Topic:"Residential Demand Response Program" in M11108

Page 56 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 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 sales forecast is based on average use and customer count projections. Growth in residential sales between 2021 and 2022 was driven by factors such as continued COVID-19 restrictions, an increase in new customers, and the adoption of heat pumps. Figure 39 provides a comparison of forecast and actual sales data.

area and the resulting structural index are in Figure 41. 11 Future surveys will help to identify trends in this area. 12 13 Figure 41: Building Characteristics and Structural Index 14 Year BSE Heat EIA BSE Heat NS Floor Area (m2) Structur...

AI summary The text includes a table showing building characteristics and structural index data from 2023 to 2033, including metrics like BSE Heat EIA and BSE Heat NS, along with floor area and structural index values. It also references a load forecast report and mentions that future surveys will help identify trends in this area.

t Appendix B Page 5 of 33 Appendix B – Forecast Model Details Residential SAE Model Fit Residential Model 2023-2033 Reconciliation The following tables provide details reflecting the changes between 2023 and 2033 forecast years. Some of th...

AI summary This section provides a reconciliation of residential load forecasts from 2023 to 2033, showing changes in key metrics such as existing and new customer load, EV load, solar load, RTR, and DSM. The data highlights increases in customer load and EVs, while solar load decreases slightly.

(698) (402) Change 4.7% 7.8% 13.6% -9.2% -0.3% -5.6% 11.0% to load Res Sales = Existing Customer Load + New Customer Load + EV Load + Solar Load + RTR + DSM Existing customer load is calculated as Res Average Use (9,845 kWh/customer in 202...

AI summary The document discusses residential load forecasting, including factors like existing customer load, new customer load, EV load, solar load, and DSM. It provides data on residential average use, including calculations for 2023 and 2033, and highlights changes in load factors such as heating, cooling, and other variables.

1 Request IR-13: 2 3 Figure 42: Residential Sales Components by Year on page 60 of the Application project 54 4 GWh for New Customers in 2023 and 106 GWh for 2024. In the 2022 Load Forecast the 5 New Customer forecast was 56 GWh for 2022 a...

AI summary The request challenges the overstatement of new customer growth forecasts in residential sales, citing discrepancies between annual and cumulative figures. The response clarifies that the figures are cumulative, with incremental data provided to justify the forecasted growth.

New New Customers New Customers New Customers New Customers Residential with Heat Pump with Electric with Non Electric with Supplementary Year Customers Heat Baseboard Heat Heating Electric Heat 2023 6,177 3,706 1,544 927 2,162 2024 11,959...

AI summary The document presents data on new residential customers in Nova Scotia from 2023 to 2033, categorized by heating types, and references calculations related to heat pump saturation based on annual sales data from installers. It also mentions data sources provided by NS Po.

BNI sector. The base case reaches 185 MW in 2045. This represents 30% of economic potential in that year. In 2045, The low, mid and maximum achievable scenarios reach 130, 230 and 285 MW respectively. Figure 8-10. EE BNI Market Potential,...

AI summary The document discusses energy efficiency potential in Nova Scotia, focusing on the BNI sector and residential HVAC. It outlines market potential scenarios and highlights technologies like LED lighting and whole home retrofits that contribute to electricity savings.

12.1 Energy Efficiency • Near-term Electricity Savings: The majority of near-term savings are from the Res HVAC, Res Lighting, and BNI Lighting end uses. Residential screw-in LED Bulb ranks as the highest electricity- saving market potenti...

AI summary The text discusses energy efficiency in Nova Scotia, highlighting near-term electricity and winter peak demand savings from residential and BNI lighting and HVAC measures. It notes the success of EfficiencyOne in implementing energy efficiency programs and challenges posed by market saturation, tightening codes, and low net-to-gross ratios, particularly for lighting.

REGION GENDER AGE LOW INCOME HOME HEATING TYPE(S) INEXPENSIVE UPGRADES EXPENSIVE UPGRADES AGE OF HOME OVERALL % Other HRM Cape Breton Male Female 18-34 35-54 55+ Yes No Own Rent Oil Electricity Heat pump Familiar (7-10) Unfamiliar (0-6) Fa...

AI summary The text presents statistical data on demographics, income levels, home ownership, heating types, and age of homes across different regions in Nova Scotia. It includes percentages and sample sizes for various categories such as gender, age groups, low-income status, and heating type preferences.

(485) (14) 5,657 (297) 5,360 (698) (402) Change to 4.7% 7.8% 13.6% -9.2% -0.3% 16.5% -5.6% 11.0% Residential Average Use - Regression (includes coefficients) Xheat Xcool Xother EESavings Binaries ARMA Covid Total Average Use 2023 4,353 343...

AI summary The text presents statistical data and regression analysis related to residential energy use in Nova Scotia, including changes in average use, heat and cooling inputs, and coefficients affecting energy consumption from 2023 to 2033.

residential model and test, over a period of time, if alternative inputs make the residential model more robust, considering the following:

AI summary The text discusses the potential improvement of a residential model through alternative inputs over time, aiming to make it more robust.

ed? Why were they not chosen? Document #:303618 Date Filed: May 25, 2023 Synapse (NSPI) Page 3 of 18 1 g. What consideration if any was given to labour shortages or increased automation for the 2 industrial sector? 3 h. What would be the e...

AI summary The document contains regulatory requests for data on economic forecasts, residential end-use intensity trends, and heating/heat pump usage. It seeks clarification on inflation adjustments, forecast scenarios, data transformations, and customer heating patterns, emphasizing transparency in modeling assumptions and data consistency.