Topic:"Energy Efficiency Budgets" in M11689

ORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 23: Water Heater Forecast 2 Overall % Overall Intensity Year Saturation (kWh/household) 2024 73 1,633 2025 76 1,675 2026 78 1,711 2027 80 1,745 2028 82 1,777 2029 83 1,807 2030...

AI summary The 2024 Load Forecast Report includes a forecast for water heater saturation and intensity, as well as an overview of existing federal and provincial incentives for electric vehicles (EVs). It notes that there were approximately 4100 EVs in Nova Scotia as of the end of 2023 and references a report on ensuring zero-emission vehicle (ZEV) adoption.

tion has been submitted by a third party to the NSUARB for approval as a 4 Licensed Retail Supplier (LRS) to provide service under the RTR tariffs starting in 2025. 31 30F 5 The service is forecast to produce 340 GWh of energy through wind...

AI summary A third-party application to become a Licensed Retail Supplier (LRS) under the RTR tariffs starting in 2025 has been submitted to the NSUARB. The service is expected to generate 340 GWh of energy through wind production by 2026, serving residential, commercial, and industrial customers. NS Power will provide top-up energy under the Energy Balancing Service tariff, and peak forecast remains unchanged.

term and must continue 23 to plan for serving these customers in the long term, the full amount of the municipal 24 electric utilities’ peak demand is included in the Load Forecast. DATE: April 30, 2024 Page 76 of 100 REDACTED (CONFIDENTIA...

AI summary The document discusses the 2024 Load Forecast Report, highlighting the inclusion of municipal electric utilities’ peak demand in long-term planning. It also addresses system losses and unbilled sales, noting that system losses averaged 6.5% of NSR over the past five years and are expected to remain between 6.0% and 7.0% over the 10-year forecast period.

1 normalization are the same as those used in the 2023 forecast and are described in Figure 2 57 below: 3 4 Figure 57: Peak Regression Coefficients 5 Coefficient Value Description Weekdays 30.5 Peaks that occur on weekdays will be 30.5MW h...

AI summary The document discusses peak load forecasting, including factors such as weekday vs. weekend differences, wind speed, and temperature lag. It notes that the forecast system peak is expected to increase by 1.4% annually, with near-term increases due to heating load and updated temperature averages, and long-term decreases due to reduced EV impact and hybrid heating scenarios.

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 sales are the dependent variable. The table presents coefficients, standard errors, T-Statistics, and P-values for various variables, including heating, cooling, and seasonal factors, as well as the impact of the MA(1) term.

TIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 29 of 35 Combined Model for Commercial and Industrial DSM Coefficient NonResSalesm = b1×NonResEESavingsProfiledm + b2×GenWtXHeatm + b3×GenWtXCoolm + b3×GenWtXOtherm + b4×N...

AI summary This section presents a combined model for commercial and industrial demand-side management (DSM) coefficients, including variables such as non-residential energy efficiency savings, weighted end-use factors, and customer counts. The model uses historical data and binary variables to address billing issues in February 2018 and October 2022. The EESavings variable coefficient indicates the amount of DSM required to explain historical sales trends beyond end-use changes.

r a high residual and for October 2022 to account for the impact of billing delays related to hurricane Fiona in the energy models. Variable Coefficient StdErr T-Stat P-Value mVarsNew.Heat_Var 1.541 0.080 19.375 0.00% mVarsNew.Cool_Var 1.3...

AI summary The text discusses statistical analysis of energy models, including coefficients, standard errors, t-statistics, and p-values for various variables related to heating, cooling, and monthly energy usage. A specific adjustment was made for October 2022 due to billing delays caused by Hurricane Fiona.

ad Forecast Report Appendix E Page 16 of 18 2023 Forecast to Actuals Below is an estimate of the major variances between the 2023 forecast and 2023 actuals for both energy and peak. Item GWh Item MW 2023 Forecast NSR 11,288 2023 Forecast P...

AI summary The document compares 2023 forecast and actuals for energy and peak demand, highlighting variances due to weather, customer behavior, and wind generation. It also outlines updates on pilot projects, solar generation, hydrogen production, AMI integration, and assumptions related to customer growth and economic inputs.

the names of such jurisdictions and Date Filed: June 19, 2024 NSPI (Synapse) IR-8 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDEN...

AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, addressing electric water heater standards, rebate programs, and load control initiatives. The report notes that heat pump water heaters are not explicitly modeled due to low uptake, and load control programs are described but not yet evaluated for impact.

1F 5 National Energy Research Lab 3 showed that the results had not changed significantly since 2F 6 those earlier reports. The literature has not been reviewed for a more recent study. 7 8 (c) As stated on page 52 of the report, daily pri...

AI summary The text discusses the estimation of price elasticity based on Itron's experience across multiple jurisdictions, noting that NS Power relies on this data for load forecasting. The elasticity values are derived from the TVP pilot and are used to model changes in consumption behavior in response to price changes.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-22: 2 3 Commercial Sector (Section 6.0). 4 5 (a) Please explain and quantify...

AI summary NSPI explains the impact of COVID-19 on commercial sector electricity loads in 2022 and 2023, estimating a reduction of -69 GWh in 2022 and -57 GWh in 2023. The current forecast shows a lesser increase in sales due to factors like commercial sales shifting to the RTR market, increased solar adoption, and reduced EV sales.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 Small General Service (Section 6.1). 4 5 (a) Please explain and quantify the specific reasons for the difference...

AI summary NSPI responded to Synapse's information request regarding the 2024 Load Forecast Report, explaining that the forecast for Small General Service has not changed significantly from 2023. Differences are due to higher-than-expected 2023 sales and slight declines through 2026, with shifts in sales to RTR and decreased EV sales. The EV load contribution decreased from 21% to 14%, and the XOther component showed increased growth in 2024 compared to 2023.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iii) How many water heaters and smart thermostats does NSPI assume for its peak 2 reduction estimates for the TVP rates? 3 4 (iv)...

AI summary The document outlines a series of information requests from NSPI to Synapse regarding load forecasting assumptions, including peak load reductions, program participation, demand response impacts, and electrification assumptions in the 2024 Load Forecast Report.

56,808 1.27 0.856 0.070 4,323 2034 63,121 1.37 0.856 0.070 5,182 Change to 12.0% 7.9% 0.0% 0.0% 19.9% Xcool Inputs Cooling CoolUse Var Coefficient Scaling FactTotal Xcool 2024 36,759 1.38 0.330 0.035 586 2034 35,682 1.77 0.330 0.035 729 Ch...

AI summary The document contains a 2024 Load Forecast Report from the NSUARB (M11689) and NSPI's responses to Synapse Information Requests. It includes data on energy usage, such as cooling and other inputs, with percentages of change between 2024 and 2034.

Regression Sales Results Out of Monthly Model Other Regression Variables (kWh / HH) Year AContrib2Sales.GenOtherUse AContrib2Sales.Feb18 AContrib2Sales.May20 AContrib2Sales.Jun20 ntrib2Sales.Ontrib2Sales.Sentrib2Sales.C AContrib2Sales.May2...

AI summary The text presents regression sales results and other regression variables in kilowatt-hours per household (kWh / HH) over the years 2014 to 2019, including various metrics such as AContrib2Sales and GenSalesARMA. The data shows fluctuations in values across different years, with some variables having non-zero values starting in 2018.

† Monthly HDD † Monthly CDD † Economics REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-45 Attachment 2 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast...

AI summary The 2024 Load Forecast Report includes projections for energy demand, peak load, and the impact of demand-side management (DSM), solar PV, and electric vehicle (EV) adoption. It also outlines possible scenarios for hydrogen production, battery storage, and the effects of weather and economics on load forecasts.

to Synapse IR-22. 53 Note that the reported DSM impacts are in addition to DSM effects embedded in the SAE model itself. Therefore, the actual savings from DSM programs are significantly greater. Synapse Energy Economics, Inc. Evidence Reg...

AI summary The document discusses the impact of DSM programs on electricity demand, highlighting that DSM effects are in addition to those embedded in the SAE model. It also outlines projected changes in small general service sales, including the influence of factors like renewable energy, electric vehicles, and solar. NSPI notes the long-term impact of the pandemic on commercial sales and revised forecasts for large general service loads.

levels. We ask NSPI to explore the potential for greater industrial savings. We ask NSPI to explore the impacts of real time rates. We ask NSPI to explore the impacts of increases in industrial RTR. 2.5. Commercial and Industrial Electrifi...

AI summary The text discusses requests for NSPI to explore industrial savings, real-time rates, and impacts of industrial RTR. It outlines forecasts for commercial and industrial electrification, municipal sector load reductions, and the modest impact of DSM savings on energy forecasts, noting changes in the coefficient used to adjust future DSM savings.

lly for peak management. 7. NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. 8. We ask that NSPI reassess its modeling approach f...

AI summary The document outlines various requests for NSPI to refine its load forecasting models, including validating proxies for customer growth, reassessing the impact of the pandemic on residential load, and exploring the effects of solar, DSM, and RTR on different sectors. It also suggests adjusting DSM adjustment factors if savings increase and investigating measures to mitigate peak load increases.