N-12023 Load Forecast Report + Appendecies - Redacted
14 passages
..................................................... 14 6 4.0 Discussion of Major Inputs ................................................................................................ 16 7 4.1 Historical Class Sales and Energy Data .......
AI summary The document outlines sections discussing historical energy data, weather impacts, economic factors, end-use trends, price data, demand-side management, and sector-specific analyses for residential and commercial sectors in a regulatory proceeding.
1 List of Figures 2 3 Figure 1: Historical and Predicted Annual Net System Requirement ............................................ 7 4 Figure 2: Historical and Predicted Annual System Peak ....................................................
AI summary The document lists figures related to historical and predicted energy system requirements, peak demand, heating/cooling degree day trends, temperature regression models, and geographic weather station data. These visualizations support forecasting methodologies and energy usage pattern analysis for system reliability planning.
red by Hour per EV ............................................... 42 31 Figure 29: Peak Demand of ChargePoint EV Charging Fleet ...................................................... 43 32 Figure 30: EV Impact to Energy and Peak Forecasts...
AI summary The document contains a list of figures related to energy demand forecasting, EV charging impacts, solar PV effects, battery potential, residential and commercial electrification trends, and historical vs projected electricity prices. The text is redacted, with confidential information removed, and spans multiple pages of analysis.
DATE: April 28, 2023 Page 3 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary A redacted section from the 2023 Load Forecast Report, dated April 28, 2023, page 3 of 98. The document contains confidential information removed, with no visible content beyond the title and page reference.
1 Figure 43: Illustrative Contribution of Specific End Uses............................................................ 61 2 Figure 44: Commercial Class Sales ...................................................................................
AI summary The text lists figures illustrating energy sales, demand forecasts, temperature regression models, and economic indicators. It includes historical and projected data for residential, commercial, and industrial sectors, along with demand response and peak temperature analysis.
Figure 72: Monthly historical Residential LRS load at peak and forecasts .................................. 93 31 Figure 73: AMI Peak Estimates ..................................................................................................
AI summary The document outlines the 2023 Load Forecast Report, including attachments with residential and commercial demand models, forecast classes, and appendices covering NS Power forecasts, model details, comparisons, and stakeholder presentations. Most content is redacted, with figures and appendices listed but not detailed.
erage annual increase of 0.7 14 percent. Annual historic and forecast NSR are shown below in Figure 1. 15 16 Figure 1: Historical and Predicted Annual Net System Requirement 17 18 DATE: April 28, 2023 Page 7 of 98 REDACTED (CONFIDENTIAL IN...
AI summary NS Power's 2023 Load Forecast Report projects a 0.7% annual increase in Net System Requirement (NSR) and a 2.3% annual rise in system peak demand, driven by customer growth, electrification, and EV adoption. Demand Side Management (DSM) and Demand Response (DR) programs are expected to mitigate some of this growth.
e in the province, re-evaluate the 29 use of housing completions for the near-term; 30 31 • Given the current inflationary environment, evaluate the use 32 of Median Household Income in place of Total Household 33 Income, as it is biased b...
AI summary The document outlines three actions for improving load forecasting: re-evaluating housing completions, using median household income over total income to address inflationary biases, and incorporating household demographics (size, age) to refine demand patterns by time of day. Data sources include the Conference Board of Canada and Statistics Canada.
e 16 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 of energy through wind production, with 10 percent serving residential customers, 50 2 percent serving commercial customers, 30 percent serving ind...
AI summary The 2023 Load Forecast Report details energy distribution by customer type (10% residential, 50% commercial, 30% industrial, 10% losses) and explains weather data's impact on electricity sales via HDD/CDD metrics. Peak forecasts remain unchanged as NS Power must serve full peak demand regardless of energy source allocation.
od 19 January 2013 to December 2022. The average temperature continues to show a warming 20 trend: the 30-year average annual HDD is 3,892 while the 10-year average is 3,782. 21 NSUARB that if no electricity is sold to a customer under the...
AI summary The 2023 Load Forecast Report analyzes climate trends impacting Heating Degree Days (HDD) and Cooling Degree Days (CDD), showing a decline in HDD (-17/year) and increase in CDD (+1.4/year) due to warming. The NSUARB requires licensed retail suppliers to demonstrate justification for not selling electricity by 2024 (M10293).
Figure 7: HDD Trend 8 9 10 DATE: April 28, 2023 Page 19 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 8: CDD Trend 2 3 4 5 These trends are reduced over time (approximately 40 years) such tha...
AI summary The 2023 Load Forecast Report analyzes HDD and CDD trends, projecting a 40-year reduction in annual HDD to 3,638 by 2033 and a rise in CDD to 124. This shift implies reduced winter heating demand and increased summer cooling demand for residential and commercial sectors, with leap year anomalies noted in 2024, 2028, and 2032.
3.1 0.3 3 4 DATE: April 28, 2023 Page 31 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of electricity demand projections, though specific details are redacted due to confidentiality. The report is part of a regulatory proceeding and includes data relevant to forecasting methodologies and energy usage patterns.
energy exports are not included. Figure 53 provides a breakdown of the significant 7 variances between forecast and actuals for 2022. 8 9 Figure 53: 2022 Variance to Actual 10 Res Comm Ind Other Losses NSR 2022 Forecast 4,715 3,091 2,542 7...
AI summary The text discusses energy usage variances in 2022, highlighting the impact of weather, unexplained residential load increases, and factors like continued pandemic restrictions and higher-than-expected heat pump installations. It also forecasts an annual increase in NSR from 2023 to 2033, driven by new customers, space heating, and EV adoption, with some offset from solar, DSM, and RTR.
Page 94 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document is a redacted version of the 2023 Load Forecast Report, which discusses energy demand projections and related planning considerations for Nova Scotia. Key topics include forecasting methodology, energy usage patterns, and infrastructure planning.
N-7NSPI (Synapse) RIR-1 to RIR-46 - Redacted
19 passages
1 g) Labour shortages and increased automation were not considered explicitly, but any 2 significant factors that would impact the employment or GDP variables would be included 3 in the underlying forecast provided by the Conference Board...
AI summary The text discusses forecasting methodologies for energy demand, noting that labor shortages and automation impacts are implicitly considered via the Conference Board of Canada's (CBoC) GDP forecasts. Using a 10-year period for the Medium Industrial model reduces statistical relevance of employment variables, with adjusted R-squared dropping from 0.72 to 0.37. Forecasts use annual CPI for inflation adjustments and rely on CBoC's 'most likely future' economic scenarios, with discrepancies checked against bank forecasts in Attachment 1.
41946.0 46386.7 3630.7 513.0 34.5 2.0 2898 42756 479 21381 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-5 Attachment 1 Page 2 of 3 Bank Source GDP Emp Housing CPI 2023 2024 2023 2024 2023 2024 2023 2024...
AI summary The document contains economic data from multiple banks (BMO, RBC, TD, National Bank, Scotiabank) projecting Nova Scotia's GDP, employment, housing, and CPI for 2023-2024, contextualized within the 2023 Load Forecast Report (Synapse IR-5 Attachment 1). The data reflects varying economic outlooks and serves as input for energy demand forecasting.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-6: 2 3 Residential End-Use Intensity Trends (Section 4.4, pp 32-51) 4 5 (a) Please provide in electroni...
AI summary NSPI provided data from NRCan and U.S. EIA for residential and commercial models in response to Synapse Energy Economics' IR-6 request, detailing adjustments made to align intensities with NRCan reports and NS Power billing data. Attachments reference specific data tables and modifications in the 2023 Load Forecast Report.
End Use Consumption Data End-Use Stock Data Lighting Table 3 Assumed 100% of households have lighting Room and central air- Table 4 Table 27 conditioning Electric furnace and heat Table 9 Table 21 pumps Electric hot water Table 10 Table 28...
AI summary The document details end-use consumption data compilation methods, referencing tables from Attachment 2 and external sources like EIA or surveys. It specifies which tables populate the 'NRCanDetail' tab in the 2023 LFR Attachments 2 and 3 for various end uses, including space heating, electric hot water, and lighting.
Use and GHG Emissions Table 4: Space Cooling Secondary Energy Use and GHG Emissions by Cooling System Type Table 5: Space Heating Secondary Energy Use and GHG Emissions by Energy Source Table 6: Space Heating Secondary Energy Use and GHG E...
AI summary The document presents tables analyzing secondary energy use and GHG emissions across various building types, heating systems, and appliance categories, alongside explanatory variables related to housing stock and floor space, as part of the 2023 Load Forecast Report.
le Detached Heating System Stock by Heating System Type Table 23: Single Attached Heating System Stock by Heating System Type Table 24: Apartments Heating System Stock by Heating System Type Table 25: Mobile Homes Heating System Stock by H...
AI summary The document contains tables detailing heating, cooling, water heating, and appliance stock data by building type, along with thermal requirements and energy use metrics. It references the 2023 Load Forecast Report (Synapse IR-6 Attachment 1) and is sourced from the Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analysis Group.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Energy Use (PJ) 41.8 44.1 45.3 42.3 40.2 38.4 39.1 44.9 45.2 46.2 46.3 49.5 44.1 43.0 40.9 42.1 38.1 38.9 41.1 41.2 Energy Use by End...
AI summary The document presents energy use data from 2000 to 2019, showing total energy consumption and breakdown by end-use categories such as space heating, water heating, appliances, lighting, and space cooling. The data highlights fluctuations in energy use over time and by category.
0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.2 0.2 0.2 0.3 0.2 Shares (%) Space Heating 65.4 65.0 65.2 64.8 64.9 63.2 62.0 63.5 67.0 68.1 66.2 67.9 64.7 65.1 66.5 68.2 65.1 66.1 64.7 65.2 Water Heating 18.7 19.0 18.8 18.5...
AI summary The text presents statistical data on energy consumption distribution across various categories such as space heating, water heating, appliances, lighting, and space cooling over time, alongside metrics like total floor space and total households. The data reflects trends and changes in energy usage patterns.
5.0 4.5 4.2 3.7 4.5 4.6 4.7 6.0 5.9 6.4 5.8 6.3 6.2 7.1 7.0 7.9 7.1 6.9 7.3 7.1 Shares (%) Electricity 15.9 16.6 17.0 18.3 20.5 21.3 19.4 17.2 18.7 18.8 17.8 17.5 18.6 20.6 22.5 22.1 24.2 25.4 23.7 24.2 Natural Gas 0.0 0.0 0.0 0.0 0.0 0.0...
AI summary The data presents trends in energy consumption and intensity across various fuel types and total floor space over time, showing fluctuations in shares and energy usage patterns. This information is relevant for understanding energy efficiency and consumption trends.
0.4 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.2 0.2 Shares (%) Electricity 54.1 52.3 55.6 53.2 47.6 49.4 48.2 54.9 58.7 66.3 65.0 59.0 59.1 58.4 63.5 61.6 63.1 67.6 72.0 71.1 Natural Gas 0.0 0.0 0.0 1.5 2.0 3.7...
AI summary The text presents statistical data on energy consumption by type and energy intensity over time. It includes percentages of electricity, natural gas, fuel oils, and other energy sources, along with floor space and energy intensity metrics. The data appears to be part of an analysis or report on energy usage patterns.
1.0 1.3 1.2 1.5 1.0 1.2 0.9 0.9 1.0 0.7 0.9 0.7 1.4 1.2 1.2 1.4 1.5 1.6 2.0 1.5 Shares (%) Space Heating 51.4 50.1 51.3 52.8 53.5 50.6 47.3 50.5 48.5 45.6 48.6 55.0 46.4 45.7 46.6 48.1 46.7 46.2 40.5 42.5 Water Heating 3.0 2.8 2.7 3.0 2.8...
AI summary The text presents a series of numerical data points, likely representing energy usage percentages across different categories such as space heating, water heating, and lighting. The data appears to be related to energy consumption patterns over time, possibly in a regulatory context.
1.0 1.2 1.1 1.1 1.2 1.0 1.1 1.1 1.2 1.1 1.0 0.9 0.6 0.6 0.7 0.8 0.6 0.7 0.5 0.5 Shares (%) Electricity 52.7 50.3 53.5 52.5 47.5 49.4 47.8 56.4 59.0 65.0 63.8 57.6 57.6 56.8 62.2 59.5 70.4 62.1 66.9 65.9 Natural Gas 0.0 0.0 0.0 1.6 2.3 4.5...
AI summary The text presents a series of numerical data tables, including energy consumption shares by fuel type, activity metrics such as floor space, and energy intensity measurements over time. These data points provide insights into energy usage patterns and trends.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Energy Use for Arts, Entertainment and Recreation (PJ) 1.0 1.0 1.0 1.0 1.2 1.1 1.0 1.1 1.0 0.9 0.9 1.1 1.1 1.0 1.1 1.2 1.1 1.1 1.1 1....
AI summary The document presents annual data on total energy use and energy use by source for the Arts, Entertainment, and Recreation sector from 2000 to 2019. Electricity, natural gas, light fuel oil, and other energy sources are detailed, showing fluctuations in consumption over time.
ooling 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.1 Shares (%) Space Heating 54.3 53.9 55.0 52.0 58.4 53.8 52.4 55.9 52.3 46.9 41.6 50.1 51.1 50.0 50.6 52.2 51.1 50.2 44.8 46.4 Water Heating 5.7 5.4 5.3 6...
AI summary The text presents data on energy consumption across various categories such as space heating, water heating, and lighting, along with energy intensity metrics over time. It includes percentages of energy use by category and floor space in million square meters, providing insights into energy usage patterns.
Appendix B Nova Scotia Residential Sector and BNI Sector Baseline Study
AI summary This document presents a baseline study focused on the residential sector and BNI sector in Nova Scotia. It provides an analysis of energy usage and demand patterns, serving as a foundation for future energy planning and regulatory decisions.
2.5 4.6 4.0 1.6 4.0 4.0 4.1 2.4 3.3 3.5 3.0 This question was randomly posed to approximately one in ten respondents. 32 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 32 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED)...
AI summary The text includes a survey question about familiarity with LED parking garage lights, part of a 2019 electricity usage survey for businesses. It is from a load forecast report and an energy efficiency study, indicating a focus on energy usage patterns and customer research.
ENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-35 Attachment 1 Page 4 of 8
AI summary The document refers to the 2023 Load Forecast Report by Synapse, specifically Attachment 1, which is part of a regulatory proceeding. It provides context for forecasting methodologies and energy usage patterns.
Year Month ResEndUse.ResHeat NResEndUse.SmlGenHeat NResEndUse.GenHeat mVars.HeatLoad mVars.Days mVars.Heat_AvgMW mPkDayWthr.PkHDDIdx mVars.Heat_Var 2020 7 9,691.1 466.7 2,356.9 12,514.6 31.0 16.8 - - 2020 8 3,489.8 168.6 851.3 4,509.7 31.0...
AI summary The text presents a series of data points related to residential and non-residential end-use heating, as well as variables associated with heat load and weather indices from 2020 to 2021. The data includes monthly values for heat demand, days, and weather-related indices, indicating a focus on energy usage patterns and heating demand trends over time.
Year Month ResEndUse.ResHeat NResEndUse.SmlGenHeat NResEndUse.GenHeat mVars.HeatLoad mVars.Days mVars.Heat_AvgMW mPkDayWthr.PkHDDIdx mVars.Heat_Var 2024 4 231,666.0 13,158.0 57,023.8 301,847.7 30.0 419.2 0.6 268.0 2024 5 153,565.9 8,730.0...
AI summary The text presents a table with data related to residential and non-residential end-use heating, heat load, days, average heat load in MW, peak heating degree day index, and heat variability across months from 2024 to 2025. The data appears to be used for analyzing energy consumption patterns and heating demand.
N-8Evidence - Synapse
9 passages
..................................................................................4 1.4. Recommendations from the Previous Forecast Review .....................................................6 2. ENERGY FORECAST .............................
AI summary The document outlines a regulatory proceeding focusing on energy and peak demand forecasting, including DSM effects, sensitivity analysis, and responses to prior recommendations. Sections cover residential, commercial, and industrial sectors, electrification trends, and methodological approaches to forecasting.
) adjustments applied to the SAE forecast values. Third, NSPI applies other factors to reflect customer growth and specific program adjustments. We will discuss all these components in this evidence. 1.1. Forecast Comparisons First, we loo...
AI summary NSPI's 2023 load forecast projects an 825 GWh (7.3% growth) increase in total load from 2023 to 2033, driven by electrification, cooling demand, and EV adoption, offset by rooftop solar and DSM. The forecast reflects changing conditions including climate-driven fossil fuel reduction and increased electrification.
is growing the most, driven by electric vehicles, new customers, and building electrification. The commercial forecast increases at a more modest level, and the industrial load shows a small increase. Table 3. Sector energy requirements (G...
AI summary The document outlines rising energy demand across residential, commercial, and industrial sectors by 2033, driven by electrification and growth. It highlights NSPI's forecast of a 4.8% load reduction from DSM programs by 2033, while emphasizing the need to address significant increases in energy and peak requirements.
42.6 34.1 31.5 22.4 41% 2032 73.2 46.8 8.3 42.1 33.2 31.1 21.8 41% 2033 71.3 43.4 7.7 41.0 30.8 30.3 20.3 41% Source: Synapse from NSPI load forecast filings—using DSM savings from Figure 38. 2 See Section 4.6 of the forecast Report. Synap...
AI summary Synapse Energy Economics, Inc. provides evidence on NSPI’s 2023 load forecast, referencing prior Board Order recommendations and emphasizing the importance of heat pump and water heater end uses in residential energy forecasting. The text highlights ongoing forecast improvement efforts and sectorial load components.
t 6 2. ENERGY FORECAST In Table 3, we saw the sectorial components of the Nova Scotia load. Here, we will review each of them in sequence, going in the same order as in the forecast report. 2.1. Major Inputs and Regression Models In additi...
AI summary The document outlines the energy forecast methodology, emphasizing sectorial load components and reliance on the Conference Board of Canada's 20-year economic forecast. It notes uncertainties in long-term projections and the use of sensitivity analyses to assess risks.
es are generally consistent with the various bank forecasts available. However, there is greater uncertainty in any longer-term forecast, and sensitivity analyses are useful for looking at this issue. The residential model uses household i...
AI summary The analysis evaluates NSPI's residential load model, highlighting reliance on income and housing data, inconsistencies in reporting load increases, and the need to reevaluate the COVID-19 variable. The model's statistical validity is acknowledged but suggests alternative variables could improve accuracy.
va Scotia by 2033. 26 That would represent a total energy load of 1,003 GWh with peak load impacts ranging from 209 to 374 MW. 27 The residential sector load impact in 2033 is estimated at 666 GWh. 28 The projection of overall EV energy co...
AI summary The document projects significant growth in electric vehicle (EV) energy consumption and peak load impacts in Nova Scotia by 2033, with EVs accounting for 8.3% of energy sales and 8.0–14.2% of peak load. Residential EV adoption is forecast to contribute 12.4% of energy sales and 7.5% of peak load by 2032, based on data from the Load Forecast Report.
shortcomings of this proxy variable. Given that NSPI has increased its forecast for new customer growth, it should carefully monitor trends and make any needed modifications in the next load forecast. Other In its Evidence from 2022, Synap...
AI summary The text critiques NSPI's load forecasting methodology, particularly its use of a proxy variable for COVID-19 work-from-home impacts. NSPI has not revised its modeling approach despite a 2022 Board directive, and its use of a non-integer value for a binary variable is criticized. Residential energy usage projections increased from 2022 to 2023 forecasts, contrary to prior expectations. The Board previously recommended incorporating household demographics into load modeling.
ent. But there is a projected 10 percent increase in the number of customers which boosts the model load by 48 GWh (or 14.8 percent). Note also the big increase associated with commercial EV usage. 49 Note that the reported DSM impacts are...
AI summary The document highlights a 10% customer increase boosting model load by 48 GWh, driven by commercial EV growth and DSM impacts exceeding SAE model estimates. NSPI notes pandemic effects reducing commercial sales by 13 GWh annually, while large Gen loads rise by 26 GWh due to electrification and institutional expansion. Solar and DSM projections are absent from NSPI's submission.
90033Synapse (NSPI) IR-1 to IR-46
8 passages
Document #:303618 Date Filed: May 25, 2023 Synapse (NSPI) Page 1 of 18 1 Questions regarding the NSPI “2023 Load Forecast Report” of April 28, 2022 2 Request IR-1: 3 Report Tables 4 a. Please provide in electronic spreadsheet format all ta...
AI summary The document outlines three requests (IR-1, IR-2, IR-3) for data related to NSPI's 2023 Load Forecast Report, including spreadsheet formats for tables, historical sales data, and clarification on billed vs. accrued sales methodologies. The focus is on transparency and accuracy in load forecasting and historical energy usage.
ndustrial “to improve the fit.” Please quantify the nature of the improved 28 fit. 29 c. Please identify the effect if the industrial forecast used the same period as the residential 30 and commercial forecasts (that is, January 2013 to De...
AI summary The text outlines requests for detailed weather data and methodology related to heating degree days (HDD) and cooling degree days (CDD) used in a forecast. It asks for temperature data, calculation methods, and explanations for data period changes, focusing on accuracy and consistency in energy demand modeling.
Document #:303618 Date Filed: May 25, 2023 Synapse (NSPI) Page 8 of 18 1 d. Please describe any changes in the data and statistical analysis used to develop the 2 coefficient for the DSM variable for the Commercial and Industrial forecast...
AI summary The document contains regulatory requests (IR-18) directed at NSPI, seeking clarifications on statistical methods for DSM coefficients, residential solar adjustments, and the impact of COVID-19 on energy forecasts. Requests focus on data sources, calculation methodologies, and assumptions related to long-term work-from-home trends.
e Company conducted any analysis on the long-term impact of the COVID- 23 19 pandemic, including analysis of whether the incremental sales attributable to the 24 increase in work-from-home load is apt to decline over time? Please explain....
AI summary The document contains regulatory requests addressing NSPI's load forecasts, including impacts of the pandemic on sales, assumptions about home size increases, and effects of critical peak pricing (CPP) and time-of-use (TOU) rates on EV charging behavior. Questions also seek clarity on building efficiency regulations and the status of CPP/TOU pilots.
e impacts of COVID on the 2021 and 2022 loads. 12 b. Please explain and quantify the ongoing effects of COVID in the commercial forecast. 13 c. Please explain why the current forecast (Figure 44) remains shows an increase through 14 2033 w...
AI summary The text outlines regulatory requests (IR-21 to IR-23) seeking explanations for forecast discrepancies in electricity demand, focusing on factors like COVID impacts, EV loads, space heating, DSM programs, and solar generation. Requests emphasize quantifying model changes, evaluating efficiency trends, and clarifying forecast assumptions for 2021–2033.
ease explain and quantify the specific reasons for the differences from the previous 5 forecast. 6 b. Please quantify how much of the increase is associated with expansion of institutional 7 facilities and how much from other factors. 8 9...
AI summary The document outlines requests for detailed explanations and quantifications regarding forecast discrepancies, growth factors, survey representation, municipal load changes, and system losses. It seeks data on industrial and municipal energy usage trends, reliability of projections, and historical system loss patterns.
a. Please provide the system losses and unbilled sales for each year of the last five years. 4 b. Please provide information about how system losses vary over a typical year. 5 c. Please identify and discuss the reasons for any significant...
AI summary The text outlines requests for data on system losses, unbilled sales, net system requirement, peak demand, and demand response. It seeks source data, calculations, and explanations for these topics, including DR resource modeling, ELCC derivation, and peak demand calibration.
Document #:303618 Date Filed: May 25, 2023 Synapse (NSPI) Page 15 of 18 1 Appendix B: Combined Model for Commercial and Industrial DSM Coefficient (pp 27-28) 2 a. Please provide in electronic spreadsheet format the data and the statistical...
AI summary The document contains regulatory requests (IR-41 to IR-43) directed at Synapse (NSPI), seeking detailed data, model parameters, and explanations for DSM coefficient models, peak forecasts, and sensitivity analyses. Requests focus on transparency of methodologies, data sources, and assumptions used in NSPI's submissions.
90066NSUARB (NSPI) IR-1 to IR-26
6 passages
M11108 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF: THE PUBLIC UTILITIES ACT - and - IN THE MATTER OF: NOVA SCOTIA POWER INCORPORATED (NS Power) 10 – Year Energy and Demand Forecast (2023 Load Forecast Report) INFORMATION REQUEST...
AI summary The Nova Scotia Utility and Review Board has issued an information request to Nova Scotia Power Inc. regarding their 10-year energy and demand forecast (2023 Load Forecast Report), seeking responses by June 20, 2023, with contact details provided for follow-up.
Document: 303590 (P-194) Date Filed: 05/30/23 UARB Page 1 1 Request IR-1: 2 Pages 16 and 17 of the Application discuss customers taking service under the Renewable to 3 Retail (RTR) Tariff in 2023. Lines 3 and 4 on page 17 state: “…the cor...
AI summary The document contains four requests (IR-1 to IR-4) questioning Nova Scotia Power's (NSP) preparedness for RTR tariff adoption delays or higher take-up, the use of a single HDD reference temperature (18°C), housing start correlations with customer forecasts, and inflation-adjustment methodologies for financial variables. Each request seeks clarification on assumptions and data practices.
Document: 303590 (P-194) Date Filed: 05/30/23 UARB Page 2 1 i. The figures for 2020 onward have been revised from the 2022 Load Forecast 2 report. The 2022 report forecasted compensation in 2022 at 18,637 and 2023 at 3 18,747. This report...
AI summary The document requests explanations for revised load forecast data (2022-2023 compensation, non-manufacturing employment) and justification for 2023 GDP/employment trends. It questions data sources (e.g., Conference Board of Canada), cross-checking with other forecasts, and the impact of revised figures on load projections.
If so, please 20 provide a copy. 21 iii. Has the data been checked against other forecasts? 22 e) Figure 19: Industrial Economic Drivers provides the historical and forecast manufacturing 23 GDP and employment both declining in 2023. 24 i....
AI summary The text requests clarification on revised manufacturing GDP and employment data from the 2022 Load Forecast report, including reasons for negative 2023 growth, discrepancies between employment and GDP trends (2025–2027), and validation against other forecasts. It emphasizes the impact of these revisions on load forecasting.
ol. Please explain why heating has a higher value than cooling in July. 27 b) Under the inputs and outputs tabs in the column WtXHeat, the values in July are higher 28 than October. Please explain. 29 c) Under the inputs and outputs tabs i...
AI summary The text contains four questions seeking explanations for discrepancies in heating and cooling values across different months (July, October, November, December, June) within the WtXHeat and WtXCool columns of an analysis. The questions focus on why heating values exceed cooling values in July, why cooling values in colder months surpass those in June, and the significance of these data patterns.
lues for cooling in November and December and why the values are higher 32 than June. 33 d) In the outputs tab July has higher values for WtXHeat than for WtXCool. Please explain. 34
AI summary The text raises two questions about temperature-related metrics: why cooling values in November/December exceed those in June, and why July shows higher WtXHeat than WtXCool. These queries seek explanations for seasonal variations in heating and cooling weightings.