N-12026 Load Forecast Report - Redacted
8 passages
1 4.2 2025 Data Modifications Due to the Cyber Incident 2 3 NS Power’s billing processes were impacted in 2025 by the cyber incident and estimated monthly 4 billing was implemented for the residential, small general, general demand and mun...
AI summary NS Power adjusted 2025 billing and load forecast data due to a cyber incident. They used 2024 patterns to estimate monthly sales and historical data to estimate peak contributions, ensuring accurate forecasts without anomalies.
Page 37 of 105 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report REDACTED 1 The modelling uses a baseline residential electric‑heating load shape derived from AMI data for 2 the year 2022. Total household consumption wa...
AI summary The 2026 Load Forecast Report uses AMI data to model residential electric-heating load shapes, focusing on heat pumps and hybrid heating scenarios. It evaluates the impact of switching from electric to non-electric heating during peak events and low temperatures, scaling results based on participation assumptions and program implementation timelines.
1 comparison of energy and peak values predicted by the SAE models with and without the 2 residential hybrid heating representation showed that the hybrid-reduction did not fully match the 3 model results discussed above, because of differ...
AI summary The text discusses the adjustment needed in residential peak and energy forecasts due to differences in modeling approaches between the SAE models and NS Power’s own modeling. The impact of hybrid heating programs is now estimated using actual customer heat use values from AMI data, rather than estimates from E3 as in past forecasts.
https://ecologyaction.ca/sites/default/files/2023-05/RegionalZEVAdoptionOptions_Dunsky_March2023.pdf, 24 prepared by Dunksy Energy+Climate Advisors for the Ecology Action Center DATE: May 15, 2026 Page 45 of 105 REDACTED (CONFIDENTIAL INFO...
AI summary The 2026 Load Forecast Report discusses the impact of electric vehicles (EVs) on residential energy sales and peak demand based on analysis of customer-level AMI data. It estimates a per-customer load increase of 3820 kWh per year and a coincident peak impact of 0.39 kW for at-home charging. Commercial and MDV/HDV charging impacts are estimated using E3's EV Load Shaping Tool.
1 Figure 31: PV Impact to Energy (cumulative) Total Solar Total Load Year New Installs Load (GWh) Peak (MW) Installs (GWh) 2026 2,680 -31 0 15,767 -169 2027 5,622 -66 0 18,709 -204 2028 8,864 -105 0 21,951 -243 2029 12,439 -148 0 25,526 -2...
AI summary The text discusses the impact of solar photovoltaic (PV) installations on energy load and peak demand, noting that while solar generation reduces overall customer consumption, a significant portion of energy is still supplied by the utility. Additionally, net metering customers exhibit higher peak demands compared to non-net metering customers during both summer and winter periods.
Year d forecast forecast +364 2035 -875 -511 (42%) 8 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2026 Load Forecast Report Appendix E Page 9 of 21 Residential hybrid heating (1) • NS Power modelled the system impact of potential hybrid h...
AI summary The document discusses NS Power's modeling of residential hybrid heating programs using AMI data, considering various trigger scenarios and participation rates. The 2026 Load Forecast retains E3's peak-mitigation estimate but adjusts the energy reduction estimate to the median modelled outcome of -85 GWh by 2036.
come of -85 GWh by 2036 was therefore used for the 2026 forecast. • Hybrid heating represented as a distinct end-use in residential SAE regression model to reflect these modelled results. 9 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 202...
AI summary The document discusses the modeling of residential hybrid heating and heat pump electric water heaters, using AMI data and SAE regression models. It outlines the methodology for forecasting load based on participation rates and trigger conditions such as CPP events and temperature thresholds.
Using updated peak model 2025 Load Forecast, with from this Forecast (MW) updated input data (MW) 2026 Forecasted Peak 2,442 2,487 Interruptible -42 -42 Weather (-14.9oC 12hr lag avg, +38 +38 -15.1oC 24hr lag avg) Wind (14.1 km/h daily avg...
AI summary The 2026 Load Forecast Report updates the 2025 forecast using new input data, including recent installations of heat pumps, EVs, and solar, as well as policy and incentive changes. A hybrid impact model based on AMI data was incorporated, and heat pump water heaters were introduced as a separate end-use category in the residential model.
N-2NSPI (CA) RIR 1 to 6
4 passages
Load Forecast Report: 2021 2022 2023 2024 2025 Actual Demand Reduction Year 2021 2022 2023 2024 2025 2026 2027 n/a 2028 n/a 2029 n/a 2030 n/a 2031 n/a 2032 n/a 2033 n/a 2034 n/a 2035 n/a 1 (b) Please provide a brief explanation of the basi...
AI summary The text includes a table of load forecasts from 2021 to 2035 and a series of questions related to demand reduction, AMI technology, time-varying pricing (TVP), and peak demand events. The questions ask for explanations, confirmations, and details on the expected impact of these initiatives.
and stakeholder engaged manner as part of the Company's ongoing pricing innovation (as described in the 2024 Consensus Agreement, M11822). [4](#page-19-0) (d) System peak demand reduction has already been achieved by AMI technology-enabled...
AI summary The document discusses the impact of AMI technology-enabled TVP rates on system peak demand reduction, citing achievements and forecasts. It references the 2024 Consensus Agreement and provides data on peak demand periods based on OASIS data and the Tariff.
& lt;sup>6 Please refer to page 4 of the M11822 Consensus Agreement for further details. & lt;sup>7 M12499 – Board Decision and Order, 325082. December 16, 2025. 1 list of commitments and directives (2026/27 Work Plan, filed as Attachment...
AI summary The text discusses the evaluation of TVP Tariff performance and stakeholder input, including the refinement of metrics and mechanisms for feedback. It also raises questions about the relationship between peak load and sales, the impact of AMI data, and the feasibility of using multiple peak events for analysis.
winter morning or evening peaks), the selected peak hour may not reflect the intrinsic peak conditions of non-residential customers. As a result, non-residential coincident demand can appear higher or lower depending on when the system pea...
AI summary The analysis discusses the limitations of using a single peak hour to represent non-residential demand, highlights the benefits of AMI data in capturing coincident peak demand, and suggests using multiple peak events for more accurate analysis. NS Power supports this approach.
N-3NSPI (E1) RIR 1 to 11
5 passages
NON-CONFIDENTIAL 1 Request IR-1: 2 3 Reference: NS Power 2026 Load Forecast Report, page 40, lines 3-11 4 5 Based on the 2026 Load Forecast report, E1 understands that the hybrid heating 6 participation rate is based on a 50 percent adopti...
AI summary The text requests clarification and explanation from NS Power regarding the assumptions and methodology used in the 2026 Load Forecast Report, particularly concerning hybrid heating participation rates, energy and peak demand reductions, and the use of AMI data for revised estimates.
(d) NS Power did not consider using per-participant energy and demand impacts from the AMI analysis and applying them to the revised participation estimate to estimate the total energy reduction and peak demand reduction for the purposes o...
AI summary NS Power did not use per-participant energy and demand impacts from the AMI analysis to estimate total energy and peak demand reductions in the 2026 Load Forecast. No hybrid heating program is currently proposed, and the forecast uses values representative of a range of scenarios. Hybrid heating is considered a demand side management activity when participants are incentivized.
1 Request IR-2: 2 3 E1 has used data from the Load Forecast (total peak reduction, total energy reduction, and 4 cumulative participants) to calculate full load hours, unitary demand reduction, and unitary 5 energy reduction in Table 1. 6...
AI summary E1 has used data from the Load Forecast, including total peak reduction, total energy reduction, and cumulative participants, to calculate full load hours, unitary demand reduction, and unitary energy reduction as presented in Table 1.
1 2 (c) Please explain why the modelled hybrid heating program has an average annual 3 energy reduction of 3,034 kWh. 4 5 (d) What percent reduction does 3,034 kWh represent for: 6 7 (i) an average residential customer's total annual elect...
AI summary The text requests explanations and data related to a hybrid heating program's energy reduction impact, including calculations, methodologies, and underlying assumptions. It also asks for alignment with NS Power's characterization and sources for the program's energy and demand impacts.
(c) Please refer to CA IR-1 part (f). Hybrid event trigger scenario Event hours 17 (f) The adjustments are required in order to match realistic hybrid heating program impacts, 18 which were estimated based on modelling using empirical cust...
AI summary The text discusses the need for adjustments in hybrid heating program impacts based on empirical customer data and load shape modeling. It also raises concerns about the quality of AMI data used in the load forecast following a cyber incident and whether the 2022 load shape remains valid with changes in heat pump usage and customer behavior.
N-8Evidence - J. Wilson - CA
3 passages
II. Introduction and Summary - Q: Please summarize the topics you will address in your review of the 2026 Load Forecast Report. - A: My evidence reviews several technical or program-based adjustments to the base load forecast that I find d...
AI summary The review of the 2026 Load Forecast Report identifies several issues with technical and program-based adjustments, particularly the residential heating intensity forecast and forecasts for demand reduction programs. The reviewer recommends immediate corrections and improvements to forecasting practices, including revising forecast workbooks, rejecting the current hybrid heating forecast, and adjusting DLC, TVP, and BNI curtailment forecasts.
Q: Did NS Power implement Synapse's recommendation to validate heating intensity assumptions? A: No. In its decision on the 2025 Load Forecast Report, the Board endorsed NS Power's acceptance of Synapse's recommendations. Synapse recommend...
AI summary NS Power did not implement Synapse's recommendation to validate heating intensity assumptions with AMI or empirical data, despite the Board's endorsement of the recommendation. NS Power claims the assumptions are based on a 2022 Itron study, but the load forecast does not clearly reference this data. NS Power is currently working on techniques to use AMI data for evaluating heating loads.
Q: What is your recommendation to the Board? A: The Board should immediately reject NS Power's flawed residential hybrid heating forecast as unsupported by facts or sound reasoning. Normally, it would be reasonable for the Board to direct...
AI summary The respondent recommends that the Board reject NS Power's residential hybrid heating forecast due to its lack of factual support and sound reasoning. They suggest using NS Power's proposed hybrid heat pump adoption rate and energy savings data as a temporary basis for a more accurate forecast, while urging the use of AMI data for a comprehensive review by 2027.
N-10Rebuttal Evidence - NS Power
4 passages
the CA. Submissions were filed by the SBA. DATE FILED: September 8, 2026 Page 3 of 23 Nova Scotia Wholesale and Renewable to Retail Electricity Market Rules, effective 2007 02 01, amended 2016 06 01. M12861, NSEB, Hearing Order, 2026, Load...
AI summary NS Power has agreed to intervenor recommendations, including incorporating Advanced Metering Infrastructure (AMI) data into forecasting activities. This is described as a long-term process requiring data assessment and model development. The More Access to Energy Act outlines the IESO-NS's responsibilities in forecasting electricity demand and resource adequacy.
NS Power Response: At the time the hybrid heating assumptions were developed, NS Power relied on the best information reasonably available, including an analysis of AMI data for over 20,000 electrically heated NS Power customers and a stud...
AI summary NS Power explains that its hybrid heating assumptions were based on available AMI data and heat pump load studies. It acknowledges E1's DSM Potential Study and expects to use its findings in future load forecasts. NS Power argues against presenting specific hybrid heating participation scenarios, citing complexity and lack of clear benefit.
M12861, N-8, John Wilson Evidence, page 8, lines 25-32 and page 9, lines 1-2. on the difference between E3's hybrid and non-hybrid scenarios. This adjustment would allow NSPI to incorporate the mitigating effects of hybrid heating while pr...
AI summary The text discusses recommendations for improving NSPI's load forecasting by incorporating hybrid heating effects and validating assumptions using AMI data. It emphasizes the importance of modeling hybrid electric heating explicitly and using empirical data to enhance forecast accuracy and transparency.
NS Power Response: NS Power agrees that modeling the impact of hybrid heat pumps should be included in the underlying residential intensity calculations and will update the calculations accordingly for 2026. Where possible [emphasis added]...
AI summary NS Power agrees to include hybrid heat pump modeling in residential intensity calculations for 2026 and will use AMI data where possible. They also agree to develop the capability to model hybrid commercial heating in future forecasts but not for 2026. NS Power disputes the claim that their hybrid heating forecast contradicts the Board's directive in the 2025 Load Forecast Report proceeding.