Topic:"Residential Demand Response Program" in M12861

.. 27 15 Figure 15: Yearly Change in Customers, Population, and Housing Completions........................ 29 16 Figure 16: Yearly Change in Residential Customers.................................................................... 30 17 F...

AI summary The text lists figures analyzing customer trends, economic drivers (residential, commercial, industrial), energy forecasts (heat pumps, EVs, hybrid heating), and load modeling scenarios. It focuses on data visualization for regulatory proceedings, including residential and commercial energy demand projections.

IAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Residential Customers 2 3 4 Regarding household size, population is currently used in combination with customer count to 5 estimate average household...

AI summary The document discusses the estimation of average household size in the SAE model using population and customer count data, and how changes in household size affect usage variables. It references figures illustrating yearly changes in residential customers and household size over time.

1 Figure 18: Residential Economic Drivers New Household % % Year Construction Compensation Change Change (number) (mil $2002) 2016 3,468 17,118 2017 3,858 11.2 17,517 2.3 2018 4,214 9.2 18,036 3.0 2019 4,207 -0.2 18,061 0.1 2020 4,470 6.2...

AI summary The table presents residential economic drivers from 2016 to 2036, showing trends in new construction and household compensation. New construction numbers and compensation figures are provided with percentage changes over time, illustrating growth and fluctuations in residential economic factors.

1 5. RESIDENTIAL SECTOR 2 3 The Residential sales forecast is generated as the product of a residential average use forecast and 4 a customer count forecast. The residential average use model is specified using a SAE model 5 structure and...

AI summary The residential sector sales forecast is based on average use and customer count projections, with EVs and solar contributing to load. The forecast accounts for billing issues from a cyber incident, using accrued sales for 2025. Weather-normalized sales grew by 0.9% in 2025, driven by new customers and heat pumps.

Page 62 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 home as several large employers in the province implemented back-to-office policies. It is 2 expected that residential sales will continue to i...

AI summary The 2026 Load Forecast Report predicts that residential electricity sales will increase through 2026 due to factors such as back-to-office policies, new customers, and electric heating, before stabilizing by the end of the decade. Solar, DSM, and RTR are expected to reduce load, but these effects will be offset by new customer and EV load by 2031.

7 by comparison, fluctuating by at most ± 0.5 percent. 8 9 Figure 72 shows the breakdown in the contribution to peak by Commercial end use. 10 11 Figure 72: Commercial End-Use Peak Shares 12 13 14 15 As with the residential class, there is...

AI summary The text discusses the increasing contribution of electric vehicles (EVs) to peak electricity demand in the commercial sector, rising from 0.2% in 2026 to 10.7% in 2035 due to the electrification of space heating. Figure 72 illustrates the breakdown of commercial end-use peak shares.

MOVED) 2026 Load Forecast Report Appendix B Page 7 of 34 Residential SAE Model Fit Residential Model 2026-2036 Reconciliation The following tables provide details reflecting the changes between 2026 and 2036 forecast years. Some of the num...

AI summary The document provides a reconciliation of residential load forecasts from 2026 to 2036, showing changes in various factors such as existing customers, new EVs, solar, RTR, and DSM. The data highlights a slight decrease in average use from 2026 to 2036, with notable increases in new EVs and solar, and significant changes in DSM captured.

(464) (217) Change -0.1% 6.6% 12.1% -6.9% -0.9% -0.3% -4.3% 6.2% to load Res Sales = Existing Customer + New Customer + EV + Solar + RTR + Hybrid + DSM Existing customer load is calculated as Res Average Use (10,377 kWh/customer in 2026, 1...

AI summary The text discusses the calculation of residential load, including existing customer load and various components like heat, cooling, and energy efficiency savings. It provides data on average residential use and input variables for 2026 and 2036, along with percentage changes.

asses, especially General Demand). Class 2025 Forecast (GWh) 2026 Forecast (GWh) Residential 117 49 Commercial 176 255 Industrial 128 117 Total 421 421 12 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix E P...

AI summary The 2026 Load Forecast Report compares residential electricity demand forecasts for 2025 and 2026. Key factors influencing the change include less solar generation, higher EV sales, less RTR migration, lower heat pump penetration, and a delayed, weaker hybrid impact.

,966 1,354 13,296 3,052 29,961 553,222 10,354.23 14,600 4,100 2033 509,966 1,219 14,514 2,747 32,708 557,188 10,310.27 14,600 4,100 2034 509,966 1,097 15,612 2,472 35,180 560,757 10,316.49 14,600 4,100 2035 509,966 987 16,599 2,225 37,405...

AI summary The text contains a table with numerical data and a reference to a redacted 2026 Load Forecast Report. It mentions structural changes, new home forecasts, and the impact of programs like RTR and DSM, but no specific arguments or entities are discussed.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 11 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Year Cust Count Change in Res Customers Population Change in Population Housing Completions Starts Household Size Year...

AI summary This table presents data on customer counts, population changes, housing completions, and household sizes from 2015 to 2036. It provides a detailed forecast for load demand, including trends in residential customers, population growth, and housing starts over time.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 12 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Year Cust Count Change in Res Customers Population Change in Population Housing Completions Starts Household Size Year...

AI summary The document presents a table showing historical data on customer count, population changes, housing completions, and household size from 2004 to 2007. The data is part of a 2026 Load Forecast Report and is used for analyzing trends in residential energy consumption.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 34 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 2024 2025 billed 2025 accrued 2026 Residential WN (kWh) 21,157,507 33,365,662 -29,778,051 -5,732,680 -10,861,808 1...

AI summary The document presents a load forecast report for residential energy consumption from 2023 to 2036, including billed and accrued values, monthly breakdowns, and the impact of various factors such as solar, EVs, and DSM programs on total sales and energy usage.

REDACTED 2026 Load Forecast Report Synapse IR-1 Attachment 1 Page 186 of 196 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Year Heat WaterHt Cooking Misc Lighting TV Dryer Refrig Freezer Dish Washer EV Total 2026 Residential End Use Peak Sha...

AI summary The document provides a table showing the residential end use peak shares for 2026 and 2035, with data on various categories such as heat, water heating, cooking, and electricity usage for EVs. It highlights the percentage contributions of each category to the total load and the changes between 2026 and 2035.

Residential Commercial Industrial Municipal and Other Losses Total 2026 5,315 3,165 2,183 131 781 11,575 2036 5,646 3,213 2,168 259 810 12,097 Change 331 48 -15 128 17 522 Percent Change 6% 2% -1% 98% 2% 5% Source: Synapse from Table A1 fr...

AI summary The table presents a load forecast for residential, commercial, industrial, and municipal sectors in Nova Scotia for the years 2026 and 2036, showing increases and decreases in demand across different sectors. The data is sourced from Synapse's Table A1 from the 2026 Load Forecast.

3. ENERGY FORECAST NS Power forecasts load using different methodologies for different rate classes, reflecting the distinct drivers and data available for each. For the residential and commercial classes, NS Power uses Statistically Adjus...

AI summary NS Power uses different forecasting methods for various rate classes. Residential and commercial classes use SAE models, which combine end-use and econometric methods. Industrial classes use econometric models and customer surveys, while the municipal class is treated as an accounting matter due to third-party energy supply.

Table 4. Residential load: post regression (GWh) Existing Customer Average Use from Regression Model (kWh/year) New Cust. Load EVs Solar RTR Hybrid Res. DSM Adjust. Res Sales Total Res. DSM (at meter) DSM captured by end uses 2026 10,376 6...

AI summary Table 4 presents residential load forecasts for 2026 and 2036, including factors such as new customer load, EVs, solar, RTR, and DSM adjustments. The data shows changes in load across various categories, with some factors increasing and others decreasing.

Request IR-10: - Residential Water Heaters (WH) (Section 4.5.3, p. 43-44) - a. Refer to the following statement on page 43 of the NS Power 2026 Load Forecast Report: "This equates to an overall residential saturation of approximately 1.4 p...

AI summary The document requests NS Power to explain the assumptions and evidence used in forecasting the growth rate of heat pump water heater adoption, as well as to clarify whether energy efficiency improvements for these devices are assumed over time, including any Uniform Energy Factor (UEF) assumptions.

1 Request IR-1: 2 3 Reference: Exhibit N-1, Section 4.5.2. 4 5 (a) Please provide a narrative description of each of the "potential hybrid programs which 6 have been modelled by NS Power." (p. 37) 7 8 (b) Please provide the modelling refer...

AI summary Request IR-1 seeks detailed information from NS Power on hybrid heating programs, including modeling data, heat pump system types, energy reduction assumptions, and alignment with provincial electrification policies. It questions the methodology behind energy use forecasts, backup heating system usage, and the validity of hybrid program outcomes.