for personal light-duty vehicles by 2035, with 30% achieved by 2030, based on vehicle lifetimes and sales-stock relationships.

(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.

class in 2025, 27 compared to a variance of 3 percent on the peak side. To simplify the analysis the same 28 forecast for heat pump shares was used and sales were not adjusted. Date Filed: July 8, 2022 NSPI (NSUARB) IR-10 Page 1 of 1 10 -...

AI summary The document discusses a 10-year energy and demand forecast, referencing a 2022 Load Forecast Report and a forecast for heat pump shares. It mentions a variance of 3 percent on the peak side and the use of the same forecast for heat pump shares without adjustments to sales.

and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 With reference to Section 4.4 End-Use Intensity Trends, page 41 of 98, Figure 25: Water 4 H...

AI summary The NSUARB has requested actual data for water heater usage from 2016 to 2021 and clarification on discrepancies between two water heater forecasts in the application. NSPI has responded by referring to a table containing the requested actuals.

the 7 two forecasts, with the 2021 series having an average of 1.6 percent growth year over year 8 and the 2022 series having an average of 1.7 percent growth year over year. Date Filed: July 8, 2022 NSPI (NSUARB) IR-13 Page 2 of 2 10 - Ye...

AI summary The document discusses the source of data used to estimate the number of electric vehicles (EVs) in Nova Scotia as of the end of 2021. The data was obtained from provincial open datasets related to EV rebates and registrations.

32 BEV 53 123 211 315 436 575 729 900 1,106 1,345 Diesel 5,201 5,176 5,143 5,102 5,053 4,996 4,931 4,858 4,778 4,681 4,569 Gasoline 5,865 5,837 5,800 5,754 5,698 5,634 5,561 5,479 5,388 5,279 5,152 23 Date Filed: July 8, 2022 NSPI (NSUARB)...

AI summary The document provides a 10-year energy and demand forecast, including EV adoption projections, and responds to an information request regarding the difference between the 2022 and 2021 EV forecasts. NSPI submitted a table with data used in each model as part of their response.

Updated EV assumptions 2021 Average use per year (kWh) Peak Load (kW) % charging at peak Plugin Hybrid Electric 1790 Growth 1.8 Plugin Hybrid 3.3 10% Battery Electric 3950 Decay 0.9 Battery 8.13 10% 2021 Energy 2021 Peak Year Total convers...

AI summary The document provides updated assumptions about electric vehicle (EV) usage, including average annual energy consumption, peak load, and the percentage of charging occurring at peak times for Plugin Hybrid Electric (PHEV) and Battery Electric (BEV) vehicles from 2020 to 2027. It includes projections on total conversions, energy consumption in GWh, and peak load in MW.

y and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 With reference to Section 4.4 Electric Vehicles, page 43 of 98, the application states, “...

AI summary NSPI responded to NSUARB's information request regarding the basis of driving habit simulations in the 2022 Load Forecast Report. For LDVs, data from the New England profile in the 2017 National Household Travel Survey was used, and for MDVs, data from NREL’s Fleet DNA was used. NSPI stated that no significant changes in driving patterns were observed due to increased work-from-home arrangements.

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.

1 Request IR-19: 2 3 With reference to Section 4.4 End-Use Intensity Trends, page 53 of 98, Figure 34: 4 Commercial and Industrial Electrification Forecasts (cumulative), please expand this table 5 to include actuals for the years 2019, 20...

AI summary The request asks for the inclusion of actual data for 2019 to 2021 in the Commercial and Industrial Electrification Forecasts table. The response provides an expanded table with actuals for those years and continues with projected data up to 2031.

y and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 With reference to Section 5.0 Residential Sector, page 59 of 98, Figure 37 Comparison of...

AI summary NSPI explains the weather-adjusted sales metric, which adjusts actual sales to reflect what sales would be under normal weather conditions. In 2019, actual sales were higher due to colder than normal temperatures, and the adjustment subtracts the weather impact to provide a normalized sales figure for comparison with forecasts.

nergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-25: 2 3 With reference to Section 5.0 Residential Sector, page 59 of 98, the application stat...

AI summary NSPI responded to an information request regarding the impact of COVID-19 on residential energy sales forecasts. They noted that the +100 GWh impact in 2022 and +54 GWh for 2023 and beyond is higher than the +42 GWh impact in 2021, attributing this to unexplained variances and weather normalization adjustments.

ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 With reference to Section 5.0 Residential Sector, page 60 of 98, the application ident...

AI summary NSPI responded to two information requests from the NSUARB regarding the 2022 Load Forecast Report. The first addressed EV penetration rates in the residential sector, noting that the forecast assumes provincial rates without considering current vehicle shortages. The second clarified that billing data used in the analysis was from the CIS system in 2018, before AMI meter installation.

y a lower increase in 2018. The forecast has house size increasing at a slower rate than 3 in New England as multi family housing is expected to make up a larger portion of new housing 4 stock. Date Filed: July 8, 2022 NSPI (NSUARB) IR-28...

AI summary The document discusses a 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, highlighting anticipated changes in commercial sales and the impact of demand-side management (DSM) on sales forecasts from 2027 to 2032. It also references a slower rate of house size increase in Nova Scotia compared to New England due to the growth of multi-family housing.

ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-31: 2 3 With reference to Section 6.3 Large General Service, page 70 of 98, the application in...

AI summary NSPI responded to an NSUARB information request regarding the 2022 Load Forecast Report, explaining that solar adoption is considered in the commercial class but not in the large general service class due to a lack of specific information on self-production.

nd 90 percent for 12 dryers by 2032. Miscellaneous loads represent small plug loads, and these are expected to increase 13 faster than efficiency gains as more electronic devices are purchased. Date Filed: July 8, 2022 NSPI (NSUARB) IR-35...

AI summary The response to IR-36 explains that the significant growth in energy and demand forecasts after 2022 is primarily due to increased expectations for space heating and EV adoption, driven by carbon reduction targets.

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-1: 2 3 Please provide all inputs and outputs to the customer coun...

AI summary NSPI provided customer count models for Residential and Small General classes, using housing completions data and a GDP-based model with a COVID-19 binary variable. The Residential forecast adds housing completions to existing counts, while Small General uses non-manufacturing GDP and a 2021 binary factor.

Single Family Multi Family Completions Completions Residential Customer (Conference (Conference Customer accounts Board data) Board Data) Count Year Month SmlGenCustNManGDP Yr20Plus XMissing YMissing Variable Coefficient StdErr T-Stat P-Va...

AI summary The text presents statistical data on residential customer accounts and economic variables (e.g., NManGDP, Yr20Plus) from 2012 to 2017, including coefficients, standard errors, and p-values for regression analysis. It includes monthly metrics and economic indicators related to small generators and customer account counts.

2 0 0 2020 2 25,680.00 34,663.02 0 0 0 2020 2 25,730.71 25,730.71 0 0 2020 3 25,655.00 34,590.53 0 0 0 2020 3 25,676.91 25,676.91 0 0 2020 4 25,661.00 34,518.05 0 0 0 2020 4 25,623.10 25,623.10 0 0 2022 LFR SBA IR-1 Attachment 1 Page 3 of...

AI summary The text contains a table with numerical data spanning 2020 and 2022, including entries labeled 'Residential,' 'Small General Inputs,' 'Coefficients,' and 'Outputs.' It references '2022 LFR SBA IR-1 Attachment 1 Page 3 of 6,' suggesting a regulatory proceeding context.

Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Please refer to page 52 of the 2022 Load Forecast Report (the “Filing”), Lines 14-18:...

AI summary NSPI responds to a request regarding electrification targets and programs, citing the Environmental Goals and Climate Change Reduction Act (EGCCRA) which sets carbon reduction goals and includes specific targets such as a zero-emission vehicle mandate and improved building emissions performance.

(SBA) IR-3 Page 1 of 4 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Electric Baseboards 2 Electric Ovens 3 Electric Infra...

AI summary The document provides a 10-year energy and demand forecast, highlighting growth in heating, transportation, and cooling. It notes that transportation growth is driven by EV adoption and increased cooling demand due to rising temperatures and longer summers. The forecast data is part of the 2022 Load Forecast Report (NSUARB M10569).

NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Federal Zero Emission Vehicle Sales Mandates 2 3 4 Assumption : Based on 50,000 yearly car sales in NS versus Federal government EV 5 adoptions targets 6 7 L...

AI summary NSPI provides responses to information requests from the Small Business Advocate, discussing industrial electric load growth driven by process equipment electrification and new industrial facility construction, noting minimal impact from transportation electrification based on IEA projections.

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.

tical Peak Pricing” and “Business, Non-Profit & Industrial Curtailment” 10 separately for Figure 54. 11 12 (c) Is response time a factor considered while computing these ELCC values? 13 14 Response IR-6: 15 16 (a) Demand Response (DR) is a...

AI summary The response discusses how Demand Response (DR) programs are factored into Energy Loss Cost Curves (ELCC) calculations, referencing a study from the pre-IRP work to the 2020 Integrated Resource Plan. It outlines assumptions made by NS Power regarding DR program characteristics and their impact on ELCC values.

Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 2 3 On the commercial side, electric space heating is expected to increase compared to historic 4...

AI summary NSPI responds to a request regarding the accuracy of a heat pump adoption forecast and whether adoption rates under current incentives have been forecasted. The forecast is described as a smoothed adoption profile aimed at achieving 2050 net zero emissions targets, and NSPI states no forecasts under current incentives have been provided.

ever would lead to relatively 20 minor changes in the profile if the starting point (current state) and ending point (net zero by 21 2050) are held constant. 22 23 (b) No. Date Filed: July 8, 2022 NSPI (SBA) IR-11 Page 1 of 1 10 - Year Ene...

AI summary The response explains a discrepancy in energy forecasts between figures by accounting for project timing, adjusting annualized values by half the 2022 values to reflect actual impacts over the 2022–2032 period.

n the commercial 17 class less half of 21 GWh, or the 83 GWh as shown in Figure 54. Likewise, the industrial 18 difference would be 41 GWh less half of 10 GWh, or the 36 GWh shown in Figure 53. Date Filed: July 8, 2022 NSPI (SBA) IR-12 Pag...

AI summary The document discusses energy and demand forecasts for commercial and industrial sectors, including the electrification of heating, cooling, and transportation. It references figures from the 2022 Load Forecast Report and responds to information requests from the Small Business Advocate.

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.

on/saturation at this time? 24 25 (c) Please describe in detail any adjustments NS Power made to the forecast in Figure 27 26 before using it as a driver in its forecast model. 27 Date Filed: July 8, 2022 NSPI (SBA) IR-19 Page 1 of 2 10 -...

AI summary NSPI responded to an information request regarding adjustments made to the 10-Year Energy and Demand Forecast. NSPI stated that Figure 27 represents assumptions related to EV adoption and energy consumption, and no adjustments were made to the forecast before using it in its model.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Synapse Energy Economics Inc. Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Report T...

AI summary The document details NSPI's responses to Synapse Energy Economics Inc.'s information requests regarding the 2022 Load Forecast Report (NSUARB M10569). It includes provision of historical sales data, energy usage metrics, load data, and unmetered sales information, with specific references to attachments and figure listings.

Heating Indices (kWh / HH) Cooling Indices (kWh / HH) Other Indices (kWh / HH) Other Regression Variables Regression Sales Results Out of Monthly Model (kWh / HH) Year SmlGenIndices.Heating AContrib2Sales.SmlGenHeatUse SmlGenIndices.Coolin...

AI summary The text presents a set of numerical data related to various energy indices and regression variables, including heating, cooling, and other usage metrics, along with their contributions to sales results. The data spans multiple years and includes variables such as ventilation, cooking, refrigeration, lighting, and office usage.

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.

mVarsNew.Jan_Other 1.290 0.093 13.799 0.00% mVarsNew.Feb_Other 1.236 0.101 12.266 0.00% mVarsNew.Mar_Other 1.377 0.084 16.369 0.00% mVarsNew.Apr_Other 1.570 0.047 33.165 0.00% mVarsNew.May_Other 1.543 0.035 43.547 0.00% mVarsNew.Jun_Other...

AI summary The text presents statistical model results, including variables (e.g., monthly 'Other' costs) and model metrics (R-squared: 0.976, AIC: 7.87). It references 'AnnualSavings.DSMDemSavings' with a 0.50% value, suggesting analysis of demand-side management savings.

6667 37075.8 12/6/2021 4.741667 37500.47 12/7/2021 7.220833 32568.2 12/8/2021 -0.59167 37381.32 12/9/2021 -3.4875 39735.68 12/10/2021 -4.9625 39883.8 12/11/2021 7.758333 35142.52 12/12/2021 7.945833 32563.46 12/13/2021 5.695833 34317.49 12...

AI summary The text presents a series of temperature and load data points from December 2021 and references a 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report (NSUARB M10569). It also mentions NSPI's responses to consumer advocate information requests.

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 The document outlines NSPI's responses to consumer advocate information requests regarding the 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report, referenced as NSUARB M10569.

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 The document discusses a 10-year energy and demand forecast from the 2022 Load Forecast Report, filed by NSPI in response to information requests from the Consumer Advocate. The forecast includes load estimation methods, such as temperature-dependent load variables and baseload estimation.

0.897 2,844.58 634.04 0 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 1 Page 56 of 57 Variable Coefficient StdErr T-Stat P-Value Units Definition CONST 19044.52 56.624 336.331 0.00% Constant term Daily.HDD13 232....

AI summary The text presents a statistical table with coefficients, standard errors, T-statistics, and P-values for various variables, including heating and cooling degree days, days of the week, and lagged heating degree days. These variables are likely related to energy usage modeling or forecasting.

Daily.Dec 21504.333 520.882 41.284 0.00% REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 2 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 403.966 232.686 -97.305 177.016 43.995 Summary Feb 202...

AI summary The document provides heating load data for February 2021, showing a total heating load of 344,558 MWh, which is 22,790 MWh below the normal heating load of 367,348 MWh. Coefficients and other variables are also listed, indicating analysis of heating demand.

9389 2 28 2.9 15.1 6096 3511 -278 3022 635 12985 29 0 0 0 1621 673 2294 2 29 3.2 15.2 6148 3541 -314 2671 751 12798 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 3 of 15 Variable Coefficients Month HDD13 H...

AI summary The text provides data on heating load for March 2021, showing a total of 282,880 MWh, compared to a normal heating load of 304,810 MWh, with a variance of -21,930 MWh. It also includes statistical coefficients and other numerical values.

2022 LFR CA IR-14 Attachment 2 Page 4 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 180.936 232.686 -97.305 177.016 43.995 Summary Apr 2021 heating load 120947 MWh Normal heating load 161906 MWh 2021 Varinace to Normal -409...

AI summary The document provides heating load data for April 2021, showing an actual load of 120,947 MWh compared to a normal load of 161,906 MWh, resulting in a variance of -40,959 MWh. It also includes variable coefficients and HDD (Heating Degree Days) values for analysis.

April-21 April Normal Total Load from Load Load Load Heating Actual Month Load From Load From Load From Load from Total Heating from Feb From From Load Load Load Day Actual HDD 0 HDD 13 HDD13 HDD 13 HDD0 lag1 lag2 Load (MWh) Month Day HDDO...

AI summary The text presents a table with load data, including HDD (Heating Degree Days) values and load measurements from different periods, such as 'lag1' and 'lag2', along with total heating load and daily load figures. The data appears to be part of an analysis related to energy consumption patterns.

June-21 June Normal Total Load from Load Load Load Load Load Heating Month Load From Load From Load From Load from from Total Heating Actual from Feb From From Load Load from Load Day Actual HDD 0 Actual HDD 13 Actual CDD 18 HDD13 HDD 13 H...

AI summary The document contains a table with load data, including heating and cooling degree days (HDD and CDD), load values, and lag values for different days in June. The data includes load from various sources, total load, and heating load details.

0 0 0 0 0 7 29 0.0 0.0 1.4 0 0 0 0 0 799 799 30 0.0 0.00 0.00 0 0 0 0 0 0 0 7 30 0.0 0.0 1.7 0 0 0 0 0 956 956 31 0.0 0.00 0.00 0 0 0 0 0 0 0 7 31 0.0 0.0 2.7 0 0 0 0 0 1498 1498 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-1...

AI summary The text presents a summary of heating load data for August 2021, indicating a total heating load of 32,073 MWh, a normal heating load of 25,787 MWh, and a variance of 6,286 MWh. It also includes statistical data, such as variable coefficients and HDD (Heating Degree Days) values, which are used in energy demand forecasting.

0 0 40 28 0.00 0.00 0.00 0 0 0 0 0 0 0 9 28 0.0 0.4 0.16 0 98 0 0 0 53 151 29 0.00 0.00 0.00 0 0 0 0 0 0 0 9 29 0.0 0.3 0.29 0 58 0 0 0 97 155 30 0.00 0.00 0.00 0 0 0 0 0 0 0 9 30 0.0 0.8 0.32 0 195 0 0 0 107 302 REDACTED (CONFIDENTIAL INF...

AI summary The text provides data on heating load for October 2021, showing a load of 30,150 MWh, which is significantly lower than the normal heating load of 49,475 MWh, with a variance of -19,325 MWh. It also includes variable coefficients and other statistical data related to heating degree days and lagged variables.

694 924 0 510 200 2328 10 27 0.0 5.7 996 1326 0 949 194 3466 28 0.00 4.60 805 1071 0 703 127 2706 10 28 0.0 5.7 1001 1333 0 1009 236 3579 29 0.00 3.47 606 807 0 815 175 2402 10 29 0.0 3.7 638 849 0 1014 251 2752 30 0.00 4.98 871 1160 0 614...

AI summary The text presents a summary of heating load data for November 2021, showing a total heating load of 162,886 MWh, which is lower than the normal heating load of 181,899 MWh by 19,013 MWh. It also includes variable coefficients and other numerical data related to heating degree days and lagged values.

Date Year Month Day HourEnding Temp Load 12/1/2016 2016 12 4 8 4.7 1276.964 Chart Title 12/2/2016 2016 12 5 8 4.6 1212.008 2000 12/5/2016 2016 12 1 8 -3.2 1373.484 1800 12/6/2016 2016 12 2 8 -3.9 1413.756 1600 12/7/2016 2016 12 3 8 -1 1426...

AI summary The text presents a table of temperature and load data over several dates in December 2016 and December 2017, including a linear regression equation and an R-squared value, indicating an analysis of the relationship between temperature and load.

Date Year Month Day HourEnding Temp Load 12/1/2016 2016 12 4 18 7.9 1416.985 Chart Title 12/2/2016 2016 12 5 18 3.8 1334.853 2500 12/5/2016 2016 12 1 18 -0.4 1575.374 12/6/2016 2016 12 2 18 -0.7 1520.778 2000 12/7/2016 2016 12 3 18 4.6 154...

AI summary The text presents a table with dates, temperatures, and load values over a period in 2016 and 2017, including a chart with a linear regression equation and an R-squared value, indicating an analysis of temperature and load relationship.

/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.

fact, and the variance is driven by individual behaviours, temperature and weather in the 17 days before, patterns that may be related to specific days of the week (for example, more Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 2 of 3...

AI summary The text discusses energy and demand forecast trends, noting differences between morning and evening peaks influenced by factors like lighting load and temperature sensitivity. Variance is attributed to behavioral patterns, weather, and specific days of the week, with a normalized peak estimate of around 2100 MW.

-13.7 0 2,291 2,101 2027 2326 2,133 -13.7 0 2,326 2,133 2028 2361 2,170 -13.7 0 2,361 2,170 2029 2398 2,207 -13.7 0 2,398 2,207 2030 2434 2,243 -13.7 0 2,434 2,243 2031 2479 2,289 -13.7 0 2,479 2,289 2032 2532 2342 -13.7 0 2,532 2,342 REDA...

AI summary The document contains a 10-year energy and demand forecast from the 2022 Load Forecast Report (NSUARB M10569) and includes NSPI responses to consumer advocate information requests. The data includes forecasted values for energy and demand across the years 2027 to 2032. The content is marked as confidential and is an attachment only.

1 Request IR-15: 2 3 According to Exhibit N-34, Matter No. M10431, Response to CA IR-41, Attachment 1, NS 4 Power has developed scaled class load shapes for 2019 using its load research sample. The 5 Report indicates that these data have b...

AI summary The request asks NS Power to provide updated scaled class load shapes, estimates of monthly losses by class, and an explanation for using 2013 loss factors instead of more recent data. The response refers to confidential attachments and explains that losses are calculated at the system level, not by class.

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.

ecast Report, pp. 19-24. 7 Exhibit N-7, NS Power response to CA IR-1(c-d). 8 Each of the major components is multiplied by a regression factor, which is close to 1.0 for heating and other, but only 0.6 for cooling. 9 Exhibit N-7, NS Power...

AI summary The analysis examines cooling and heating model outputs, showing increasing CDD-driven cooling demand but stable heating demand despite decreasing HDD. Heat pumps and electrification are suggested to offset reduced heating needs, though NS Power requires further validation of electrification load forecasts.

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.

EfficiencyOne Evidence 1 62,492 heat pumps had been accredited.” 8 This amounts to less than one-sixth of the domestic 2 heat pumps intended by the original end date. 3 4 The challenges experienced in the UK illustrate how the market can d...

AI summary EfficiencyOne (E1) highlights that only 62,492 heat pumps were accredited, far below policy targets. It criticizes NS Power's insufficient disclosure of electrification scenario assumptions in the 2022 Load Forecast, raising concerns about modeling uncertainties and their impact on planning decisions.

vidence 1 when system reliability is jeopardized.” 31 2 3 E1 recommends that NS Power apply the same framework when assessing the value provided 4 by different demand response programs. 5 6 5. SUMMARY 7 What are the primary recommendations...

AI summary E1 recommends updating NS Power's heat pump modeling to reflect current adoption trends, exploring electrification scenarios with specific technologies, providing a plan for demand response capacity, and applying a consistent framework for assessing demand response programs to ensure system reliability and effective resource planning.

Power) 10-Year Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL Year Forecast Incentive Level Actual Incentive Level 2013 M04819, E-7 (C ), ENSC (Avon) RIR-11 Ce...

AI summary The document outlines E1's responses to NS Power's information requests regarding energy and demand forecasts, including rebate details for heat pump programs from 2013–2015. It references specific matter numbers (M10569, M04819) and highlights rebate levels for Central Ducted Air Source and Ground Source Heat Pumps, with varying percentages and caps.

Year Forecast Energy Forecast Demand Actual Energy Actual Demand Savings Wood/Pellet Savings Wood/Pellet Savings Savings Stove Stove Wood/Pellet Wood/Pellet Stove Stove 2022 M09096 E-1(i) Appendix A Attachment 1 Technical n/a Tables • Rows...

AI summary The text presents tables comparing forecast and actual energy/demand savings across years (2022-2025), referencing specific regulatory matters (M09096, M10473) and technical appendices. It includes row/column references from '2023-2025 Settlement Plan Measure Level Energy Efficiency Technical Tables' and mentions 'ETS Unit Installations' in energy savings contexts.

the load forecast. 29 30 NS Power Response: 31 32 E1 states that “[LIIR] the ELCC is likely to be very close to 1 but it should still be 33 considered given the program does not represent perfect capacity”. 14 This is 14 M10569, Exhibit N-...

AI summary NS Power responds to E1's claim regarding the ELCC for LIIR customers, arguing that a quantitative ELCC analysis is unnecessary given both parties agree the value is close to 1. NS Power acknowledges EV charging characteristics similar to ELCC adjustments and has modeled 70% of EV load as responsive in the 2022 Load Forecast.

ing storm closures at peak periods. Lastly, Mr. Wilson requested that NS Power complete the line loss determination model and report on its progress on a quarterly basis until the project is complete. The SBA raised concerns about the accu...

AI summary Mr. Wilson requested NS Power to finalize the line loss model with quarterly updates. The SBA questioned the EV and space heating forecast accuracy, urging data integration and incentive evaluation. EOne recommended updating heat pump models, exploring electric thermal storage, and assessing DR capacity frameworks to address growing peak load.

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 text outlines recommendations for improving NS Power's modeling practices, including re-evaluating elasticity assumptions, updating residential model inputs (e.g., income metrics, housing data), aligning economic forecasts with major banks, refining EV adoption rate data, and maintaining communication on infrastructure projects to ensure system adequacy.

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.

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 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.

1 (b) Please elaborate on any coincidence factor associated with the electric backup of 2 heat pumps. Is any coincidence factor assumed for electric backup heating in the 3 RESHAPE analysis? 4 (c) What penetration of electric resistance he...

AI summary The text includes questions about heat pump backup systems, RESHAPE analysis assumptions, climate change impacts on heating/cooling degree days, low-GWP refrigerants, and EV charging management in Nova Scotia. NS Power is asked to elaborate on technical and modeling considerations.