N-12025 Load Forecast Report + Appendices - Redacted
10 passages
............................................. 74 16 Figure 58: Historical and Forecast Annual NSR ........................................................................ 75 17 Figure 59: Forecast Components ..................................
AI summary The text lists figures related to energy demand forecasting, demand response programs, peak load analysis, and the impact of electric vehicles. Topics include system reliability, load management, and integration of renewable energy sources through advanced metering infrastructure.
8 30 Figure 72: 2024 Monthly class sums from AMI data vs System Generation ............................. 89 31 Figure 73: Example of time-varying EV effect detection in AMI data ....................................... 91 32 Figure 74: Syst...
AI summary The document outlines the 2025 Load Forecast Report, referencing figures analyzing AMI data, system generation, EV effects, and energy sensitivity. It includes attachments and appendices detailing residential/commercial demand models, forecast comparisons, and stakeholder presentations, with partial confidentiality noted.
Page 15 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.0 DISCUSSION OF MAJOR INPUTS 2 3 4.1 Historical Class Sales and Energy Data 4 5 The Load Forecast is developed using NS Power’s “billed” sales...
AI summary NS Power's 2025 Load Forecast Report explains the use of 'billed' sales data over 'accrued' sales due to billing delays, with a transition to AMI-based accrued sales pending sufficient historical data. Residential/commercial forecasts use 2015-2024 billed sales, while industrial forecasts use extended periods for improved model fit. Peak demand data is derived from hourly load data.
1 EVs on the road by 2035, mostly made up of LDVs compared to a forecast of 200,000 vehicles by 2 2035 in the 2024 Load Forecast. 3 4 The impact of EVs on residential energy sales and peak (reflecting at-home charging) was analysed 5 using...
AI summary The text discusses the analysis of the impact of electric vehicles (EVs) on residential energy sales and peak demand using AMI data from 2023 and 2024. Customers were divided into two groups: new EV owners and a control group. The method compares monthly energy consumption across years to estimate the EV effect on energy usage and peak demand.
Page 87 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 create these figures, the sum of all the available AMI meters, per hour, per class, is later adjusted 2 so the monthly totals correspond to reve...
AI summary The 2025 Load Forecast Report discusses the use of AMI data to improve the accuracy of load shape estimates and system generation comparisons. The report highlights how AMI data provides more detailed and accurate hourly resolution, leading to smoother load shape curves and better alignment with system generation data.
2025 Load Forecast Report Redacted 1 Figure 72: 2024 Monthly class sums from AMI data vs System Generation 2 3 10.5 AMI Data Used in the 2025 Forecast 4 5 AMI data was used to create class-level load shapes for 2024, offering a more comple...
AI summary The 2025 Load Forecast Report discusses the use of AMI data to improve load forecasting by providing more accurate customer consumption patterns. It highlights improvements from analyzing EV impact and consumption differences between single-family and multi-unit residential buildings.
Page 89 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Grid NS project, or data obtained from other regions of the world where driving characteristics 2 and public charging infrastructure may differ...
AI summary The text discusses the use of AMI data in analyzing EV charging patterns and SFU/MURB consumption, highlighting the importance of data granularity and integration with external datasets. It also outlines future research directions, such as assessing the impact of electric heating on load shapes and refining forecasting models.
the 18 forecasting SAE models. 19 DATE: June 27, 2025 Page 90 of 94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 Figure 73: Example of time-varying EV effect detection in AMI data 2 DATE: June 27, 2025 P...
AI summary The text references a 2025 Load Forecast Report, which includes a redacted figure showing an example of time-varying EV effect detection in AMI data. The report is part of a regulatory proceeding and contains confidential information that has been removed.
ad, timing pushed out Residential COVID variable removed from forecast COVID Variable timeframe, commercial variable removed entirely 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 6 of 19 Average C...
AI summary The document discusses updates to residential load forecasting, including the removal of the residential COVID variable and the adjustment of average consumption estimates for new customers based on AMI and customer segmentation data. The forecast also incorporates the Conference Board of Canada’s housing completion forecasts, adjusted for historical underestimation.
17 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix E Page 18 of 19 2024 vs 2025 Peaks • The warmer-than-normal weather in 2024 resulted in a lower than forecast peak load, as well as a small number of high-lo...
AI summary The 2025 Load Forecast Report highlights differences in peak load between 2024 and 2025, noting colder-than-average weather in 2025 led to more high-load days. Ongoing work includes integrating AMI data, evaluating electrification impacts, and assessing new technologies like time variable pricing and direct load control.
N-7NSPI (Synapse) RIR 1 to 29 - Redacted
6 passages
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-12: 2 3 Prevalence of AMI (Section 10.4, pp. 88-89) 4 5 (a) Please provide the percent of customers with AMI for each of t...
AI summary NSPI provided data on AMI prevalence in customer classes for the 2025 Load Forecast Report (NSEB M12349), detailing percentages and the breakdown of the 'Rest' category.
114 125 433 395 31,941 2,072 34,013 45,095 543 2032 349 382 141 155 536 490 39,612 2,570 42,182 53,264 646 2033 426 467 173 189 656 599 48,438 3,142 51,579 62,661 766 2034 508 556 206 225 781 714 57,700 3,742 61,442 72,524 891 2035 593 650...
AI summary The document references the 2025 Load Forecast Report (NSEB M12349) and NSPI's response to Synapse Information Request IR-16, specifically regarding the source data and calculations for the values in Figure 68. The response includes an attachment related to the 'Previous Coincidence Factor.'
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 Appendix B: Residential Model 4 5 (a) Please provide in electronic spreadsheet format the data and the statistical...
AI summary NSPI responded to Synapse's information requests regarding the 2025 Load Forecast Report, providing details on the residential model, including statistical parameters, data, and factors influencing HP Heat, HP Cool, and Other variables. The response also outlined the forecasted HP Heat share for 2025 and 2035.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) Please provide NSPI’s own forecast of space heating stock saturation from 2 2024 to 2050 for both heat pumps and electric resist...
AI summary The document contains information requests from the NSEB to NSPI regarding the 2025 Load Forecast Report, specifically concerning space heating stock saturation forecasts, hybrid heating systems, and modeling assumptions in the SAE framework. It also requests details on how peak load impacts were estimated.
conducted any surveys or studies to confirm actual heating 29 system usage post-installation for hybrid heating systems? If so, please provide 30 the surveys or studies. Date Filed: August 19, 2025 NSPI (Synapse) IR-26 Page 4 of 10 REDACTE...
AI summary The document requests information on whether NS Power has conducted surveys or studies on hybrid heating system usage post-installation and whether AMI data has been used to analyze heat pump load patterns. NSPI responds by referring to Attachment 1 and explains assumptions made regarding backup heating sources for heat pumps.
2025 Load Forecast Report (NSEB M12349) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) NS Power does not use data from E1 regarding heat pump installations subject to 2 rebate programs, as it provides only partial d...
AI summary NSPI states that it does not use data from E1 regarding heat pump installations due to incomplete data, and instead collects data directly from contractors. NSPI has not conducted a dedicated analysis of AMI data for heat pump load patterns due to the complexity of disaggregation, though it is working on techniques to evaluate heating loads using AMI data.
N-8Evidence - Synapse
7 passages
5 Load Forecast 13 improvement rate to just 0.1 percent per year, based on a source that appears to come from U.S. Energy Information Administration (EIA), but is not actually specified by NSPI. 25 NSPI’s approach to efficiency improvement...
AI summary The text critiques NSPI's load forecasting methodology for underestimating efficiency gains from heat pump adoption and relying on unspecified EIA data. It supports NSPI's planned use of AMI data to improve forecasting accuracy by analyzing end-use technologies' impacts on load shapes.
ommendations in previous years. It represents an important step toward improving the accuracy and transparency of NSPI’s load forecasting by grounding assumptions in observed customer usage patterns. Recommendations and considerations For...
AI summary The text recommends improving NSPI’s load forecasting by adjusting heat pump models with scaling factors based on E3 scenarios, developing explicit hybrid heating modeling, and validating assumptions using AMI data. It emphasizes long-term modeling improvements and supports NSPI’s commitment to analyzing AMI data for accuracy and transparency.
ends to focus on demand response in the next ELCC study. 62 In responses to discovery, NS Power clarified that it may not be possible to incorporate the next ELCC study into the 2026 Load Forecast. 63 We appreciate that NS Power is elevati...
AI summary The text discusses concerns regarding Nova Scotia Power's (NS Power) use of the Energy Load Contribution Credit (ELCC) factor for demand response (DR) in its load forecasts. Synapse Energy Economics Inc. (Synapse) raises concerns about the outdated basis of NS Power's ELCC assumptions and the lack of consideration for interactive effects and future electrification impacts. Recommendations are made for NSPI to conduct a portfolio ELCC analysis and consider more demand response programs.
nd timing with the presence of electric heating. Research targeted at specific end uses may also allow for potential refinements of saturation and intensity values used in the forecasting SAE models.” We are very supportive of NS Power’s i...
AI summary The document discusses the use of AMI data to improve load forecasting and presents a sensitivity analysis in the 2025 Load Forecast. The analysis highlights the impact of weather, economic drivers, and DSM on energy and peak demand forecasts. The importance of evaluating hydrogen production facilities and battery adoption scenarios is emphasized.
Evidence Regarding Nova Scotia Power’s 2025 Load Forecast 26 5. QUESTIONS AND RECOMMENDATIONS In this Evidence we ask for further clarifications and make several recommendations: 1. The impacts of trade tariffs are an ongoing uncertainty....
AI summary The document requests clarifications and recommendations regarding Nova Scotia Power’s 2025 load forecast, emphasizing the need to account for trade tariffs and improve heat pump modeling. It suggests incorporating trade tariff impacts into economic forecasts and refining heat pump load assumptions using AMI data for greater accuracy and transparency.
ces. We strongly support NSPI’s new commitment to analyzing AMI data and encourage the utility to prioritize this effort as it works to refine its modeling of electric heating impacts. 3. We recommend that NSPI begin modeling heat pump wat...
AI summary The text emphasizes the importance of modeling heat pump water heaters as a separate end use technology in load forecasts, monitoring the impact of the carbon levy removal on EV sales, and updating solar installation projections. These actions are recommended to improve forecast accuracy and align with electrification goals.
in particular heat pumps, EVs, DSM, and demand response, we again recommend that NSPI develop a few different scenarios (e.g., low case and high case) in addition to the reference case. We support NSPI’s ongoing efforts to improve the tran...
AI summary The text discusses recommendations for improving NSPI's load forecast, particularly regarding the modeling of heat pumps, EVs, DSM, and demand response. It suggests exploring different scenarios and increasing DSM levels, as well as modeling heat pump water heaters as a separate end-use technology.
98721Synapse (NSPI) IR-1 to IR-29
4 passages
ort. 3 h. Please confirm whether managed charging is incorporated into the 2025 forecast. If it is 4 not, please explain. If NS Power has forecasted managed charging, please provide this 5 information. 6 Request IR-10: 7 Price Forecast (Se...
AI summary The text outlines regulatory requests to NS Power regarding managed charging forecasts, electricity price increases, renewable-to-retail (RTR) impacts on peak load, AMI prevalence, ELCC study timelines, and DSM potential based on E1’s 2019 study. Requests focus on clarifying assumptions, data sources, and timelines for key planning documents.
he two forecasts? The adjustments imply 19 that NSPI is essentially using E3’s HP analysis results. What is the role of NSPI’s 20 own heat pump energy and peak estimates? 21 h. Refer to Figure 24 regarding heat pump forecast and the “2025...
AI summary The text raises questions about NSPI's use of E3's heat pump analysis, methodology for estimating heating intensities, data sources, and coordination with EfficiencyOne. It also inquires about AMI data usage for EV, PV, and residential consumption/savings tracking.
ugh rebate programs? 33 i. The report mentions the use of AMI data to meter actual consumption or savings data 34 associated with EV (p. 39), PV (p. 43) and single-family homes (page 59).
AI summary The report discusses the use of Advanced Metering Infrastructure (AMI) data to track actual consumption and savings associated with electric vehicles, photovoltaic systems, and single-family homes.
Date Filed: 07/28/2025 Synapse (NSPI) Page 10 of 12 1 i. Has NS Power conducted any analysis of AMI data to verify hourly or seasonal 2 load patterns of customers with heat pumps? 3 ii. If so, please provide the methodology, sample size, a...
AI summary The document outlines regulatory requests from Synapse (NSPI) seeking data on AMI analysis for heat pump load patterns, water heater efficiency forecasts, and DSM values. It emphasizes methodological transparency, data sources, and validation of assumptions in NSPI's load forecasting models.