N-12022 Load Forecast Report - Redacted
8 passages
Total New Year Load (GWh) Peak (MW) Installs 2022 2,610 -24 0 2023 3,947 -36 0 2024 5,351 -49 0 2025 6,825 -63 0 2026 8,505 -78 0 2027 10,420 -95 0 2028 12,604 -115 0 2029 15,093 -138 0 2030 17,931 -164 0 2031 20,724 -185 0 2032 23,069 -20...
AI summary The document discusses the projected load growth from 2022 to 2032, noting that distributed solar and battery storage combinations are not significantly assumed in the 2022 Load Forecast. It highlights the high cost of home batteries compared to gas generators, with the latter being more cost-effective for backup power. New pricing mechanisms like CPP and TOU may encourage battery use, but current costs remain high.
ase at an average of 2 percent per year, 17 or around the rate of inflation, which results in a flat profile in real terms. Figure 35 shows 18 price forecasts by class. 19 19 M09288, NS Power 2020-2022 Fuel Stability Plan, NSUARB Decision,...
AI summary The document discusses electricity price forecasts and their impact on sales, referencing historical and projected real electricity prices. It notes that prices are increasing at an average of 2% annually, similar to inflation, and mentions the use of price elasticity estimates in SAE models to predict sales changes.
n of the different components for 2019, 2020 14 and 2021 actuals vs forecast and weather normalized totals, and the 2022 forecast. 15 16 Figure 37: Comparison of Forecast to Actuals 17 Year 2019 2020 2021 2022 Forecast Sales 4551 4540 4718...
AI summary The text compares forecasted and actual electricity sales for 2019, 2020, 2021, and 2022, highlighting the impact of weather and non-weather factors, particularly the influence of the COVID-19 pandemic on residential electricity usage and forecasting assumptions.
quirements) on many commercial sectors including 23 retail and restaurants. Schools have largely remained open since the fall of 2020, except 24 for several universities that chose to deliver classes online for the 2020/2021 school year. 2...
AI summary The document discusses the impact of the COVID-19 pandemic on commercial energy sales in Nova Scotia, noting a significant drop in 2020 and 2021, followed by an expected rebound in 2022. It also compares commercial energy sales to GDP and employment indicators, showing a similar decline in all three metrics during the pandemic.
l as shown in Figure 55, but the 10 year period aligns 12 with the annual HDD estimate and provides a better reflection of current weather trends. 13 14 Figure 55: Peak Temperatures 15 Time Avg Evening Avg Avg Annual Avg Daily Period Peak...
AI summary The text discusses the alignment of a 10-year period with annual heating degree day (HDD) estimates and highlights trends in minimum temperatures over the past 30 years. It also outlines the method for calculating peak contributions from large customer classes and presents a forecast for system peak demand from 2022 to 2032.
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 the dependent variable is sales. The model includes various coefficients and statistical values for different variables, such as heating, cooling, and energy efficiency savings, as well as seasonal and event-specific factors.
2022 Load Forecast Report Appendix B Page 13 of 32 Appendix B – Forecast Model Details Small General Load – Post Regression (GWh) Load from NS Power C&I Solar SG DSM Small Gen Total DSM Regression Electrification adjustment Sales (with SG...
AI summary The 2022 Load Forecast Report Appendix B outlines the calculation of small general load using a regression model. It includes inputs such as average use per customer, customer count forecasts, and adjustments for programs like DSM. The model projects changes in load from 2022 to 2032, including variables like XHeat, XCool, and XOther.
Appendix B Page 17 of 32 Appendix B – Forecast Model Details General Service Model Fit General Demand 2022-2032 Reconciliation The general demand class, which makes up the largest portion of the commercial sector, is forecast as gross tota...
AI summary This section discusses the General Demand 2022-2032 Reconciliation, focusing on forecasting methods for the general demand class in the commercial sector. It mentions the use of a flat scaling factor and adjustments for NS Power commercial growth programs, including heat pumps, PV, and DSM.
N-2NSPI (CA) RIR-1 to RIR-17 - Redacted
7 passages
2022 LFR CA IR-14 Attachment 2 Page 6 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 CDD18 0 232.686 -97.305 358.84 Summary Jun 2021 heating load 7323 MWh Normal heating load 6995 MWh 2021 Varinace to Normal 328 MWh
AI summary The document provides variable coefficients for different heating and cooling metrics, along with a summary of June 2021 heating load, normal heating load, and the variance between them. These figures are used for analysis in a regulatory proceeding.
0 0 0 0 0 7 29 0.0 0.0 1.4 0 0 0 0 0 799 799 30 0.0 0.00 0.00 0 0 0 0 0 0 0 7 30 0.0 0.0 1.7 0 0 0 0 0 956 956 31 0.0 0.00 0.00 0 0 0 0 0 0 0 7 31 0.0 0.0 2.7 0 0 0 0 0 1498 1498 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-1...
AI summary The text provides data on heating load for August 2021, showing a total of 32,073 MWh, which is 6,286 MWh higher than the normal heating load of 25,787 MWh. It also includes variable coefficients and HDD (Heating Degree Days) values, indicating factors influencing heating demand.
0.0 0.0 1.4 0 0 0 0 0 750 750 31 0.0 0.00 3.23 0 0 0 0 0 1784 1784 8 31 0.0 0.0 0.9 0 0 0 0 0 486 486 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 9 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag...
AI summary The document provides data on heating load in September 2021, showing a significantly lower load of 3294 MWh compared to the normal heating load of 6039 MWh, with a variance of -2745 MWh. Coefficients for variables such as HDD13, lag1, and CDD18 are also listed.
11 28 0.5 9.2 3217 2143 -46 1439 365 7118 29 0.00 7.65 2674 1781 0 1934 313 6701 11 29 0.6 11.4 3972 2646 -62 1630 358 8543 30 0.00 11.88 4148 2763 0 1355 481 8747 11 30 1.6 12.1 4234 2820 -152 2013 405 9320 REDACTED (CONFIDENTIAL INFORMAT...
AI summary The document provides data on heating load for December 2021, showing a total heating load of 318,539 MWh, which is lower than the normal heating load of 327,335 MWh by 8,796 MWh. It also includes variable coefficients and other metrics related to heating degree days and lagged values.
and Demand Forecast (2020 Load Forecast Report) (NSUARB M09707) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 On page 9 of 137, NS Power reports the 2020 growth in system peak at 8.9% and the prior 4 y...
AI summary The document contains a request from NSUARB to NS Power regarding discrepancies in load forecasts and actual system peak growth, particularly focusing on the 2019 forecast and actual figures, and requesting detailed explanations and supporting data related to lighting load and temperature impacts.
fact, and the variance is driven by individual behaviours, temperature and weather in the 17 days before, patterns that may be related to specific days of the week (for example, more Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 2 of 3...
AI summary The text discusses energy and demand forecast trends, noting differences between morning February/March peaks and evening December/January peaks. It attributes the variance to factors like lighting load and temperature sensitivity, estimating a difference of around 150 MW and a temperature sensitivity of approximately 30 MW per degree Celsius.
1 Request IR-15: 2 3 According to Exhibit N-34, Matter No. M10431, Response to CA IR-41, Attachment 1, NS 4 Power has developed scaled class load shapes for 2019 using its load research sample. The 5 Report indicates that these data have b...
AI summary The request asks NS Power to provide updated load shapes, loss estimates, and explanations regarding the use of loss factors in their load forecasts. The response refers to confidential attachments and indicates that losses are calculated at the system level, not by class.
N-4NSPI (NSUARB) RIR-1 to RIR-36
5 passages
82,761 62 38 32 1,119 42 172 2022 98,761 62 38 35 1,214 46 185 2023 114,935 63 37 38 1,310 49 198 2024 131,433 64 36 41 1,408 52 212 2025 148,095 65 35 44 1,506 56 226 2026 164,924 65 35 47 1,605 59 239 2027 181,922 65 35 50 1,705 63 253 2...
AI summary The document presents a 10-year energy and demand forecast from 2022 to 2032, including figures for energy consumption, demand, and related metrics. It is part of NSPI's response to NSUARB information requests and is labeled as non-confidential.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 With reference to Section 5.0 Residential Sector, the application discusses the model used...
AI summary NSPI responds to NSUARB's information request regarding the residential sales forecast model. They explain that the price of home heating oil was excluded as it was not statistically significant. They also note that housing completions data comes from the Conference Board of Canada, and they lack information on how government commitments to affordable housing will affect forecasts.
y and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 With reference to Section 5.0 Residential Sector, page 59 of 98, Figure 37 Comparison of...
AI summary NSPI explains the weather-adjusted sales metric, which adjusts actual sales to reflect what sales would be under normal weather conditions. In 2019, actual sales were higher due to colder than normal temperatures, and the adjustment subtracts the weather impact to provide a normalized sales figure for comparison with forecasts.
nergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-25: 2 3 With reference to Section 5.0 Residential Sector, page 59 of 98, the application stat...
AI summary NSPI responded to an information request regarding the impact of COVID-19 on residential energy sales forecasts. They noted that the +100 GWh impact in 2022 and +54 GWh for 2023 and beyond is higher than the +42 GWh impact in 2021, attributing this to unexplained variances and weather normalization adjustments.
rgy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-30: 2 3 With reference to Section 6.1 Small General Service, page 67 of 98, the application sta...
AI summary NSPI explains that the change in the growth rate for Small General Service load is due to the starting point of the forecast. The 2021 forecast used 2021 sales of 293 GWh, while the 2022 forecast uses 2022 sales of 311 GWh, reflecting expected economic recovery.
N-7Refiled NSPI (CA) RIR 1 to RIR-17 - Redacted
13 passages
(b) No. Both provincial and municipal governments have discussed targets related to 29 affordable housing and population growth, but no concrete policies or programs have been Date Filed: July 8, 2022 NSPI (CA) IR-8 Page 1 of 2 REDACTED (C...
AI summary NSPI states no concrete policies exist for affordable housing, with housing forecasts relying on Conference Board data. New customer load estimates consider electric heating and building efficiency. Population impacts are modeled via economic variables, not explicitly.
the data series. The series is based on the 29 Conference Board of Canada’s February 5-year forecast plus their 20-year forecast produced 30 in January for the years beyond 2026. Date Filed: July 8, 2022 NSPI (CA) IR-10 Page 1 of 2 REDACTE...
AI summary NSPI's 10-Year Energy and Demand Forecast (2022 Load Forecast Report) relies on the Conference Board of Canada's 5-year and 20-year forecasts. The report compares energy sales to manufacturing GDP, noting historical alignment and no anticipated material changes in the forecast period. The document is part of NSPI's responses to the NSUARB (M10569).
1 Response IR-14: 2 3 (a) Weather normalized (WN) sales and net system requirement are provided in the figure 4 below: 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WN Sales 9,635 10,362 10,352 10,318 10,023 10,136 10,419 10,262 10,146...
AI summary The response provides weather normalized sales and net system requirement data from 2012 to 2021, along with an explanation of the weather adjustment methodology used in forecasting. The forecast models assume normal weather, while historical data includes actual weather variables.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 DailyEnergy = Constant + b1×HDD13 + b2×HDD0 + b3×Lag1HDD13 + 2 b4×Lag2HDD13 + b5×JanHDD13 + b6×FebH...
AI summary The text describes a model used to calculate daily energy demand based on temperature variables, including HDD and CDD factors. It references attachments containing model inputs, outputs, and coefficients, and explains the allocation of weather impact across residential, commercial, and municipal sectors. The normalization factor for weather adjustments was updated from 20 MW/degree to 25 MW/degree in 2016, with a revised figure provided for 2019.
degree since 2016. The adjustment for the morning 20 peak in 2019 in Figure 60 was inadvertently omitted from the Report and a copy of the 21 revised figure is provided below. 22 Date Filed: July 8, 2022 NSPI (CA) IR-14 Page 3 of 4 REDACTE...
AI summary The text discusses an adjustment to the 2019 morning peak load in a 10-Year Energy and Demand Forecast, which was omitted from the original report. The adjustment was attributed to differences between morning and evening peak loads, not solely lighting. Actual and weather-adjusted 2021 sales data are also provided.
0.0 0.00 4.61 0 0 0 0 0 1655.15 1655 6 28 0.0 0.1 0.4 0 23 0 0 0 129 152 29 0.0 0.00 0.00 0 0 0 0 0 0 0 6 29 0.0 0.0 0.0 0 9 0 0 0 4 13 30 0.0 0.00 0.00 0 0 0 0 0 0 0 6 30 0.0 0.0 0.9 0 0 0 0 0 305 305 REDACTED (CONFIDENTIAL INFORMATION RE...
AI summary The document provides data on heating load for July 2021, showing a total of 12,226 MWh, which is significantly lower than the normal heating load of 22,598 MWh, resulting in a variance of -10,373 MWh. Variable coefficients and other metrics are also presented.
0.0 0.0 1.4 0 0 0 0 0 750 750 31 0.0 0.00 3.23 0 0 0 0 0 1784 1784 8 31 0.0 0.0 0.9 0 0 0 0 0 486 486 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR CA IR-14 Attachment 2 Page 9 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag...
AI summary The document provides data on heating load for September 2021, showing a heating load of 3294 MWh, which is significantly lower than the normal heating load of 6039 MWh, with a variance of -2745 MWh. Variable coefficients and other metrics are also included.
0 0 40 28 0.00 0.00 0.00 0 0 0 0 0 0 0 9 28 0.0 0.4 0.16 0 98 0 0 0 53 151 29 0.00 0.00 0.00 0 0 0 0 0 0 0 9 29 0.0 0.3 0.29 0 58 0 0 0 97 155 30 0.00 0.00 0.00 0 0 0 0 0 0 0 9 30 0.0 0.8 0.32 0 195 0 0 0 107 302 REDACTED (CONFIDENTIAL INF...
AI summary The text provides data on heating load for October 2021, showing a load of 30,150 MWh, which is significantly lower than the normal heating load of 49,475 MWh, with a variance of -19,325 MWh. It also includes variable coefficients and other statistical data related to heating degree days and lagged variables.
11 28 0.5 9.2 3217 2143 -46 1439 365 7118 29 0.00 7.65 2674 1781 0 1934 313 6701 11 29 0.6 11.4 3972 2646 -62 1630 358 8543 30 0.00 11.88 4148 2763 0 1355 481 8747 11 30 1.6 12.1 4234 2820 -152 2013 405 9320 REDACTED (CONFIDENTIAL INFORMAT...
AI summary The document presents heating load data for December 2021, showing a total heating load of 318,539 MWh, compared to a normal heating load of 327,335 MWh, resulting in a variance of -8,796 MWh. It also includes statistical coefficients and variables related to heating degree days and lagged values.
ciated with a 0.2 degree Celsius 23 variance in peak, assuming a weekday with full lighting load. 24 (v) What, if any, other factors impacted the 2019 variance? 25 Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 1 of 3 REDACTED (CONFIDEN...
AI summary The 2019 variance in energy demand was primarily influenced by differences in lighting load and the timing of the peak (morning vs. evening). Variance at specific temperatures can reach up to 200 MW, driven by individual behaviors, temperature, and weather patterns.
fact, and the variance is driven by individual behaviours, temperature and weather in the 17 days before, patterns that may be related to specific days of the week (for example, more Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 2 of 3...
AI summary The text discusses energy and demand forecast trends, noting differences between morning and evening peaks influenced by factors like lighting load and temperature sensitivity. Variance is attributed to behavioral patterns, weather, and specific days of the week, with a normalized peak estimate of around 2100 MW.
1 Request IR-15: 2 3 According to Exhibit N-34, Matter No. M10431, Response to CA IR-41, Attachment 1, NS 4 Power has developed scaled class load shapes for 2019 using its load research sample. The 5 Report indicates that these data have b...
AI summary The request asks NS Power to provide updated scaled class load shapes, estimates of monthly losses by class, and an explanation for using 2013 loss factors instead of more recent data. The response refers to confidential attachments and explains that losses are calculated at the system level, not by class.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-16: 2 3 Reference Report p. 77. We understand NS Power’s argument to be as follows: Using the...
AI summary The Consumer Advocate requests clarification on NSPI's use of the 25 MW/°C demand change estimate in weather normalizing load forecasts. NSPI confirms the estimate is not used in the forecast model, and clarifies that weather normalized values are used for explanation and comparison, not as inputs to the model.