N-12023 Load Forecast Report + Appendecies - Redacted
52 passages
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.
associated weather stations 19 20 DATE: April 28, 2023 Page 24 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 13: Weather Station Weighting 2 Zone Average of 3 Weight Zone Name Weather Latitud...
AI summary The 2023 Load Forecast Report discusses the use of weather station weighting in load forecasting, specifically comparing forecasts based on temperatures from a single station (Shearwater RCS) versus a weighted average of multiple stations. The Mean Absolute Percentage Error (MAPE) is used as the metric for comparison.
2023 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 In the commercial models, non-manufacturing gross domestic product (GDP) and non- 6 manufacturing employment continue t...
AI summary The 2023 Load Forecast Report discusses forecasting methods for different customer sectors, including the use of economic drivers such as GDP and employment data. The report highlights the use of econometric models and the importance of adjusting variables to constant dollars to remove inflation effects.
Page 31 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of projected electricity demand, incorporating factors such as Heating Degree Days (HDD) and Cooling Degree Days (CDD), and is part of a larger regulatory proceeding involving Nova Scotia Power and the Nova Scotia Utility and Review Board.
1 4.4 End-Use Intensity Trends 2 3 In addition to economic data, the SAE model also uses end-use data, in the form of 4 saturations and efficiencies, from NRCan and the US Energy Information Agency (EIA). 5 NRCan data for the residential s...
AI summary The SAE model uses end-use data from NRCan and EIA to develop end-use intensity trends. Historical saturation trends and efficiency estimates are combined with survey data and adjusted based on NRCan and NS Power billing data. EVs and rooftop solar PV are modeled separately due to limited historical data and program impacts.
the residential sector in order to achieve carbon 14 reduction targets of net-zero by 2050 as shown in Figure 20. 15 16 Figure 20: E3 Residential Space Heating Saturation 17 18 DATE: April 28, 2023 Page 33 of 98 REDACTED (CONFIDENTIAL INFO...
AI summary The document discusses residential and commercial space heating saturation models, particularly focusing on the E3 model and its trajectory towards achieving net-zero carbon reduction targets by 2050. It also notes adjustments made to NS Power models to align with E3 estimates by 2040.
) 2023 Load Forecast Report REDACTED 1 Figure 22: Commercial Space Heating Saturation Comparison 2 3 4 5 For the 2023 forecast, energy values predicted by the SAE models are higher than the E3 6 models in the case of the residential class...
AI summary The 2023 Load Forecast Report compares energy and peak demand predictions from SAE and E3 models. Residential class energy values are higher in SAE models, while commercial class predictions are similar. Peak demand differences are smaller than in the 2022 forecast, with ongoing efforts to reduce model discrepancies.
vehicles (MDV) such as delivery trucks and other medium-duty fleet vehicles, and heavy- 17 duty vehicles (HDV) focusing on buses. The EV forecast estimates that Nova Scotia will 13 EfficiencyOne Report: 2021 DSM Programs Evaluation Reports...
AI summary The document discusses the forecast for electric vehicle (EV) adoption in Nova Scotia, projecting over 195,000 EVs on the road by 2032, primarily light-duty vehicles (LDVs), compared to a 2022 forecast of 97,000 vehicles by 2032. The report references data from EfficiencyOne and the Canadian government's EV sales targets.
compared to a forecast of 97,000 vehicles by 2032 in the 2022 Load Forecast, which had 3 an estimate of 30 percent of vehicle sales by 2030. 4 5 Figure 26: EV Sales Forecast 6 7 8 9 The impact of EVs on energy sales and peak demand depends...
AI summary The text discusses the forecast of electric vehicle (EV) sales and their impact on energy sales and peak demand. It references E3's EV Load Shaping Tool, which models EV driving and charging behavior in Nova Scotia to estimate load shapes and peak demand contributions.
Vehicle Avg Avg kW/vehicle Avg kWh/year Type km/year 15 on Peak LDV 17,427 4,323 0.9 MDV 22,779 8,205 1.6 HDV 62,888 113,890 7.3 6 7 The peak impact assumes that 70 percent of charging is managed by NS Power (including 8 smoothing through...
AI summary The text discusses the impact of electric vehicles (EVs) on the grid, focusing on peak demand and energy consumption. It notes that managed charging (70% by NS Power) reduces peak demand to 0.9 kW/vehicle, compared to 1.6 kW/vehicle in unmanaged scenarios. The Smart Grid Nova Scotia (SGNS) Project is collecting data on EV impact, with 100 smart chargers installed for testing.
ypotheses with DER assets. 22 Through the project about 100 EV smart chargers have been installed, and testing is 23 ongoing to demonstrate the impact of charging and the benefit of utility-managed charging 15 Based on the NRCan 2009 Canad...
AI summary The text discusses the installation of approximately 100 EV smart chargers as part of a project to test the impact of utility-managed charging on the grid. A reference is made to the NRCan 2009 Canadian Vehicle Survey Summary Report for context on vehicle data.
news articles, or by the default language in the ChargePoint app that customers use, 23 that suggests all customers are eligible to save from off-peak rates. 24 16 M09985 – CI C0010788 – Smart Grid Nova Scotia Project – Semi-Annual Report,...
AI summary The document discusses the impact of electric vehicle (EV) charging on energy and peak demand, referencing figures that illustrate seasonal charging energy delivery, peak demand from EV charging, and estimated energy and peak impacts based on the number of EVs. It also mentions managed charging measures and their potential effect on reducing peak demand.
measures; this sensitivity assumes an average peak demand of 1.6 7 kW/vehicle based on the E3 unmanaged model. 8 9 Figure 30: EV Impact to Energy and Peak Forecasts (cumulative) 10 Peak @ Peak @ Load Year EVs 0.9kW/vehicle 1.6kW/vehicle (G...
AI summary The text discusses the impact of electric vehicles (EVs) on energy and peak demand forecasts, using an average peak demand of 1.6 kW/vehicle based on the E3 unmanaged model. The forecast data shows a cumulative increase in EVs and corresponding energy and peak demand growth from 2023 to 2033.
3 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of projected electricity demand, incorporating factors such as Heating Degree Days (HDD) and Cooling Degree Days (CDD). The report is part of a regulatory proceeding and includes redacted confidential information.
Page 45 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 likely contains information related to electricity demand forecasting for Nova Scotia. Due to redaction, specific details are not available.
l as smaller appliances such as computers, dehumidifiers, 26 microwaves, etc. This category also includes solar generation (photovoltaic or PV) 27 and EV forecasts. 28 DATE: April 28, 2023 Page 47 of 98 REDACTED (CONFIDENTIAL INFORMATION R...
AI summary The document discusses residential and commercial end-use intensities, highlighting trends such as increased use of heat pumps, changes in electric baseboard heating, and the impact of EV load and PV generation. Supporting data is referenced in Attachment 1.
2023 Load Forecast Report REDACTED 1 2 Figure 35: Historical and Projected General Commercial End-Use Intensity 3 (kWh/m2) 4 5 6 7 Supporting data for General commercial end-use intensities is included in Attachment 3. 8 9 For the 2023 Loa...
AI summary The 2023 Load Forecast Report discusses historical and projected general commercial end-use intensity, using baseline data from the EIA 2021 Annual Energy Outlook. Small scale solar and EV load are included in the Miscellaneous category of the General Commercial class. The report highlights growth in the commercial and industrial sectors driven by net-zero emissions goals and electrification programs.
Page 50 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of projected electricity demand, incorporating factors such as weather patterns, economic trends, and energy efficiency initiatives. The report includes detailed projections and assumptions related to future load requirements.
1 emissions. These programs involve converting heating loads to electricity (mainly from 2 oil), accelerating the uptake of electric cooling technologies, and examining opportunities 3 to power industrial processes through electrification....
AI summary The text discusses electrification programs targeting commercial and industrial sectors, focusing on converting heating loads to electricity, promoting electric cooling technologies, and electrifying industrial processes. Forecasts for electrification growth by customer class are provided, with specific data shown in Figure 36.
3 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 likely contains information related to electricity demand forecasting in Nova Scotia. Due to redaction, specific details and analysis are not visible.
1 4.6 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the 4 use of electricity in Nova Scotia, and the forecast takes the projected energy and demand 5 savings into account. Between...
AI summary The document discusses the role of Demand Side Management (DSM) in Nova Scotia's electricity use, noting that DSM plans are based on E1’s proposed supply agreement and 2019 Potential Study. It highlights the challenge of double-counting DSM savings in forecasting models and explains the approach used to address this issue by incorporating historical DSM savings into regression models.
Page 54 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of expected electricity demand, though specific details have been redacted due to confidentiality. It likely includes projections and methodologies used to estimate future load requirements.
Page 58 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 multi-unit residences and 15,500 single family homes. Single-family homes are assumed 2 to use approximately 16,000 kWh per year on average, whi...
AI summary The 2023 Load Forecast Report estimates residential electricity usage based on average consumption rates for single and multi-unit homes, building shell efficiency, and projected changes in house size. Efficiency improvements are expected but will be lower in Nova Scotia than in New England, while house size is expected to increase, though at a slower rate.
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.
1 6.0 COMMERICAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General 4 rate classes. Like the residential model, the commercial SAE models express monthly sales 5 as a function of heating, coolin...
AI summary The Commercial SAE model forecasts electricity use for Small General and General rate classes based on heating, cooling, and other loads, incorporating factors like GDP, employment, and HDD/CDD. The model was updated in 2023 to include EV load in the commercial class, previously modeled only in the residential class. The model reflects a rebound in commercial sales post-pandemic.
D) 2023 Load Forecast Report REDACTED 1 Figure 47: Historical and Forecast Annual General Demand Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2023 6 to 2033. Total change between 2023 an...
AI summary The 2023 Load Forecast Report discusses historical and projected annual general demand sales, noting a 4.4% increase from 2023 to 2033. The Large General Service class shows slower growth due to revised estimates of large project completion, with customer surveys and historical data informing the forecast.
m and must continue 23 to plan for serving these customers in the long term, the full amount of the municipal 24 electric utilities’ peak demand is included in the Load Forecast. 25 DATE: April 28, 2023 Page 72 of 98 REDACTED (CONFIDENTIAL...
AI summary The document discusses the 2023 Load Forecast Report, highlighting system losses and unbilled sales, with system losses averaging 6.4% of NSR over the past five years and projected to remain between 6.0% and 7.0% over the 10-year forecast period. It also defines Net System Requirement (NSR) as the energy required to supply residential, commercial, and industrial sales, plus system losses, excluding certain factors like industrial self-generation and exports.
1 10.0 PEAK DEMAND 2 3 The total system peak is defined as the highest single hourly average demand experienced 4 in a year. It includes both firm and interruptible loads. Due to the weather-sensitive load 5 component in Nova Scotia, the t...
AI summary The text discusses the definition of total system peak demand in Nova Scotia, focusing on the period from December through February. It outlines NS Power's method of using an end-use approach to forecast peak demand, incorporating factors like heating, cooling, and demand response activities. EVs and DR programs are included in the 2023 Load Forecast, referencing studies and models from E3 and E1.
used as variables. The dependent 24 variable was 10 years of hourly system load (excluding large industrial customers) for 25 winter months November-March. Figures 57-59 show the results of each of the models. 26 DATE: April 28, 2023 Page...
AI summary The document presents regression model results from the 2023 Load Forecast Report, analyzing the relationship between hourly system load and variables such as peak temperature, weekdays, and wind. The models aim to predict load patterns during winter months, excluding large industrial customers.
3.284311 0.098476 33.3515 3E-240 3.091295456 3.477325791 3.091295456 3.477325791 3 4 5 Figure 58: 12hr Avg Lag Peak Temperature Regression Model Results 6 SUMMARY OUTPUT Regression Statistics Multiple R 0.76298 R Square 0.582138 Adjusted R...
AI summary The text presents regression model results for temperature lag and load forecasting, including statistical outputs such as R-square, standard error, and coefficients for variables like weekdays, wind, and 12-hour average lag. These models are part of a 2023 Load Forecast Report.
1 customer and interruptible customer contributions, and finally DSM. As discussed in 2 Section 4.4, the EV contribution to peak is expected to be mitigated via utility managed 3 charging. The firm peak without EV peak mitigation (assuming...
AI summary The text discusses the impact of electric vehicles (EVs) and heat pumps on peak electricity demand in Nova Scotia, estimating their contributions to peak load by 2033. It references various models and studies, including those by E3, Itron, and the SAE model, and notes differences in estimates based on efficiency assumptions.
Appendix A – Forecast Values 1.1 Table A1: Energy Requirement – 2023 NS Power Forecast Energy Forecast
AI summary This section presents Table A1 from the 2023 NS Power Forecast, which outlines energy requirement forecasts. It provides a basis for understanding future energy needs and planning accordingly.
evening January 11 weekday 2022 155 - 2,061 2,216 12.6 -15 -14 evening 2023 146 4 2,105 2,256 1.8 -14 Forecast 2024 147 12 2,111 2,271 0.7 -14 Forecast 2025 148 24 2,119 2,291 0.9 -14 Forecast 2026 156 36 2,148 2,340 2.1 -13 Forecast 2027...
AI summary The text presents a series of load forecast data from 2022 to 2033, including metrics such as peak demand, load factors, and forecasted values. It is part of an appendix from the 2023 Load Forecast Report, which includes confidential information that has been redacted.
2,627 2,819 3.0 -13 Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix B Page 1 of 33 Appendix B – Forecast Model Details 2023 NS Power Load Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Loa...
AI summary This section of the 2023 Load Forecast Report Appendix B details the residential average use SAE model, which incorporates variables for heating, cooling, and other end uses, as well as factors like efficiency, saturation trends, and seasonal patterns to forecast residential electricity demand.
ercial Model Detail Small General Service Small General Service is projected using an SAE average use model and a sales forecast is generated as the product of the average use and customer forecast. Like the residential model, monthly Smal...
AI summary The document discusses the Small General Service load forecasting model, which uses an SAE average use model and incorporates factors such as heating and cooling requirements, GDP, employment, and price elasticities. It also includes adjustments for seasonal and event-related factors like the pandemic and Hurricane Fiona.
ast Report Appendix B Page 10 of 33 Appendix B – Forecast Model Details Variable Coefficient StdErr T-Stat P-Value MStructSmlGen.WtXHeat 0.855 0.036 23.811 0.00% MStructSmlGen.WtXCool 0.302 0.045 6.655 0.00% MStructSmlGen.WtXOther 0.714 0....
AI summary This document presents a statistical analysis of forecast models, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. The table includes variables such as heating, cooling, and other factors, as well as monthly bin coefficients and a SMA(1) term.
-4.3% 22.4% 0.0% 0.0% 18.0% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor Small General Input Variables – XOther Intensities Econ + Reg Struct Vent Water Cook Refrig Light Office Misc OtherUse Coeff Scaling Total Heat Var Fact...
AI summary The text provides a table and formula related to energy consumption forecasting for general service, including variables such as cooling, ventilation, and lighting, with data for the years 2023 and 2033. It includes calculations for XCool and XOther, and highlights changes in energy use intensities and coefficients over time.
2023 Load Forecast Report Appendix B Page 15 of 33 Appendix B – Forecast Model Details General Service The General Service rate class model is estimated on a total monthly sales basis where total monthly billed sales is a function of total...
AI summary The General Service rate class model is estimated using monthly sales data, influenced by heating and cooling requirements, GDP, employment, and price factors. The model includes variables for HDD, CDD, and seasonal adjustments, as well as binary variables for specific months and events like the COVID-19 pandemic.
general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total sales rather than average use as is the case in the small general and residential classes. Like the small general model, a flat s...
AI summary The document discusses the forecast for general demand load in the commercial sector, using a regression model that includes factors such as EV load, solar, and DSM. Adjustments are made to the model to account for these factors, and the forecast shows a projected increase in demand by 2033.
(437) (264) Change 4.3% -2.7% 11.4% -1.9% -6.8% 4.4% Gen Sales = Sales + RTR + EV Load + Solar Load + DSM General Demand Sales –Regression XHeat XCool XOther Binaries ARMA Sales (GWh) 2023 533,784 114,190 1,746,561 (13,042) (3,141) 2,378,3...
AI summary The text provides a forecast model for general demand, including variables such as XHeat, XCool, and XOther, along with their respective intensities and coefficients. It outlines the regression model and how changes in factors like heating and cooling demand are calculated using multiplicative growth rates.
ad Forecast Report Appendix B Page 20 of 33 Appendix B – Forecast Model Details General Demand Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total Xcool 2023 312,580 1.38 0...
AI summary The document provides a forecast model detailing input variables for XCool and XOther, including cooling intensities, economic and structural factors, regression coefficients, and scaling factors for the years 2023 and 2033. It highlights changes in these variables over time.
-4.4% 27.4% 0.0% 0.0% 23.0% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor General Demand Input Variables – XOther Intensities Econ Reg + Struct Vent Water Cook Refrig Light Office Misc Other Coeff Scaling Total Heat Use Factor...
AI summary The text presents demand input variables and their changes over time, including calculations for cooling and other demand factors. It also describes an industrial econometric model with variables for monthly sales and economic factors, noting the inclusion of a binary variable for October 2022 due to billing delays from Hurricane Fiona.
MBin.Julm + MBin.Augm + MBin.Sepm + MBin.Octm + MBin.Novm + MBin.Decm + MBin.Oct22 + b1×MEcon.ManGDP A binary variable was added for October 2022 to account for billing delays after hurricane Fiona. Variable Coefficient StdErr T-Stat P-Val...
AI summary The text discusses a statistical model incorporating binary variables for months and a binary variable for October 2022 to account for billing delays caused by Hurricane Fiona. Coefficients, standard errors, T-Statistics, and P-Values are provided for each variable, indicating their significance in the model.
2023 Load Forecast Report Appendix B Page 30 of 33 Appendix B – Forecast Model Details CoolAvgMWm = CoolLoadm/ Daysm /24 The impact of peak-day weather conditions are then captured by interacting peak-day HDD and CDD with average monthly h...
AI summary This section of the 2023 Load Forecast Report discusses the methodology for calculating heating and cooling load variables, as well as the base load variable, in the forecast model. It outlines how peak-day heating and cooling degree days (HDD and CDD) interact with average monthly load requirements and how non-weather sensitive load components are incorporated into the model.
energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coefficients found after running the sales model. ResOther can be written as: ResOtherm =ResSalesm- b1×ResXHeatm-b2×ResXCoolm...
AI summary The text describes an energy sales model that separates weather-dependent and non-weather-dependent variables, including the impact of past demand-side management (DSM) activities. It also accounts for factors like the average daily wind speed on peak days and the effects of events such as the COVID-19 pandemic and billing delays related to Hurricane Fiona.
nd a binary was added for October 2022 to account for the impact of billing delays related to hurricane Fiona in the energy models. Variable Coefficient StdErr T-Stat P-Value mVarsNew.Heat_Var 1.690 0.095 17.783 0.00% mVarsNew.Cool_Var 1.2...
AI summary A binary variable was added to the energy models in October 2022 to account for billing delays caused by Hurricane Fiona. The statistical analysis shows significant coefficients for various variables, indicating their strong influence on the load forecast model.
Figure C4: Energy Forecast Accuracy NSR less mills Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast for: for: for: for: for: for: for: for: for: for: Issued 2013 2014 2015 2016 2017 2018 20...
AI summary Figure C4 presents energy forecast accuracy data over time, showing forecast values for NSR less mills from 2012 to 2022. The data illustrates forecast trends and variations across different years, highlighting the accuracy of energy forecasts issued in various years.
D Page 6 of 9 Appendix D – Forecast Sensitivity Analysis Figure D4: Peak Forecast (Residential, Commercial and Small and Medium Industrial) The asymmetry in this figure, seen as the off-centre median, is explained by the bias introduced by...
AI summary The document discusses a forecast sensitivity analysis, highlighting the asymmetry in peak demand forecasts due to the use of the MAX function on monthly peak heating degree days (HDD). It notes that monthly HDD has become more influential than peak HDD in recent years, particularly in 2024, due to the impact of year-long residential heating on sales and the proposed E3 electrification scenarios.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 5 of 21 Changes from 2022 Input Data Source COVID adjustments Residential class still has a WFH variable that reduces over time. Commercial class varia...
AI summary The 2023 Load Forecast Report discusses updates to the load forecast, including changes from the 2022 report. Key updates include adjustments for the impact of the COVID-19 pandemic, electrification of heating, and the inclusion of EV sales mandates. The report also addresses peak design conditions and the expected increase in solar generation.
les in the residential class at the end of 2022, continued use of a COVID variable helps model the change in sales from pre to post COVID until “new normal” is established in data set. • Previous model relied on economic variables to accou...
AI summary The document discusses the impact of the COVID-19 pandemic on residential and commercial electricity sales, the use of a COVID variable in modeling sales changes, and the forecasted increase in electric vehicle (EV) load due to federal ZEV sales targets. The 2023 forecast shows higher EV adoption and increased energy and peak demand compared to the 2022 forecast.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 13 of 21 Other • RTR participation is expected to start in 2024 with wind farm being built in 2023 totaling 33.6MW or approximately 130 GWh of annual g...
AI summary The document discusses the expected increase in renewable energy generation, particularly from wind and solar sources, and highlights changes in legislation affecting solar adoption. It also notes the impact of weather and pandemic-related factors on residential load forecasts and customer growth.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 15 of 21 Forecast Comparison - Commercial The significant changes to the forecast for 2023 are the drop in load between 2023 and 2024 due to the introd...
AI summary The 2023 Load Forecast Report highlights changes in load forecasts across commercial, industrial, energy, and peak categories. Key factors include the introduction of RTR, increased EV consumption in the commercial class, reduced industrial load due to lower forecasts for a large customer, and higher EV penetration impacting peak demand.
N-7NSPI (Synapse) RIR-1 to RIR-46 - Redacted
279 passages
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Report Tables 4 5 (a) Please provide in electronic s...
AI summary NSPI responds to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report by providing figures from various attachments and IRs, including tables, graphs, and data sources. The response details the location of specific figures within different report attachments and Synapse IR attachments.
ions by Energy Source Table 39: Apartments Secondary Energy Use and GHG Emissions by End-Use Mobile Homes Table 40: Mobile Homes Secondary Energy Use and GHG Emissions by Energy Source Table 41: Mobile Homes Secondary Energy Use and GHG Em...
AI summary The text presents tables detailing energy use and GHG emissions by energy source and end-use for residential and mobile homes in Nova Scotia. It also references the 2023 Load Forecast Report and mentions the Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analysis Group.
41.9 42.4 42.8 41.9 39.0 37.1 38.4 40.2 40.3 40.1 41.3 42.2 39.3 36.2 34.8 34.6 31.7 31.8 32.6 32.6 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text provides data on energy use and GHG emissions in Nova Scotia, including heating and cooling degree-day indices and a table showing secondary energy use and GHG emissions by end-use from 2000 to 2019. The data excludes electricity production-related emissions and includes a note on the source of the information.
41.9 42.4 42.8 41.9 39.0 37.1 38.4 40.2 40.3 40.1 41.3 42.2 39.3 36.2 34.8 34.6 31.7 31.8 32.6 32.6 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text presents data on energy use and GHG emissions for the residential sector in Nova Scotia, including lighting energy use and heating and cooling degree-day indices over multiple years. It also references the Office of Energy Efficiency and a Load Forecast Report.
1.6 1.7 1.7 1.7 1.7 1.7 1.6 1.6 1.5 1.5 1.5 1.5 1.5 1.6 1.5 1.4 1.5 1.5 1.6 1.5 Activity Total Households (thousands) 359.5 362.6 366.6 370.6 374.0 377.0 379.6 382.9 387.9 392.5 389.8 391.2 393.4 393.3 397.0 400.0 402.0 406.0 410.8 415.6 E...
AI summary The document presents data on energy intensity, household numbers, and GHG emissions for the residential sector in Nova Scotia. It includes statistics on total households, energy intensity, and heat loss over time, excluding electricity-related GHG emissions. The data is sourced from the Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analysis Group.
pace (million m2) 3.8 2.8 3.3 3.9 4.1 4.4 5.0 6.7 6.6 7.4 8.4 9.1 10.1 10.8 11.5 7.5 13.0 13.7 14.5 15.2 Energy Intensity (MJ/m2) 7.3 11.7 9.2 9.8 7.2 14.2 9.9 5.4 12.2 12.2 16.6 9.7 16.7 15.0 12.0 22.7 14.5 13.6 20.1 13.2 Total Space Cool...
AI summary The document presents data on energy use and GHG emissions for the residential sector in Nova Scotia, including space heating and cooling, over multiple years. It includes metrics such as energy intensity, space cooling GHG emissions, and cooling degree-day index. The data is sourced from the Office of Energy Efficiency and the Load Forecast Report.
0.59 0.61 0.62 0.56 0.52 0.48 0.47 0.54 0.57 0.58 0.56 0.60 0.50 0.49 0.47 0.49 0.42 0.42 0.43 0.43 Total Space Heating GHG Emissions Excluding Electricity (Mt of CO2e) 1.4 1.5 1.5 1.4 1.3 1.1 1.2 1.4 1.5 1.5 1.5 1.7 1.3 1.2 1.2 1.2 1.0 1....
AI summary The text presents numerical data on space heating greenhouse gas (GHG) emissions and GHG intensity over time. It details emissions from various energy sources, including heating oil and wood, with corresponding emission levels and intensity measurements in tonnes per terajoule (TJ).
) 50.6 51.4 51.8 51.2 48.0 46.4 47.6 48.5 48.0 47.4 49.0 49.3 47.2 44.0 42.5 41.9 39.8 39.8 40.9 40.8 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Heat Gains (...
AI summary The document presents data on space heating secondary energy use and GHG emissions by building type in Nova Scotia from 2000 to 2019, including metrics like Heating Degree-Day Index and Heat Gains (PJ). It also notes that GHG emissions data exclude electricity production and includes a disclaimer about confidential information being redacted.
0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 1) Data on GHG emissions are presented excluding GHG emissions related to electricity production only. Office of Energy Efficiency, Demand Policy and Analysis Division, Marke...
AI summary The text provides data on GHG emissions excluding those related to electricity production, focusing on residential sector space heating energy use and emissions by building vintage in Nova Scotia, as presented in the 2023 Load Forecast Report.
2.5 2.7 2.9 2.8 2.9 2.7 2.4 2.5 2.8 2.9 2.6 2.8 2.5 2.7 2.9 3.0 2.9 3.2 3.1 3.3 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 1) Data on GHG emissions are prese...
AI summary The text presents numerical data on heating degree-day index and water heating secondary energy use and GHG emissions by energy source in Nova Scotia from 2000 to 2019. It also includes a note about excluded GHG emissions related to electricity production and mentions the Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analysis Group.
0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4 Shares (%) Electricity 29.7 29.5 29.5 30.6 34.2 35.8 32.7 29.0 30.1 30.0 27.4 26.2 28.3 31.9 35.1 35.3 39.0 39.8 37.1 37.7 Natural Gas 0.0 0.0 0.0 0.0 0.0 0.0...
AI summary The text presents statistical data on energy consumption distribution across various energy sources (electricity, natural gas, heating oil, other, and wood) over time, along with the total number of households and energy intensity per household in thousands. The data reflects trends in consumption and usage patterns.
46.9 47.6 48.0 47.5 44.5 43.1 45.3 47.6 47.1 47.2 49.2 50.0 48.2 45.2 42.9 42.4 39.6 39.4 41.2 40.9 Heat Loss (PJ) 0.2 0.3 0.3 0.3 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1) Data on GHG emissions are presented exclu...
AI summary The text provides numerical data on heat loss and GHG emissions, along with a table showing water heating secondary energy use and GHG emissions by building type in Nova Scotia from 2000 to 2019. It excludes GHG emissions related to electricity production and includes notes on data sources and categories.
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 Shares (%) Electricity 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 99.3 99.4 99.6 99.6 99.5 99.5 99.4 99.4 99.4 99.2 99.2 Natural Gas 0.0 0.0 0.0 0.0...
AI summary The document presents statistical data on electricity and natural gas usage, household activity, and energy intensity over time. It outlines trends in energy consumption and the number of households across different years, providing insights into energy use patterns.
(thousands) 15.2 15.4 15.5 15.7 15.8 16.0 16.2 16.3 16.4 16.5 16.6 16.7 16.8 16.9 17.0 17.0 17.1 17.2 17.3 17.4 (thousands) Heating Oil – Normal Efficiency 4.4 4.0 3.6 3.3 3.0 2.6 2.3 2.0 1.8 1.5 1.3 1.1 1.0 0.8 0.4 0.4 0.1 0.0 0.0 0.0 Hea...
AI summary The table presents data on the usage of various heating fuels and electricity over time, showing trends in consumption for different efficiency levels of heating oil, natural gas, and electric heating. The data indicates a decline in the use of heating oil with normal efficiency and an increase in the use of medium efficiency heating oil.
13.0 12.4 11.8 11.3 11.2 11.5 11.3 11.5 11.4 11.1 11.4 11.4 11.0 10.7 10.7 10.7 10.7 10.8 10.9 11.0 Shares (%) Electricity 53.9 53.5 52.6 51.8 52.1 51.6 51.7 51.5 51.1 51.2 51.0 50.6 50.5 50.1 49.9 49.8 49.7 49.6 49.6 49.5 Natural Gas 0.0...
AI summary The text presents a table showing the distribution of energy sources over time, with shares of electricity, natural gas, heating oil, steam, other, and wood. The data indicates a decreasing share of electricity and increasing share of heating oil over the period.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Single Detached Water Heater Stock (thousands) 255.8 257.9 260.4 263.8 266.4 269.2 271.7 274.0 276.4 278.2 280.3 282.0 283.6 285.2 28...
AI summary The text presents a table showing the total number of single detached water heaters in Nova Scotia from 2000 to 2019, categorized by energy source. The data indicates an increasing trend in the total stock, with electricity and heating oil being the primary energy sources.
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 Shares (%) Space Heating 67.5 67.0 67.3 66.9 66.9 65.2 64.1 65.5 68.9 70.1 68.4 70.1 67.0 67.3 68.7 70.3 67.2 68.1 66.8 67.3 Water Heating 17.3 17.6 17.3 17.1...
AI summary The text presents a table showing the distribution of energy usage across different categories such as space heating, water heating, and lighting, along with associated statistics like total floor space and number of households. The data spans multiple years, indicating trends in energy consumption and population growth.
40.6 41.1 41.4 40.5 37.5 35.6 36.8 38.6 38.7 38.5 39.7 40.4 37.6 34.4 32.7 32.6 29.9 30.0 30.8 30.9 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The document presents data on energy use and GHG emissions for the residential sector in Nova Scotia from 2000 to 2019, including heating and cooling degree-day indices and secondary energy use by end-use. It excludes emissions related to electricity production and includes data on coal and propane under 'Other'.
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Shares (%) Space Heating 62.9 62.1 62.1 61.6 62.1 60.7 59.8 61.8 64.7 65.5 62.9 64.2 61.0 61.9 63.4 65.0 62.0 63.2 61.8 62.3 Water Heating 21.0 21.4 21.3 21.1...
AI summary The document presents data on energy usage distribution across various categories such as space heating, water heating, and lighting, along with statistics on total floor space and total households over time. The data indicates trends in energy consumption and growth in floor space and population.
40.6 41.1 41.4 40.5 37.5 35.6 36.8 38.6 38.7 38.5 39.7 40.4 37.6 34.4 32.7 32.6 29.9 30.0 30.8 30.9 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text presents data on heating and cooling degree-day indices, GHG emissions, and energy use in the residential sector of Nova Scotia, including tables with historical data from 2000 to 2019. The data excludes GHG emissions related to electricity production and is sourced from the Office of Energy Efficiency.
0.6 0.5 0.5 0.4 0.5 0.5 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.9 0.9 0.8 0.9 0.9 Shares (%) Electricity 36.5 37.0 37.3 38.8 41.5 43.4 41.6 37.9 37.8 37.2 36.4 35.2 38.8 41.6 43.7 42.8 47.2 47.7 46.6 47.0 Natural Gas 0.0 0.0 0.0 0.0 0.0 0.0...
AI summary The text presents statistical data on energy consumption shares and activity metrics over time, including percentages for electricity, natural gas, heating oil, and other energy sources, as well as total floor space and household numbers. It provides a quantitative overview of energy usage trends.
39.0 39.5 39.8 39.0 36.1 34.2 35.6 37.5 37.7 37.7 39.1 39.9 36.9 33.7 31.8 31.7 28.6 28.6 29.4 29.2 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The document presents data on heating and cooling degree-day indices, GHG emissions, and energy use in the residential sector in Nova Scotia from 2000 to 2019, excluding emissions from electricity production. It includes data on energy use by end-use in apartments.
43.3 43.9 44.3 43.6 40.7 38.8 40.2 41.9 42.1 42.0 43.2 44.0 41.3 38.0 36.2 36.0 32.9 32.9 33.8 33.8 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text presents numerical data on energy use and GHG emissions for residential mobile homes in Nova Scotia across multiple years, including Heating and Cooling Degree-Day Indices and a table summarizing energy use and emissions by end-use from 2000 to 2019.
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Shares (%) Space Heating 70.2 69.9 70.2 69.9 69.8 68.1 66.8 68.1 71.5 72.9 71.4 73.4 70.4 70.8 72.1 74.3 71.6 72.4 71.2 71.7 Water Heating 16.1 16.3 16.0 15.8...
AI summary The document presents data on energy usage distribution across various categories such as space heating, water heating, appliances, lighting, and space cooling, along with related metrics like total floor space and total households. The data is presented in percentages and numerical values across different time points.
43.3 43.9 44.3 43.6 40.7 38.8 40.2 41.9 42.1 42.0 43.2 44.0 41.3 38.0 36.2 36.0 32.9 32.9 33.8 33.8 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text presents numerical data on heating and cooling degree-day indices, along with tables summarizing secondary energy use and GHG emissions by energy source, end use, and activity type in the Commercial/Institutional Sector. It references a 2023 Load Forecast Report and includes a note about GHG emissions data excluding electricity production.
G Emissions Summary Tables by End Use Table 24: Space Heating Secondary Energy Use and GHG Emissions by Energy Source Table 25: Space Heating Secondary Energy Use and GHG Emissions by Activity Type Table 26: Water Heating Secondary Energy...
AI summary The text presents a series of tables detailing secondary energy use and greenhouse gas (GHG) emissions by end use, energy source, and activity type across various sectors such as space heating, water heating, auxiliary equipment, lighting, and space cooling. Additional tables break down energy use in wholesale trade, retail trade, transportation, and information and cultural industries.
es Non-Space Conditioning Secondary Energy Use by End Use and by Energy Source Table 42: Information and Cultural Industries Space Conditioning Secondary Energy Use by End Use and by Energy Source Office of Energy Efficiency, Demand Policy...
AI summary The document contains multiple tables detailing secondary energy use by end use and energy source across various sectors, including offices, educational services, health care, and others. It also notes that GHG emissions data excludes emissions related to electricity production.
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 GHG Intensity (tonne/TJ) 31.7 32.6 30.3 31.9 36.1 34.7 35.4 30.7 27.0 20.9 21.4 24.9 25.3 24.2 21.7 22.8 21.1 17.8 14.9 15.4 1) Data on GHG emissions are prese...
AI summary The text presents data on GHG emissions intensity and energy use in the Commercial/Institutional Sector for Atlantic Canada, excluding electricity production emissions. It references a 2023 Load Forecast Report and includes a table with energy use and emissions data from 2000 to 2019.
ace Cooling 0.5 0.7 0.6 0.8 0.7 0.9 0.6 0.6 0.7 0.5 0.6 0.5 1.0 0.8 0.8 1.0 1.0 1.0 1.3 1.0 Shares (%) Space Heating 50.2 49.2 50.6 47.6 54.5 51.8 51.3 54.8 51.1 46.4 40.8 49.7 49.3 48.5 49.3 50.5 49.4 49.0 43.0 45.4 Water Heating 5.6 5.3...
AI summary The text presents data on energy usage across various categories such as cooling, heating, and lighting, along with energy intensity and floor space metrics. It includes percentages of energy consumption by category and annual figures from 2003 to 2022.
nsity (tonne/TJ) 30.2 31.0 28.8 30.5 34.7 34.6 35.7 30.9 27.2 21.0 21.5 25.1 25.5 24.2 21.8 23.3 21.7 18.1 15.2 15.6 1) Data on GHG emissions are presented excluding GHG emissions related to electricity production. Office of Energy Efficie...
AI summary The text provides data on GHG emissions, excluding those related to electricity production, and references a 2023 Load Forecast Report by Synapse IR-6, focusing on the Commercial/Institutional Sector and Transportation and Warehousing energy use and emissions.
0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 Shares (%) Electricity 50.1 47.6 52.0 50.5 45.1 46.6 45.3 53.9 57.5 65.1 63.2 57.4 57.6 56.2 62.2 60.0 60.5 67.5 72.1 71.2 Natural Gas 0.0 0.0 0.0 1.7 2.2 4.1...
AI summary The text presents data on energy consumption and intensity over time, showing the distribution of electricity, natural gas, and other fuels, along with floor space and energy intensity metrics. The data reflects trends in energy use and efficiency across different periods.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Energy Use for Information and Cultural Industries (PJ) 1.1 1.1 1.1 1.2 1.4 1.2 1.1 1.2 1.2 1.1 1.0 1.2 1.2 1.1 1.2 1.2 1.2 1.2 1.1 1...
AI summary This table presents the total energy use and energy use by source for the Information and Cultural Industries in Nova Scotia from 2000 to 2019, measured in petajoules (PJ). Electricity and Light Fuel Oil and Kerosene are the primary energy sources used, with some fluctuations over the years.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Energy Use for Information and Cultural Industries (PJ) 1.1 1.1 1.1 1.2 1.4 1.2 1.1 1.2 1.2 1.1 1.0 1.2 1.2 1.1 1.2 1.2 1.2 1.2 1.1 1...
AI summary The table presents total energy use and energy use by end use for the Information and Cultural Industries in Nova Scotia from 2000 to 2019. It includes categories such as space heating, water heating, auxiliary equipment, and lighting, with data measured in petajoules (PJ).
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 GHG Intensity (tonne/TJ) 37.4 36.7 36.6 39.1 40.6 38.7 36.0 38.6 36.9 34.1 36.7 41.2 35.1 32.2 31.9 32.3 30.1 27.3 23.3 24.1 1) Data on GHG emissions are prese...
AI summary The text presents data on GHG emissions intensity and energy use in the Commercial/Institutional Sector, specifically in the Educational Services subsector, across multiple years. The data excludes emissions from electricity production and includes information on energy sources and their contribution to GHG emissions.
ooling 0.4 0.6 0.5 0.7 0.6 0.7 0.5 0.5 0.5 0.4 0.5 0.4 0.8 0.7 0.7 0.8 0.8 0.9 1.1 0.8 Shares (%) Space Heating 52.1 51.3 52.6 49.2 56.1 51.1 50.1 53.8 50.0 45.1 39.5 48.2 48.0 47.3 48.1 49.4 48.2 47.3 41.4 43.7 Water Heating 6.9 6.5 6.3 7...
AI summary The text presents data on energy consumption distribution across various categories, including space heating, water heating, and lighting, along with floor space activity in million square meters over a series of years. The data highlights fluctuations in energy usage percentages and physical space metrics.
ling 0.4 0.6 0.5 0.6 0.5 0.7 0.5 0.5 0.5 0.4 0.4 0.4 0.7 0.6 0.7 0.8 0.8 0.8 1.0 0.8 Shares (%) Space Heating 46.7 45.7 46.8 42.1 50.1 45.6 44.4 47.8 44.1 38.1 28.8 39.7 41.8 41.1 41.9 43.2 41.8 41.6 35.9 37.8 Water Heating 9.2 8.7 8.5 10....
AI summary The text presents data on energy usage distribution across different categories such as space heating, water heating, and lighting, along with energy intensity and floor space over time. It provides statistical insights into energy consumption patterns.
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Shares (%) Electricity 53.2 50.4 53.8 52.5 47.8 49.4 48.2 57.3 59.7 65.8 64.1 58.5 57.6 57.2 62.8 59.8 60.6 66.8 70.9 70.6 Natural Gas 0.0 0.0 0.0 1.7 2.3 4.6...
AI summary The text presents data on energy consumption distribution across various fuel types over time, including percentages for electricity, natural gas, light and heavy fuel oil, and other sources. It also includes metrics such as floor space and energy intensity in GJ/m2.
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 historical energy use data for the Arts, Entertainment, and Recreation sector in Nova Scotia from 2000 to 2019, categorized by end use such as space heating, water heating, and lighting. The data shows fluctuations in energy consumption over time.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Energy Use for Accommodation and Food Services (PJ) 3.8 3.8 3.8 3.9 4.8 4.4 4.0 4.6 4.3 3.8 3.3 4.1 4.3 3.9 4.3 4.5 4.4 4.4 4.3 4.4 E...
AI summary The document provides historical data on total energy use and energy use by end use in the Accommodation and Food Services sector from 2000 to 2019, measured in petajoules (PJ). It includes categories such as space heating, water heating, and lighting.
roduction. 2) “Offices” includes activities related to finance and insurance; real estate and rental and leasing; professional, scientific and technical services; public administration; and others. Office of Energy Efficiency, Demand Polic...
AI summary The text discusses the Commercial/Institutional Sector in the context of energy use and GHG emissions, focusing on water heating and energy sources across various years from 2000 to 2019.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Water Heating Energy Use (PJ) 3.1 3.0 2.9 3.6 3.8 3.4 3.1 3.5 3.3 2.9 3.1 3.9 4.0 3.1 3.3 3.3 2.9 2.5 2.7 2.7 Energy Use by Energy So...
AI summary The text presents a table showing the total water heating energy use and its breakdown by energy source from 2000 to 2019 in PJ units. The data includes electricity, natural gas, fuel oil, and other sources, highlighting fluctuations over time.
9 13.8 13.7 Arts, Entertainment and Recreation 1.8 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.0 2.0 2.0 2.0 2.0 2.1 2.1 2.1 2.2 2.2 2.2 Accommodation and Food Services 6.8 6.8 6.8 6.8 7.2 7.3 7.5 7.5 7.5 7.5 7.4 7.4 7.4 7.4 7.4 7.5 7.6 8.0 8.1 8.2...
AI summary The text presents statistical data on energy use and related metrics across various sectors, including arts, entertainment, accommodation, and other services, along with floor space and energy intensity measurements. The data includes energy intensity and greenhouse gas emissions, but some values are missing or not applicable.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total Space Cooling Energy Use (PJ) 3.0 4.2 3.7 4.6 3.5 4.6 3.4 3.1 3.6 2.5 3.0 2.6 5.0 4.4 4.4 5.1 5.4 5.5 6.9 5.2 Energy Use by Energy So...
AI summary The text presents a table showing the total space cooling energy use, energy use by energy source, shares of energy sources, total floor space, and energy intensity over the years from 2000 to 2019. It indicates that electricity is the sole energy source used for space cooling, with no usage of natural gas.
10 1.66 1.60 2.20 1.22 2.24 2.03 1.92 1.98 2.03 1.99 2.78 1.68 1) Data on GHG emissions are presented excluding GHG emissions related to electricity production. Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analy...
AI summary The text presents data on GHG emissions excluding those related to electricity production, focusing on the Commercial/Institutional Sector in the Atlantic region and providing secondary energy use and GHG emissions by activity type from 2000 to 2019.
6.12 6.38 6.49 6.63 6.85 6.94 7.15 7.36 7.46 7.53 7.55 7.58 7.74 7.74 7.65 7.63 7.57 7.54 7.49 7.45 Energy Intensity (MJ/m2) 77.06 72.46 69.52 85.61 90.65 88.38 78.37 87.10 82.54 71.89 73.75 92.64 95.59 72.42 76.27 76.68 67.29 60.18 64.96...
AI summary The text presents numerical data on energy intensity and secondary energy use by end use and energy source in the commercial/industrial sector, including a table with years ranging from 2000 to 2019. It also mentions the Office of Energy Efficiency, Demand Policy and Analysis Division, Market Analysis Group, and references a 2023 Load Forecast Report.
napse IR-6 Attachment 2 Page 42 of 57 Commercial/Institutional Sector Atlantic Table 39: Transportation and Warehousing Non-Space Conditioning Secondary Energy Use by End Use and by Energy Source 2000 2001 2002 2003 2004 2005 2006 2007 200...
AI summary The text presents data on energy use in the transportation and warehousing sector for the commercial/institutional sector in Atlantic Canada, specifically Nova Scotia, from 2000 to 2019, focusing on lighting and auxiliary motors energy use, along with associated floor space and energy intensity metrics.
0.71 0.72 0.73 0.74 0.75 0.77 0.78 0.79 0.79 0.81 0.82 0.82 0.83 0.83 0.83 0.83 0.83 0.84 0.85 0.84 Energy Intensity (MJ/m2) 797.51 785.01 786.41 762.45 1,005.57 801.84 691.99 838.91 727.96 577.30 461.75 688.16 669.24 601.15 671.61 709.42...
AI summary The document contains tables with energy intensity data, heating and cooling degree-day indices, and a section on commercial/industrial energy use by end use and energy source, indicating a focus on energy consumption analysis and forecasting.
ities related to finance and insurance; real estate and rental and leasing; professional, scientific and technical services; public administration; and others. 2) “Other” includes coal and propane. Office of Energy Efficiency, Demand Polic...
AI summary The text provides a table detailing secondary energy use for lighting in educational services across various years, along with corresponding floor space and energy intensity metrics. The data spans from 2000 to 2019 and includes metrics in PJ, million m2, and MJ/m2.
206.34 204.05 197.30 194.51 208.71 197.98 189.98 198.81 197.79 193.89 193.94 193.91 165.48 166.25 177.25 174.73 167.63 183.01 188.48 198.49 Auxiliary Motors Energy Use for Educational Services1 (PJ) 0.6 0.5 0.5 0.5 0.6 0.5 0.4 0.5 0.5 0.5...
AI summary The text presents numerical data on energy use and intensity for educational services in Nova Scotia, including auxiliary motors energy use and floor space over time. It provides metrics that may be relevant to energy efficiency and resource planning discussions.
1.9 2.6 2.6 2.3 1.8 1.8 1.8 1.6 1.7 1.6 0.7 0.5 1.0 0.7 0.7 0.8 1.2 1.3 1.2 1.4 Activity Floor Space (million m2) 3.87 3.92 3.95 3.92 3.90 3.92 3.98 3.99 3.97 4.00 4.05 4.15 4.22 4.34 4.43 4.40 4.38 4.37 4.32 4.29 Energy Intensity (MJ/m2)...
AI summary The text presents data on energy intensity, floor space, and degree-day indices for the commercial/institutional sector in the Atlantic region, including a table with energy use by end use and energy source. The data spans multiple years and includes categories such as heating and cooling degree-day indices.
e IR-6 Attachment 2 Page 52 of 57 Commercial/Institutional Sector Atlantic Table 49: Arts, Entertainment and Recreation Non-Space Conditioning Secondary Energy Use by End Use and by Energy Source 2000 2001 2002 2003 2004 2005 2006 2007 200...
AI summary The document presents a table detailing energy use in the Arts, Entertainment, and Recreation sector for the Commercial/Institutional sector in Atlantic Canada, including lighting and auxiliary motors energy use, along with floor space and energy intensity metrics from 2000 to 2019.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Space Cooling Energy Use for Arts, Entertainment and Recreation (PJ) 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....
AI summary The document presents a table showing energy use for space cooling in the Arts, Entertainment, and Recreation sector from 2000 to 2019. It details energy use by source (electricity and natural gas) and includes metrics like energy use in PJ and shares, as well as floor space in million square meters.
0.24 0.25 0.26 0.27 0.27 0.28 0.29 0.30 0.30 0.32 0.33 0.34 0.35 0.35 0.36 0.37 0.37 0.38 0.39 0.39 2 Energy Intensity (MJ/m ) 116.87 154.32 130.99 158.22 123.84 153.25 111.25 101.15 113.15 76.70 92.63 77.65 147.88 126.51 126.94 146.38 153...
AI summary The text presents a series of numerical values, likely representing energy intensity measurements over time, with corresponding MJ/m² values listed in two rows. The data shows fluctuations in energy intensity, which may be relevant to energy efficiency or resource planning discussions.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Space Cooling Energy Use for Accommodation and Food Services (PJ) 0.2 0.3 0.3 0.4 0.3 0.4 0.3 0.3 0.3 0.2 0.3 0.2 0.4 0.4 0.4 0.4 0.5 0.5 0...
AI summary The text presents a table showing energy use for space cooling in accommodation and food services from 2000 to 2019. It details energy use by source (electricity and natural gas) and includes floor space data. Electricity is the sole energy source used for cooling during the entire period, with consistent 100% share and increasing energy use over time.
1.9 2.4 2.5 2.1 1.7 1.7 1.8 1.5 1.6 1.6 0.6 0.5 0.9 0.7 0.6 0.8 1.2 1.3 1.2 1.3 Activity 2 Floor Space (million m ) 1.92 1.95 1.99 2.01 2.09 2.15 2.25 2.28 2.30 2.33 2.34 2.36 2.38 2.40 2.40 2.45 2.48 2.55 2.56 2.58 Energy Intensity (MJ/m...
AI summary The text presents data on floor space, energy intensity, heating and cooling degree-day indexes for the commercial/institutional sector in the Atlantic region, including a table of energy use by end use and energy source. The data includes figures for various years and is part of a load forecast report.
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 Shares (%) Electricity 25.4 19.1 26.7 22.3 21.4 17.8 13.5 34.9 35.6 40.6 30.1 31.1 31.8 27.7 38.8 34.7 34.1 44.5 48.6 49.1 Natural Gas 0.0 0.0 0.0 2.9 3.8 8.2...
AI summary The text presents statistical data on energy consumption by type (electricity, natural gas, fuel oils, etc.) and floor space over time, indicating trends and proportions of energy usage across different categories.
the calculations used to obtain the 3922 kWh/year/household 29 estimated heat pump energy impact for 2023. 30 (g) Please provide documentation of the E3 electrification model, RESHAPE. Date Filed: June 20, 2023 NSPI (Synapse) IR-7 Page 1 o...
AI summary The document requests documentation of the E3 electrification model and RESHAPE, related to the 2023 Load Forecast Report and heat pump energy impact calculations. NSPI is responding to Synapse Energy Economics' information requests under NSUARB M11108.
1 (g-h) E3’s RESHAPE model is designed to simulate diversified system-level building 2 electrification load shapes. System diversity is captured in the model through a regionally 3 specific sample of buildings representing the housing stoc...
AI summary The document discusses the RESHAPE model used by E3 to simulate building electrification load shapes in Nova Scotia. It outlines data sources and methods used to characterize residential and commercial building stocks, including data from NS Power, NRCAN, EIA, and others.
t all- 22 electric and all-electric and their space heating energy intensity (kWh/m2). Combined 23 with floor area estimates for a typical small and medium/large commercial customer 24 derived from CBECS, E3 estimated the space heating dem...
AI summary The document discusses the estimation of space heating energy intensity for commercial customers in Nova Scotia, using data from CBECS and NS Power, and scaling a sample of buildings from the New England region to represent the province's commercial heating service demand fuel mix. Load forecast data for 2025 and 2030 are also presented.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Residential Water Heaters (WH) (Section 4.4, p 37-38) 4 5 (a) Are more efficient hot water end-u...
AI summary NSPI's response to Synapse Energy Economics' request regarding the 2023 Load Forecast Report discusses the consideration of efficient water heaters, efficiency standards, and the impact of heat pump (HP) adoption on water heating load. It notes that efficiency improvements are factored into the intensity equation based on EIA forecasts.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests CONFIDENTIAL (Attachment Only) 1 Request IR-9: 2 3 Residential Electric Vehicles (EV) (Section 4.4, pp 38-43) 4 5 (a) Please provide...
AI summary The document outlines information requests from the NSUARB to NSPI regarding the 2023 Load Forecast Report, focusing on residential electric vehicle (EV) sales forecasts, load shaping tools, time of use tariffs, and EV load management assumptions. The requests aim to clarify data sources, methodologies, and assumptions used in the forecasting process.
CONFIDENTIAL (Attachment Only) 1 of LDV weekly driving patterns expressed as the probability that a driver is at a given 2 location or is driving. 3 4 5 6 The driving population is characterized by drivers’ EV type and access to charging....
AI summary The text discusses modeling LDV weekly driving patterns and the impact of EV types and charging access on load shapes. It contrasts unmanaged and managed charging scenarios, highlighting how drivers respond to electric rates and the lack of consideration for time-varying prices in unmanaged scenarios.
ped over time to achieve those savings. 18 19 (g) Please refer to Partially Confidential Attachment 2. 20 21 (h) Please refer to Attachment 3. 22 23 (i) Please refer to Attachment 1. Date Filed: June 20, 2023 NSPI (Synapse) IR-9 Page 5 of...
AI summary The document includes references to attachments and a load forecast report from Synapse, detailing energy consumption and peak demand for various vehicle types and charging locations.
,000 100,000 50,000 - 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 EV Sales 2023 Forecast EV Sales 2022 Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-9 Attachment 1Page 2 of 9 Forecast...
AI summary The text provides data on population trends in Halifax and Nova Scotia from the 2011 to 2021 censuses, as well as a reference to a source for vehicle sales data. It also mentions a forecast for new vehicle sales and EV sales projections for 2023 and 2022.
Approximate sales in Halifax and rest of NS based on ratio of census population Forecast begining 2022 assumes average of 2016 to 2021 Growth rate of 0.5% from 2023 to 2045 EV Sales based on mandates Total Halifax Rest of Year NS Total Hal...
AI summary The text presents sales data for Halifax and the rest of Nova Scotia based on census population ratios and forecasts EV sales from 2020 to 2029, including PHEV and BEV percentages. The data includes historical sales trends and projected growth rates.
kWh/year kW/vehicle on peak LDV 4,323 0.85 MDV 8,205 1.62 Transit Bus 113,890 7.33 2022 Energy 2022 Peak Year BEV tot PHEV tot MDV HDV Total GWh Peak MW Avg kW per car 2021 709 238 947 4 0.8 0.9 2022 1,768 1,096 2,864 12 2.4 0.85 2023 3,50...
AI summary The document presents data on energy consumption and peak demand for different vehicle types (LDV, MDV, Transit Bus) and provides projections for BEV and PHEV totals, energy usage in GWh, peak demand in MW, and average kW per car from 2021 to 2032.
annual basis, due by July 31 and January 31, which started July 31, 2020. 4 In its Decision regarding the approval of the Solar Garden Rate Rider, the NSUARB also provided the following: NS Power accepted Synapse’s recommendation that NS P...
AI summary The NSUARB required NS Power to include specific performance metrics in its Smart Grid Project Reports, including generation data, outage details, program participation, and bill impacts. Synapse also recommended additional monthly reports on the solar garden's performance.
vided in Attachment 3 – Use Case Testing Dashboard. The blue shading in this attachment denotes where devices are connected, and where NS Power is gathering data and running use case tests as planned. Please refer to Attachment 4 – Solar G...
AI summary The document discusses the progress of the Smart Grid Nova Scotia Project, including use case testing, data collection, and the development of a preliminary economic analysis model. Attachments provide details on device connectivity, specific metrics, and lessons learned. The final business case will include grid and customer benefits from utility-controlled assets.
C&I Building Management System Bidirectional Charger Installations 11 Jan - 29 Jun Testing & Evaluation ESP Release 1&2 Use Case Testing Baseline Data Collection Bidirectional Charger Testing Data Analysis and Reporting Customer Experience...
AI summary The text outlines various phases and activities related to the implementation and evaluation of building management systems and bidirectional charger installations, including testing, data collection, and customer experience surveys, as part of an energy system platform initiative.
t and Corresponding ev.energy Grid Signal ...............................24 Figure 22 – Early Example Results from the ev.energy Wind Following Use Case, for the period November 27 to December 1, 2022 .........................................
AI summary The document includes a redacted section from a 2023 Load Forecast Report by Synapse, an attachment from a Smart Grid Semi-Annual Report, and an update report on SGNS use case testing. These documents provide insights into load forecasting, smart grid initiatives, and use case testing related to energy systems.
ed’ charging, which is when the driver opts out of both demand response events and smart charging in order to receive energy to their vehicle immediately. It is observed that smart charging sessions Page 9 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The document discusses patterns in EV charging behavior, noting that smart charging sessions are typically longer and consume more energy, often occurring overnight. Charging away from home is shorter, and boosted charging events are not high energy consumption events. Seasonal variations in baseline energy consumption from ChargePoint devices are also analyzed.
Avoided Generation & • EVSE control signal latency (seconds) • System load (MW) be compared with contingency None contingency event to satisfy 10 Pilot 1/R2b Industrial Rate (EVSE5) target charge rate • Value of discharge capacity ($/kW, C...
AI summary The text discusses metrics related to EVSE control signal latency, system load, and V2G discharge capacity, focusing on industrial rate (EVSE5) and pilot testing for load curtailment and reserve capacity.
• Expected and/or forecasted EVSE curtailment kW versus actual curtailment kW (%). [note: this text was absent in the • Available discharge power versus actual discharge power (%) measured for V2G. previous report in error] EV V2G Chargers
AI summary The document discusses metrics related to EVSE curtailment and V2G discharge power, comparing expected/forecasted values with actual performance. These metrics are presented as part of a report, with a note indicating an error in the previous report.
duction forecasts value-stacking with other use-cases (%) • Forecasted PV Output (kW) and energy (kWh) versus actuals (%)
AI summary The text discusses the comparison between forecasted and actual photovoltaic (PV) output in terms of kilowatts (kW) and energy in kilowatt-hours (kWh), as well as the value-stacking of PV with other use-cases, presented as a percentage.
• Forecasted PV Output (kW) and energy (kWh) versus actuals (%)
AI summary The text references forecasted photovoltaic (PV) output in kilowatts (kW) and energy in kilowatt-hours (kWh) compared to actual performance in percentages. This indicates a focus on evaluating the accuracy of energy generation forecasts for PV systems.
• PV Inverter response times to target PF setpoint after receiving signal (Seconds) • Value of Lost Load (VOLL) ($/kW) • Target PF setpoint versus actual PF measured (%)
AI summary The text outlines technical metrics related to photovoltaic (PV) inverters and the value of lost load, focusing on response times to power factor (PF) setpoints and the difference between target and actual PF measurements.
, 2022 Siemens Canada Limited: Date Terrance Cormier Siemens SGA PMO Lead Unrestricted Page 10 of 15 Document # PM-FM-011 Version 5 2022-08-10 . . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9...
AI summary The document contains redacted information from a 2023 Load Forecast Report and a Smart Grid Semi-Annual Report, including attachments and pages related to energy forecasting and grid management. It includes a document version and date, but the content is partially confidential and not fully visible.
0 . CD 0 B -h 0 C') CD 0 CD :7 -, 0 0 1 0 :7 C) CD' 0 (I)' C) CD CD_. Ci) CD cr -' -c CD CD O 0 -o CD CD z :7 (/)cJz . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 124 of 205...
AI summary The document contains a redacted section of a report from NS Power, certified by Sanjeev Pushkarna, regarding the 2023 Load Forecast and a Smart Grid Semi-Annual Report. Confidential information has been removed.
; any contributions made during this fiscal year can be counted, even if the final document will only be published in a future fiscal year. Media products include press releases, news coverage, etc. Did you produce or contribute to any kno...
AI summary The text discusses knowledge and media products produced during the fiscal year, including press releases, news coverage, and video content. It also references a REDACTED 2023 Load Forecast Report and a Smart Grid Semi-Annual Report. Performance measures are mentioned in section 4.
0 $ from new revenue stream (where applicable) $0.00 Other economic indicator, please specify: n/a pg. 8 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 176 of 205 CI C0010788...
AI summary The document discusses progress on socio-economic and technical performance targets for a solar garden project, noting that only 40% of customers are subscribed, falling short of the 100% target. Additionally, no C&I solar customers have been commissioned, though this is expected by Q3 2022. The project has experienced minor delays due to COVID-19 restrictions, but these have had little impact on the budget.
0 $ from new revenue stream (where applicable) $0.00 Other economic indicator, please specify: n/a pg. 9 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 186 of 205 CI C0010788...
AI summary The document discusses the performance of bi-directionally capable EV charging stations and V2G capability utilization, noting that targets for the number of stations and vehicles using the technology were not met due to new technology implementation challenges.
bilities): NSP began self-identification in 2021; data will be provided in subsequent reports What share of the organization is owned by individuals who identify as LGBTQ2? Select the category that most closely applies. (8) Not Available N...
AI summary The document includes data on Nova Scotia Power's (NSP) self-identification efforts related to LGBTQ2 representation in 2021, with information to be provided in future reports. It also references a redacted 2023 Load Forecast Report and a Smart Grid Semi-Annual Report.
4% 4% 3% 2% 1% 1% 1% 1% 1% 1% 2% 2% 2% 2% 5% 6% 7% 7% 7% 7% 11% Baseline Winter Charging Distribution (2021 and 2022) - Only includes Weekday charging on non-event days and non-holidays TOD (HE) 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00...
AI summary The document presents baseline winter charging distribution data for 2021 and 2022, showing average and total kWh usage across different times of day. It also references the 2023 Load Forecast Report (NSUARB M11108) and NSPI's responses to Synapse Energy Economics information requests, though some content is redacted.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-10: 2 3 Solar Generation (PV) (Section 4.4, pp 44-45) 4 5 (a) Please provide supporting data for the av...
AI summary NSPI responded to Synapse Energy Economics' request regarding solar generation in the 2023 Load Forecast Report. They provided average PV installation capacities, assumed capacity factors, and discussed the impact of PV on summer peaks, noting ongoing data collection efforts.
2023 Load Forecast Report Synapse IR-10 Attachment 1 Page 1 of 1 PV Forecast Based on 15% growth to 2030 and 10% growth from 2030-2033
AI summary The document presents a 2023 Load Forecast Report by Synapse, outlining a PV Forecast with 15% growth to 2030 and 10% growth from 2030 to 2033.
at Pump Water Heater (Air-Water) 976.65 976.65 - Calculator V5 - (October 26, 2018) Old vs. new space/water heating system Fossil Fuel Displacement or Brand New System Is there an existing space cooling system OR were t None Customer Rate...
AI summary The text presents a calculation related to energy usage for a centralized heat pump system (Air-Air) under the R12 customer rate class, including assumptions about energy demand and peak load. It also references a 2023 Load Forecast Report and mentions confidential information that has been redacted.
2023 Load Forecast Report Synapse IR-13 Attachment 1 Page 3 of 4
AI summary The text refers to the 2023 Load Forecast Report by Synapse, specifically Attachment 1, Page 3 of 4. It does not provide further details or discussion on the content of the report.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-15: 2 3 Price Data (Section 4.5, pp 52-53) 4 5 (a) Please provide the data and calculations used to pro...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report, including the data and calculations for electricity prices and the use of a -0.15 price elasticity in SAE models. NSPI referenced historical studies on price elasticity ranging from -0.05 to -0.30 and noted that the -0.15 value is near the mid-range.
1 Request IR-16: 2 3 Demand Side Management (Section 4.6, pp 54-56). 4 5 (a) Please provide the source data for the DSM values used in this forecast. 6 (b) Please provide the DSM values used in the latest IRP. 7 (c) Please provide the DSM...
AI summary The request seeks source data for DSM values used in the forecast, including the latest IRP and E1 potential study. The response indicates that DSM values for 2023-2025 are based on EOne’s Settlement Plan and values beyond 2025 are based on the EOne Potential Study. A table is referenced with energy and demand values for various years.
Year Month NonResSales2 EESavingsProfiled WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct Bad XMissing YMissing 2019 4 319,864.16 51,305.00 0.00 91,701.92 139,620.89 38,828.00 0.00 0.00 0.00 0.00 0.00 2019 5 316,423.96 47,537.28 0.00 66...
AI summary The text presents a table containing data related to non-residential sales, energy efficiency savings, weighted cooling, heating, and other factors, along with customer counts and missing data indicators for various months from 2019 to 2020.
Year Month NonResSales2 EESavingsProfiled WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct Bad XMissing YMissing 2031 10 66,533.17 13,818.61 25,150.68 130,394.92 39,609.00 0.00 0.00 0.00 0.00 1.00 2031 11 72,729.97 288.19 58,921.91 131,4...
AI summary The text presents a table with data spanning multiple years and months, including non-residential sales, energy efficiency savings, weighted cooling, heating, and other factors, along with customer counts and various metrics. The data appears to be related to energy consumption and efficiency in Nova Scotia.
INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 2 of 6 2020 2021 2022 2023 Forecast Sales 4540 4718 4715 4830 Residential Weather variance -91 -141 -127 - Non-weather variance 226 42 263 - Actual Sales 4675 4...
AI summary The 2023 Load Forecast Report provides a comparison of forecasted and actual electricity sales from 2020 to 2023, including adjustments for weather and non-weather factors, highlighting variations in residential demand.
FORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 3 of 6 Dec-20 Jan-21 Feb-21 Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 2020 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021...
AI summary The text presents a portion of the 2023 Load Forecast Report, containing numerical data across multiple months, likely related to energy load forecasting. Due to redaction, specific details and context are not fully available.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-29: 2 3 Net System Requirement (Section 9). 4 5 (a) Please...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report, specifically addressing the source data and calculations for Figures 53, 54, and 55. The response directs to specific tabs and cells in the Attachment 4 of the report.
1 (b) The basis for the ELCC of 48 percent is described in the 2023 Load Forecast Report on 2 pages 77 and 78, as follows: “DR forecasts continue to use an effective load carrying 3 capacity (ELCC) of 48 percent to account for the fact tha...
AI summary The text references the 2023 Load Forecast Report and various attachments, discussing the Effective Load Carrying Capacity (ELCC) of 48 percent for demand response (DR), the calibration period for peak models, and how DSM potential is calculated using a blended average coefficient of 49 percent.
cannot explain. All forecasting models have uncertainty in their predictions, and the 4 unexplained item attempts to quantify this. It is calculated as the difference between 5 forecast minus actuals. 6 7 (h) Please refer to Attachment 2....
AI summary The document discusses the 2023 Load Forecast Report, which includes an efficiency study for Nova Scotia from 2021 to 2045. It references an earlier study prepared by Navigant for EfficiencyOne and filed in August 2019. The report addresses forecasting models and their uncertainties.
..........101 Page ii ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 5 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 20...
AI summary The document presents the 2023 Load Forecast Report, which includes an attachment from Synapse IR-30, focusing on Nova Scotia Energy Efficiency and Demand Response Potential Study for the period 2021-2045.
sults Across Demand Response Scenarios ..........................113 11.6.2 Comparison of Potential Results Across Scenarios ....................................................114 11.7 Demand Response Snapback Effects .......................
AI summary The document outlines a study on energy efficiency and demand response potential in Nova Scotia for the period 2021-2045. It includes sections on demand response scenarios, snapback effects, and concludes with findings on energy efficiency and demand response strategies.
Savings by Sector (MW, gross at generator) .................................................................................................................................................. 43 Figure 5-5. EE Economic Potential, Electricity...
AI summary The document provides a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period from 2021 to 2045. It includes figures related to energy savings by sector and economic potential.
..................................................... 70 Figure 8-11. EE Base Case Market Potential, Cumulative Electricity Savings by End Use (GWh, net at generator)............................................................................
AI summary The text presents a series of figures illustrating energy efficiency (EE) market potential, including cumulative electricity savings by end use, customer segment, and winter peak demand savings. These figures provide insights into the effectiveness of various EE measures in residential and BNI (Business and Non-Industrial) sectors for the year 2021.
ure 8-18. EE Base Case Market Potential, 2021 Top 40 BNI Measures for Winter Peak Demand Savings (MW, net at generator) ................................................................................................................ 78 Fig...
AI summary The document includes figures related to energy efficiency (EE) market potential, savings, and investment for the period 2021-2045, including residential cumulative achievable potential sensitivity analysis. It references the 2023 Load Forecast Report and is part of a study conducted by Synapse IR-30.
Page iii ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 9 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 E. EX...
AI summary EfficiencyOne commissioned Navigant Consulting to conduct energy efficiency and demand response potential studies for Nova Scotia from 2021 to 2045. The study focuses on residential and business, nonprofit, and institutional sectors, aiming to assess energy efficiency measures, operational activities, and user behavior changes to reduce energy consumption. Results will inform integrated resource planning and program design.
mic and market savings potential across Nova Scotia. These results 1 will be used to inform integrated resource planning (IRP), and energy efficiency and demand response program design in Nova Scotia. E.1 Estimation of Energy Efficiency Po...
AI summary The text discusses the estimation of energy efficiency potential in Nova Scotia using Navigant's DSMSim™ model. The model calculates technical, economic, and market savings potential, considering different types of efficiency measures and their impacts across various sectors and customer segments.
at is that, by definition, technical potential calculation does not consider participation overlaps. Therefore, the technical potential estimates for each DR option should be considered independently. Navigant assessed cost-effectiveness o...
AI summary The document discusses the technical and economic potential of energy efficiency (EE) and demand response (DR) in Nova Scotia, noting that technical potential calculations do not account for participation overlaps. Navigant assessed DR options, calculating achievable potential by multiplying participation assumptions with technical estimates and considering customer opt-out during events.
tial Electricity Savings (GWh, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 4 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 13 of 355 Nova Scot...
AI summary The document presents cumulative electricity savings potential from energy efficiency and demand response programs in Nova Scotia over a 25-year period, ranging from more than 2,000 GWh to just under 3,500 GWh (net at generator). It distinguishes between technical and economic potential savings (gross) and market potential (net of freeridership).
port Synapse IR-30 Attachment 1 Page 14 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Figure ES-5 presents the technical and economic electricity savings at generator potential as a percentage of cu...
AI summary The document discusses energy efficiency and demand response potential in Nova Scotia from 2021 to 2045, presenting technical and economic electricity savings as a percentage of total sales, ranging from 66% to 75% annually. It also outlines market potential scenarios with cumulative savings ranging from 19% to 31% over 25 years.
arios; (1) maximum achievable, (2) mid case, (3) base case, and (4) low case, and ranges between 375 MW and 600 MW (net at generator) over the 25-year period. Market potential is net of freeridership. Figure ES-8. EE Market Potential Winte...
AI summary The document outlines energy efficiency (EE) and demand response (DR) potential in Nova Scotia, showing winter peak demand savings ranging from 375 MW to 600 MW over 25 years. It also notes that technical and economic savings could reach 85% to 99% annually, largely due to fuel switching in HVAC systems. Line loss differences between studies are highlighted, affecting demand forecasts.
could be achieved under the specific set of assumptions outlined in this study. Program design is typically a separate activity and is outside the scope of this study. 1.3.2 Measure Characterization The scope of this study employed both pr...
AI summary This section outlines the methodology used for measure characterization in the study, emphasizing the use of primary and secondary data sources, the focus on high-impact technologies, and the limitations of assumptions regarding future technologies and societal changes.
h must be applied to dozens or in some cases hundreds of energy efficiency measures) are limited in their ability to accurately predict adoption for specific measures or in specific customer segments. ©2019 Navigant Consulting, Ltd. Page 1...
AI summary The text discusses the limitations of forecasting models in predicting the adoption of energy efficiency measures, especially at the individual measure or customer segment level. It highlights that while aggregate results can be more reliable, forecasting inaccuracies can occur at the measure-level and may offset each other when aggregated. More detailed techniques exist but are not typically warranted due to increased costs.
sures analyzed in studies such as this are not typically warranted considering the dramatic increase in costs one would have to incur to calibrate a different adoption model for every single measure. 1.4 Interpreting Results This report in...
AI summary This section outlines the methodology for interpreting energy efficiency savings potential results in Nova Scotia, emphasizing aggregated data and measure-level analysis. It also describes the report's organization, including sections on data, technical and economic potential, market approaches, and forecasting.
brium market share, behavioural measures, investment and incentive strategy, re-participation, and model calibration. Section 7 – discusses the Reference Forecast Approach and scenario configuration. Section 8 – presents the Energy Efficie...
AI summary The document outlines various sections of a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045. Sections cover market share, forecasting approaches, energy efficiency measures, demand response methodologies, and results of cost-effectiveness analyses.
t Report Synapse IR-30 Attachment 1 Page 28 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 2. GLOBAL DATA Navigant aggregated multiple data sources to simulate many elements of the market conditions...
AI summary The document outlines the data sources used by Navigant to model energy efficiency and demand response potential in Nova Scotia from 2021 to 2045. Key data includes energy forecasts, residential and industrial building stock, and historical consumption data from various sources like Nova Scotia Power and EfficiencyOne.
756 Total 480,971 Source: Navigant analysis based on Nova Scotia and StatsCan data ©2019 Navigant Consulting, Ltd. Page 21 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 30 of 355 No...
AI summary The document discusses the BNI sector in Nova Scotia, divided into four segments: Small Commercial, Large Commercial, Institutional, and Industrial. The segmentation is based on 2018 NS Power Data and EfficiencyOne’s Rate and Bill Impact Analysis reports, reflecting blended averages based on rate-code / size bins.
specifying the particular type of equipment used to satisfy that need. ©2019 Navigant Consulting, Ltd. Page 22 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 31 of 355 Nova Scotia En...
AI summary The document outlines the end uses by sector in the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It emphasizes the categorization of energy use for quality control and forecasting purposes, with specific examples provided for residential and other sectors.
Electronics & IT Other Source: Navigant 2.3 Fuel Shares Navigant developed fuel share and equipment data for each end use based on the segmentations defined in the previous sections, using the 2019 Nova Scotia Baseline Study results for sp...
AI summary The document outlines the methodology used by Navigant to develop fuel share and end use allocation data based on the 2019 Nova Scotia Baseline Study and the 2018 End Use Intensity Model. This data is used to understand energy consumption patterns and is essential for forecasting and planning purposes.
t Report Synapse IR-30 Attachment 1 Page 32 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 2.5 Electricity Consumption EfficiencyOne provided Navigant with information on actual sales and customer nu...
AI summary The document discusses electricity consumption in Nova Scotia in 2019, segmented into residential and BNI sectors. Data from the 2018 Emera MD&A report and the 2018 Nova Scotia Power load forecast were used to estimate consumption levels before demand-side management (DSM) was applied.
i. Retrofit (RET): where the model considers the baseline to be the existing equipment, and uses the energy and demand savings between the existing equipment and the efficient technology during technical potential calculations. RET also ap...
AI summary The text outlines three approaches—Retrofit (RET), Replace On Burnout (ROB), and New Construction (NEW)—for calculating energy and demand savings in efficiency programs. It describes how each method defines baseline and efficient technologies, and how costs are calculated during economic screening.
2. Sector, and End Use Mapping: The team mapped each measure to the appropriate end uses, customer segments and sectors. Where Nova Scotia-specific information was not available, Navigant utilized secondary data, including internal Navigan...
AI summary The text outlines the methodology used to map energy efficiency measures to customer segments, sectors, and end uses, utilizing both primary and secondary data sources. It also describes parameters such as annual energy consumption, peak demand, fuel type applicability, measure lifetime, and incremental costs for energy-efficient technologies.
same baseline technology density into a single competition group to avoid the double-counting of savings. (Appendix A provides further explanation on competition groups). 7 See the accompanying model input workbook for density and saturati...
AI summary The document discusses energy efficiency measure characterization approaches, focusing on residential and BNI measures, and outlines methods for analyzing energy and demand savings. It references the Load Forecast Report and mentions the use of a model input workbook for density and saturation sources.
for the main measure characterization variables. 3.3.1 Energy and Demand Savings Navigant took three general bottom-up approaches to analyzing residential and BNI measure energy and demand savings: 1. Program Evaluation Data: For most meas...
AI summary Navigant used multiple methods to analyze energy and demand savings, including program evaluation data, TRM algorithms, and engineering analysis. They also relied on EfficiencyOne and TRM data for incremental cost analysis and considered building stock and densities.
st data. Navigant conducted secondary research and used a variety of other publicly-available cost data sources indicated in the accompanying measure input detail. 3.3.3 Building Stock and Densities Navigant relied heavily on the 2019 Nova...
AI summary Navigant used the 2019 Nova Scotia Baseline Study and other secondary sources to estimate equipment densities and saturations for energy efficiency measures. Adjustments related to future codes and standards were also estimated based on Canada’s Forward Regulatory Plan 2019-2021.
panying EE measure input workbook (Appendix E) for codes and standards adjustments. ©2019 Navigant Consulting, Ltd. Page 27 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 36 of 355 N...
AI summary This section outlines Navigant's approach to calculating the technical potential of energy efficiency measures in Nova Scotia, focusing on total energy savings across sectors and end-use categories, and describes the use of the DSMSim™ model for estimating demand-side resource potential.
te the technical potential for demand-side resources considered for this study. DSMSim™ is a bottom-up technology-diffusion and stock-tracking model implemented using a System Dynamics framework. 9 9 See Sterman, John D. Business Dynamics:...
AI summary This section presents the technical potential for energy efficiency and demand response in Nova Scotia from 2021 to 2045. It uses DSMSim™, a bottom-up model based on System Dynamics, to estimate electricity savings potential by sector, including residential and BNI sectors, with varying trends over time.
of consumption ranges between 70% to 81% annually. These technical potential savings are due in part to fuel- switching measures, allowing a very large portion of HVAC load to be technically removed. Figure 4-3. EE Technical Potential, Ele...
AI summary The document discusses energy efficiency (EE) technical potential in Nova Scotia, showing that savings could reach 70% to 81% annually across sectors. This is partly due to fuel-switching measures in HVAC systems. Winter peak demand savings are even higher, reaching 78% to 99% in the BNI and residential sectors. Line loss factors differ between the study and system requirement documents, influencing the results.
7%. The potential study used these higher line loss factors from the 2014 COSS in response to stakeholder feedback to the modelling assumptions, and because it reflects the most recent data available. Figure 4-4. EE Technical Potential, Wi...
AI summary The document discusses the technical potential of energy efficiency (EE) in Nova Scotia, highlighting the significant role of electric space heating and the limited impact of lighting due to expected code and standard updates. Data from the 2014 COSS was used to refine line loss factors based on stakeholder feedback and recent data availability.
𝐶𝐶𝐶 + 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶) Where: » PV( ) is the present value calculation that discounts cost streams over time; using the selected discount rate (6.84%); » Avoided Costs are the monetary benefits resulting from electricity and capacity...
AI summary The document explains the calculation of Total Resource Cost (TRC) ratios for energy efficiency measures, using present value of benefits and costs over the measure’s life. Economic potential is determined by selecting the most effective measure from each competition group that meets the TRC threshold, ensuring no double-counting.
emand Savings by Sector (MW, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 41 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 50 of 355 Nova Scoti...
AI summary The document compares the economic electricity potential in residential and BNI sectors using the program administrator cost (PAC) test screen and the total resource cost (TRC) test. Results show similar economic potential under both tests, with slightly more measures qualifying under the PAC test.
ng and high peak coincidence of BNI lighting technologies. Figure 5-8. EE Economic Potential, Winter Peak Demand Savings by End Use (MW, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 47 . REDACTED (CONF...
AI summary The document discusses the economic energy savings potential from the top 40 highest energy-saving measures for residential sectors in 2021, highlighting that most technical potential is economically achievable. Specific measures like Residential Networked/Connected - Indoor LED Lamps and Residential Ventless Heat Pump Dryers are excluded due to not meeting the TRC threshold of 1.0.
t technical potential is economically achievable, passes a TRC test of 1.0. Therefore, the measure mix of top 10 to 20 measures is largely the same as the technical potential results for both sectors. Figure 5-11. EE Economic Potential, 20...
AI summary The document presents economic potential for energy efficiency (EE) measures in residential and BNI sectors, highlighting that the top 10 to 20 measures align with technical potential results. Figures show savings in winter peak demand, sourced from Navigant analysis.
e adoption of DSM measures can be broken down into calculation of the “equilibrium” market share and calculation of the dynamic approach to equilibrium market share, as discussed in more detail below. Market potential differs from program...
AI summary The text discusses the methodology for calculating market potential in energy efficiency, distinguishing it from program potential. It emphasizes the use of Total Resource Cost (TRC) as a cost-effectiveness measure with a threshold of 0.7, aligned with Nova Scotia regulatory practices. The approach focuses on portfolio-level or sector-level analysis rather than program-specific details.
gy presented here focuses primarily on portfolio-level or sector-level approaches. ©2019 Navigant Consulting, Ltd. Page 52 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 61 of 355 No...
AI summary The text discusses the methodology used in assessing energy efficiency market potential, emphasizing the use of the Total Resource Cost (TRC) as a primary screening tool, adjusting diffusion parameters based on industry data, and incorporating administrative costs at both measure and portfolio levels.
ipient of model results with a level of comfort that simulated results are reasonable. For this study, Navigant took a number of steps to ensure that forecast model results were reasonable, including: • Identifying the subset of potential...
AI summary Navigant Consulting ensured the reasonableness of forecast model results by calibrating them against historic program achievements, using data from 2017 and conducting detailed backcasting for key lighting measures in ENS programs.
reness. If the model accurately depicts the system that it attempts to simulate, results (Navigant Simulated Backcast) should be on the same order as actuals (EfficiencyOne’s Historical Achievements). It is worth noting that because DSMSim...
AI summary The document discusses the accuracy of the DSMSimTM model in simulating energy efficiency technology adoption, particularly residential screw-in LEDs. It highlights that while the model fits well for 2017 and 2018, it struggled to explain higher savings in 2016, possibly due to external factors like marketing campaigns or economic trends.
odel at varying levels of aggregation, using the TRC benefit-cost test as a screen set to 0.7. At-the-meter, net savings results are shown by sector, end use category, and by highest-impact measures. 8.1 Comparison of Energy Efficiency Sav...
AI summary The text discusses energy efficiency market potential across different scenarios, showing cumulative electricity savings by 2045. It outlines how measures are reparticipated after reaching the end of their useful life and provides forecasted savings for low, base, and maximum scenarios.
x F. Figure 8-13. EE Base Case Market Potential, Cumulative Electricity Savings by Customer Segment (GWh, net at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 73 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 202...
AI summary The document presents figures illustrating energy efficiency (EE) market potential and cumulative electricity savings by customer segment in Nova Scotia, highlighting residential single-family, industrial, and large commercial segments as dominant contributors to winter peak demand savings.
ata has been summarized in the appendix at the sector level due to the volume of data. ©2019 Navigant Consulting, Ltd. Page 82 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 91 of 35...
AI summary Navigant conducted a parametric sensitivity analysis to assess the impact of varying input parameters on the cumulative achievable energy savings potential by 2045 in the Base scenario, varying factors such as CO2 prices and screening thresholds.
ted winter peak demand by customer class and segment over the potential analysis period. The baseline projections aimed to define and forecast customer data for the study period, similar to the market 16 These business types were sourced f...
AI summary The document discusses the methodology for projecting customer counts and winter peak demand by customer class and building type, using 2018 as the base year. It outlines the selection of customer classes and building types for analysis and the use of data from the DnB dataset provided by EfficiencyOne.
Develop Separate Peak Demand •Use EV adoption forecast and estimated peak demand from charging to develop peak Projections for EVs demand projections Source: Navigant The first step in this approach was to define the peak period. Based on...
AI summary This text discusses the methodology used to develop peak demand projections for electric vehicles (EVs) in Nova Scotia. Navigant identified the peak period as 5-8 pm during December, January, and February. They used data such as 8760 system data, retail sales forecasts, and load forecasts from NSP to estimate coincident peak demand by customer class and building type. EV-specific projections were based on per-vehicle impacts and vehicle adoption forecasts from a simulation tool.
fficiency Potential Study were considered in the baseline winter peak projections: (1) low, (2) base, and (3) mid. These correspond to the scenarios low, base, and high, respectively, in the DR study. Figure 10-8 shows the baseline peak pr...
AI summary The document discusses winter baseline peak demand projections under different energy efficiency scenarios, noting that higher energy efficiency savings lead to lower peak demand. It clarifies that the peak demand definition differs from NS Power's forecast and excludes certain customer segments from the DR forecast.
tric Vehicles (PHEV), but not Hybrid Electric Vehicles (HEV). ©2019 Navigant Consulting, Ltd. Page 93 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 102 of 355 Nova Scotia Energy Eff...
AI summary The document forecasts vehicle adoption in Nova Scotia, predicting a low penetration of plug-in hybrid and battery electric vehicles (PEVs) through 2045. Load impacts from PEVs are estimated using a 0.6 kW/vehicle impact value from a 2019 report, assuming charging management programs are in place.
rticipant marketing and recruitment costs, annual program administration costs, O&M costs, and customer incentives. 10.4.1 Demand Response Base Case Assumptions 10.4.1.1 Participation and Hierarchy Participation assumptions are based on re...
AI summary The text discusses assumptions related to demand response (DR) participation, including the use of industry-standard S-shaped ramp curves over a 5-year period, and references participation assumptions by customer class and DR option. It also mentions the use of secondary sources such as FERC's DR program survey and detailed documentation in an Excel spreadsheet.
Technology Enablement Cost O&M Cost Participant Cost 32 The enabling technology costs represents the incremental costs associated with controls and communications for making the device DR-enabled. These costs are not expected to decline me...
AI summary The text discusses enabling technology costs for demand response (DR) devices, which are modeled as static due to their incremental nature. It also references a 2019 NS Power WACC/AFUDC value and a 2014 Cost of Service Study. A cost-effectiveness assessment is mentioned, focusing on DR options with benefit-to-cost ratios of 1.0 or greater, and describes three scenarios for potential estimates.
under the low scenario for DR 36). These variations in market adoption from the energy efficiency potential analysis were fed into the saturation assumptions to calculate DR potential. • Programmatic assumptions: In addition to these two i...
AI summary The document discusses variations in demand response (DR) program participation based on different scenarios, including differences in incentives, marketing expenditures, and enrollment levels. It notes that residential and small BNI customer participation is more sensitive to these factors than larger BNI customers.
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.
P.O. Box 64 Toronto, ON M5X 1B1 Canada 416.777.2440 navigant.com Reference No.: 207668 May 09, 2019 ©2019 Navigant Consulting, Ltd.. . Date Filed: August 14, 2019 Page 1 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast...
AI summary This document outlines the methodology for two studies on energy efficiency and demand response potential in Nova Scotia for 2021-2045. The studies aim to quantify electricity and demand savings and associated costs, by sector and end use, to inform Nova Scotia Power’s next Integrated Resource Plan.
ded in Excel, to facilitate inspection by EfficiencyOne, regulatory intervenors and the Nova Scotia Utility and Review Board (UARB). 1.1 Energy Efficiency Potential Navigant’s Demand-Side Management Simulator (DSMSimTM) model, a transparen...
AI summary The document outlines the use of Navigant’s Demand-Side Management Simulator (DSMSimTM) model for energy efficiency potential analysis. The model will be customized for this study and used to estimate energy and peak demand savings under multiple scenarios, with stakeholder input. Five scenarios, including a maximum achievable one, will be considered due to budget and timeline constraints.
test ratio remains above 1.0 (or a designated threshold) for each measure • Can easily switch between net and gross savings and cost-effectiveness results • Provides cost-effectiveness metrics at the measure, program, sector, portfolio, en...
AI summary The text describes a tool that provides cost-effectiveness metrics at various levels of granularity, supports sensitivity analysis, and allows for data import and export, facilitating detailed analysis of energy efficiency measures and programs.
ulatory intervenors and the UARB, the model inputs and outputs will be provided in Excel. 2. Approach to Potentials 2.1 Approach to Energy Efficiency Potential The EE analysis will assess three potentials (1) technical, (2) economic, and (...
AI summary The EE analysis will assess three types of energy efficiency potentials: technical, economic, and achievable. The analysis will involve multiple steps, including defining customer segments, forecasting energy sales, and calculating potential savings. Stakeholder feedback will be considered, with draft scenarios communicated on 05/24/2019.
chnical, economic and achievable potential for 2021-2045 (25 years) 3 . Date Filed: August 14, 2019 Page 5 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 133 of 355 Nova Scotia E...
AI summary This section outlines the process for developing baseline energy use, end use saturation, and sales forecasts as part of a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045. It emphasizes the importance of market characterization as a foundational step in the study.
Load Forecast 1. Energy forecast Nova Scotia Power forecasts 2. Demand forecast Customer Accounts Forecast Nova Scotia Power forecasts Customer Demographics Nova Scotia customer surveys and other primary and secondary sources Measure-level...
AI summary Nova Scotia Power provides forecasts for energy and demand, customer accounts, and demographics, using surveys, program evaluations, and statistical data. Incentive level assumptions are based on past program experience and future plans. Navigant will develop energy sales forecasts for electric consumption and peak demand, disaggregated by sector and end use.
s) Institutional t) Industrial Region u) Based on first three characters of postal codes The first key output of the market characterization will be the development of the base year analysis. The base year will be calibrated against provin...
AI summary The document discusses the development of a base year analysis for Nova Scotia's electricity consumption, calibrated against Nova Scotia Power's sales data from 2017 or 2018. The goal is to create a detailed profile of electricity consumption across customer sectors and end uses.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 135 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A We will develop the base year analysis based on a bottom-up assessment of elect...
AI summary The text outlines a bottom-up approach to developing a base year analysis for electricity consumption, starting from end-use equipment types and efficiencies and aggregating data up to customer segments, sectors, and administrator levels. The approach ensures alignment with expectations at each level of aggregation.
regated electricity consumption (at each level) is reasonable and well aligned with expectations. Figure 4. Illustrative Breakdown of Energy Sales Forecast Based on Navigant’s experience conducting energy efficiency potential studies, we c...
AI summary The text discusses the importance of aggregating electricity consumption data by customer segments and end uses in energy efficiency potential studies. It highlights that proper segmentation and alignment with data availability are crucial to avoid unnecessary complexity and ensure valuable insights for program managers and DSM planners.
f this effort, Navigant will collaborate with E1 on the segmentation. 6 . Date Filed: August 14, 2019 Page 8 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 136 of 355 Nova Scotia...
AI summary The document outlines the process of base year calibration analysis, focusing on establishing specific end uses for each customer sector. It also discusses the development of a reference case forecast, which serves as a benchmark for calculating potential savings from energy efficiency and demand response initiatives.
the base year analysis as the foundation for developing the forecast. This is illustrated by Figure 5. Figure 5. Schematic of Reference Case Development 2.1.1.1 Stock Assumptions Navigant will first develop stock growth rates based on the...
AI summary The text discusses the development of the reference case forecast, beginning with the base year analysis and the establishment of stock growth rates for residential and non-residential buildings. It also mentions the importance of EUI trends in forecasting energy use.
trends to be applied to each customer segment. EUI trends are intended to reflect natural changes in electricity consumption as a result of two factors: (1) natural conservation and (2) natural growth. • Natural conservation is a well-esta...
AI summary The text discusses the concept of natural conservation and natural growth in electricity consumption, particularly within the context of DSM programs. It outlines how EUI trends reflect changes in consumption due to these factors and highlights the importance of defining natural conservation, including the impact of future building codes and appliance standards on conservation potential.
nd we are flexible as to which approach should be incorporated into the reference forecast and conservation potential estimates. The final step of the reference case forecast will be to apply the EUI trends and the stock growth rates to th...
AI summary The document discusses the development of a reference case forecast for electricity consumption by sector and customer segment, using EUI trends and stock growth rates. It also outlines the development of global assumptions for the potential study, including inflation rates, discount rates, and avoided energy costs.
ing the following measure parameters. Figure 8. Characterization Measure Parameters 2.1.3 Step 4: Develop Energy Efficiency Potential Estimates Once the reference case forecasts and measure characterizations are complete, Navigant will dev...
AI summary The document outlines the process for developing energy efficiency potential estimates over a 25-year period from 2021 to 2045, following the completion of reference case forecasts and measure characterizations. This is part of a broader study on energy efficiency and demand response potential in Nova Scotia.
segment consumption or per square foot; and, lastly, measures such as industrial ventilation heat recovery are well-suited for estimating energy savings as a percentage of end use consumption. The DSMSimTM model can appropriately handle sa...
AI summary The text discusses methods for estimating energy savings through energy efficiency (EE) measures, including the use of the DSMSimTM model. It outlines technical potential calculations based on measure replacement types and highlights the distinction between savings from new construction and retrofit measures. The study considers all possible EE measures without economic feasibility constraints.
wever, new construction technical potential is driven by equipment installations in new building stock rather than by equipment in existing building stock. 5 New building stock is added to keep up with forecasted growth in total building s...
AI summary The document explains how new construction technical potential is calculated based on new building stock, which includes growth and replacement of demolished buildings. Annual Incremental Technical Potential (AITP) is determined by multiplying new buildings, measure density, and savings per measure. The methodology accounts for variations in units and data availability.
X Technical Suitability (dimensionless) Total Technical Potential (TTP): TTP Y = TTP Y = ∑YEAR=2020 YEAR=2029 AITPYEAR
AI summary The text introduces the concept of Total Technical Potential (TTP) as a measure of technical suitability, calculated by summing Annual Incremental Technical Potential (AITP) across years from 2020 to 2029.
measures, annual potential is equal to total potential, thus offering an instantaneous view of technical potential. The equation used to calculate technical potential for retrofit measures is provided below. Annual/Total Savings Potential...
AI summary The text discusses the calculation of annual and total savings potential for retrofit measures, using factors like existing building stock, measure density, savings per unit, and technical suitability. It also introduces the concept of competition groups, where certain efficient technologies compete for the same installation or budget.
this study include: • Competing efficient technologies share the same baseline technology characteristics, including baseline technology densities, costs, and consumption • The total (baseline plus efficient) maximum densities of competing...
AI summary This section outlines the assumptions used in the study, including that competing efficient technologies share baseline characteristics and that only one measure per competition group is selected to avoid double-counting. It also describes the framework for developing and running the energy efficiency technical potential model, highlighting key data inputs and outputs.
ous dimensions of outputs produced from the potential model: type of potential (technical) reported at various levels (sector, end use, etc.) and in certain units (GWh, MW, etc.). 16 . Date Filed: August 14, 2019 Page 18 of 40 REDACTED (CO...
AI summary The document discusses the development of economic potential in the context of energy efficiency and demand response, following the technical potential model data flow outlined in Navigant's model. It highlights the various dimensions of outputs, including potential types, levels, and units, such as GWh and MW.
or 2021-2045 Appendix A Figure 10. Navigant’s Technical Potential Model Data Flow 2.1.3.2 Develop Economic Potential
AI summary This section outlines the development of economic potential as part of a technical potential model, referencing data flow and analysis processes relevant to energy efficiency and demand-side management planning.
Economic potential is a subset of technical potential and uses the same assumptions regarding immediate replacement as in technical potential, however, only includes those measures that have passed the cost-benefit (B/C) tests chosen for m...
AI summary The text discusses economic potential as a subset of technical potential, focusing on measures that pass cost-benefit tests, specifically the Total Resource Cost (TRC) test with a B/C ratio of 1.0 or higher. It explains how DSMSimTM calculates cost tests and includes administrative costs in economic potential calculations when aggregating measures.
This section presents the approach to calculating achievable potential, which is fundamentally more complex than the calculation of technical or economic potential. The potential study will estimate the annual and cumulative achievable pot...
AI summary This section outlines the approach to calculating achievable energy and peak demand savings potential through energy efficiency (EE), involving up to five scenarios with varying incentives. The process includes simulating market adoption of energy-efficient measures and determining equilibrium market share, considering stakeholder feedback and sector-level analysis.
incentive level, and any relevant market barriers. Develop and Run the Achievable Potential Model The overall achievable potential modelling framework is illustrated in Figure 16. We will draw on the results of the economic potential analy...
AI summary The document outlines the process for developing and running the Achievable Potential Model, which uses economic potential analysis results to estimate achievable energy efficiency and demand response potential. The model incorporates specified avoided costs and is illustrated in Figure 16.
Figure 16. Navigant’s Achievable Potential Model Data Flow 26 . Date Filed: August 14, 2019 Page 28 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 156 of 355 Nova Scotia Energy E...
AI summary The text discusses the use of sensitivity analysis in energy savings potential modeling, emphasizing its importance for policy decisions and program design. Navigant plans to analyze factors such as discount rates, with a maximum of five scenarios due to budget and timeline constraints. DSMSimTM can analyze twelve influential factors simultaneously, providing insights into variable relationships.
the relationships between key variables and potential. DSMSimTM can easily and quickly run different levels of sensitivity analyses to compare results with each case of results developed. Figure 17. Tornado Chart Showing Model Sensitivitie...
AI summary The text discusses the use of DSMSimTM for sensitivity analysis and outlines Navigant's approach to estimating demand response (DR) potential, including a data flow diagram for the DR study. The document is part of a larger report on energy efficiency and demand response potential in Nova Scotia.
and cost- effectiveness results for each of the scenarios. 28 . Date Filed: August 14, 2019 Page 30 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 158 of 355 Nova Scotia Energy E...
AI summary The document outlines the steps in a demand response (DR) potential assessment, beginning with market characterization. The segmentation approach is based on Nova Scotia Power’s rate schedules and was agreed upon through discussions between E1 and Navigant, differing from the energy efficiency assessment method.
tent with how these customers are treated in the EE analysis. 30 . Date Filed: August 14, 2019 Page 32 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 160 of 355 Nova Scotia Energ...
AI summary This section outlines the process of developing baseline projections for energy efficiency and demand response potential, focusing on selecting the base year based on the latest available customer count and load data from 2017 or 2018.
hich is the latest year for which full customer count and load data is available, 2018 or 2017. The selection of the base year will be consistent with that used in the EE potential. The baseline projection for DR potential assessment entai...
AI summary The document discusses the methodology for projecting winter peak demand by customer segment and end use, utilizing a bottom-up approach calibrated with Nova Scotia Power’s projections and historical load data. Data sources include retail sales, baseline projections, and load shapes.
electric vehicles for peak All Electric vehicles. • Auto-DR demand reduction. enabled Use of BTM batteries for Behind the Meter load shifting and/or (BTM) Battery All BTM batteries. curtailment during peak Storage demand periods. 15 This i...
AI summary The text discusses the potential of electric vehicles and behind-the-meter (BTM) batteries for demand response (DR) and load management, particularly during peak demand periods. It references a study on Nova Scotia's energy efficiency and demand response potential for 2021-2045.
members for vetting these assumptions. Along with program costs and annual budgets, we also calculate levelized costs for DR programs. We routinely use levelized costs and potential savings results to develop supply curves, in which saving...
AI summary The document discusses the development of demand response (DR) potential estimates through modeling efforts, including the use of DRSim™ to simulate DR technology roll-out, costs, and interactions. Levelized costs and potential savings are used to develop supply curves, and key model inputs and outputs are outlined in Figure 22.
t supply curves which stack up savings in ascending order of costs. 36 . Date Filed: August 14, 2019 Page 38 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 166 of 355 Nova Scotia...
AI summary The document outlines key input assumptions used in modelling the technical and economic potential for energy efficiency (EE) studies in Nova Scotia, including figures that list these assumptions for both technical and economic potential as well as achievable potential scenarios.
scenarios for the EE potential study. 37 . Date Filed: August 14, 2019 Page 39 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 167 of 355 Nova Scotia Energy Efficiency and Demand...
AI summary The document discusses energy efficiency and demand response potential in Nova Scotia, referencing a study conducted in collaboration with the Nova Scotia Residential Sector and BNI. It includes a baseline study submitted to Efficiency One, with information redacted for confidentiality.
.........................................................................................30 Figure 39. Residential Water Conservation Profile ....................................................................................30 Figure 40....
AI summary This document outlines the purpose and methodology of a baseline study for the Nova Scotia residential sector and BNI sector, focusing on energy efficiency and demand response potential. It includes various profiles and figures that contribute to the 2023 Load Forecast Report.
10 years old c. Ten years or older RECORD NUMBER _ 98 Don’t know 13. How many of the following do you have in your home… RECORD Don’t know NUMBER a) Full-size refrigerators (Note: these do not include 8 mini fridges or wine fridges)? b) M...
AI summary The text contains survey questions about household appliances and a reference to a 2019 residential energy survey, as well as a mention of a 2023 Load Forecast Report and an energy efficiency and demand response potential study for Nova Scotia.
b. Number of children under age of 18 [RECORD NUMBER] 37a. [POSE IF TOTAL IN Q36 A AND B=1] Was your household income before taxes last year below $21,822? 1 Yes 2 No 3 Prefer not to say 98 Don’t know/Not sure 37b. [POSE IF TOTAL IN Q36 A...
AI summary The text contains survey questions related to household income and First Nations community residency, part of a residential energy efficiency and demand response potential study conducted in 2019. The questions are structured to gather demographic and socioeconomic data for analysis.
ttempt will be made to identify you. We appreciate your feedback! As you go through the survey, if you do not have a particular equipment type, please enter ‘0’ in the quantity box. Section A: Background/Characteristics To begin… 1. Please...
AI summary This text is a section from a 2019 survey conducted as part of a load forecast report and energy efficiency study in Nova Scotia. It includes instructions for respondents regarding their business location and postal code.
the speed of any fans or pumps automatically 1 2 8 9 controlled by a variable frequency drive (VFD)? © Narrative Research, 2019 4 . Date Filed: August 14, 2019 Page 4 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast...
AI summary The text includes a redacted portion of a 2019 commercial survey asking respondents to indicate the number of items they have based on their previous 'yes' answers to question 10. It is part of a larger study on energy efficiency and demand response potential in Nova Scotia.
heaters that are installed in the frames and doors of refrigerated cases to reduce condensation and prevent fogging.) © Narrative Research, 2019 8 . Date Filed: August 14, 2019 Page 8 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023...
AI summary The text references a 2019 commercial survey and a 2023 load forecast report, which are part of an energy efficiency and demand response potential study for Nova Scotia spanning 2021-2045. It includes a mention of heaters installed in refrigerated cases to reduce condensation and fogging.
450 548 202 333 467 246 651 681 321 428 573 268 458 544 218 784 169 170 150 153 1 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 1 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30...
AI summary The document contains a redacted section of a 2023 Load Forecast Report and a 2019 Electricity Usage Survey related to residential energy efficiency and demand response potential in Nova Scotia. It includes data from a study covering the years 2021-2045.
450 548 202 333 467 246 651 681 321 428 573 268 458 544 218 784 169 170 150 153 2 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 2 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30...
AI summary The document contains a series of numbers and a reference to a 2019 Electricity Usage Surveys - Residential table from Navigant, which is part of a larger study on Nova Scotia Energy Efficiency and Demand Response Potential for 2021-2045. The text includes a load forecast report and a redacted section indicating confidential information.
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Responses of 'Don't know' were excluded from calculation of the mean. 3 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 3 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load...
AI summary The document text includes a table from a 2019 electricity usage survey focusing on residential energy consumption in Nova Scotia. It references a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study, but no specific details or arguments are discussed in the provided text.
345 388 132 233 369 167 502 552 182 326 424 218 374 360 191 543 130 145 127 120 4 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 4 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30...
AI summary The document contains a load forecast report and an energy efficiency and demand response potential study for Nova Scotia covering the period 2021-2045. It includes an attachment from Synapse and an appendix from Navigant, with information redacted due to confidentiality.
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Responses of 'Don't know' were excluded from calculation of the mean. 5 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 5 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045, prepared by Navigant. It references a filing date of August 14, 2019, and is part of a larger document spanning multiple pages.
659 203 398 496 251 418 444 202 660 165 166 148 147 This table excludes responses of 'Don't know'. 6 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 6 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast R...
AI summary This document contains a table with numerical data and a narrative research report titled '2023 Load Forecast Report' by Synapse, attached to a study on Nova Scotia Energy Efficiency and Demand Response Potential for 2021-2045. The document is redacted and includes a reference to Navigant.
646 244 392 517 252 424 466 210 680 163 160 147 141 This table excludes responses of 'Don't know'. 7 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 7 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast R...
AI summary The text includes a table with numerical data and references to a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It also mentions an attachment from Synapse and a study by Navigant, indicating a regulatory analysis context.
175 45 98 136 76 112 108 58 162 56 49 35 29 This table excludes responses of 'Don't know'. 8 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 8 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Sy...
AI summary The document includes a table with numerical data and a narrative research section from a load forecast report, referencing a study on energy efficiency and demand response potential in Nova Scotia for the years 2021-2045. It also includes a redacted section and mentions an attachment from a 2023 report.
478 137 295 348 196 293 322 143 472 106 124 117 108 This table excludes responses of 'Don't know'. 9 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 9 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast R...
AI summary The text contains a table with numerical data and a reference to a load forecast report and energy efficiency study conducted by Navigant. It also includes a redacted document and a page reference from a legal filing.
.2 .2 .3 .2 .2 .3 .2 .3 .2 .2 .2 .2 .3 .2 .3 .2 .4 .2 .3 .3 .1 .2 Responses of 'Don't know' were excluded from calculation of the mean. 10 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 10 of 54 REDACTED (CONFIDENTIAL IN...
AI summary The text includes a statistical summary, a reference to a load forecast report, and a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045. It also references an attachment and page number from a document filed on August 14, 2019.
.9 .9 .9 .8 .9 .8 1.0 .9 .8 .9 Responses of 'Don't know' were excluded from calculation of the mean. 11 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 11 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Foreca...
AI summary The document contains a portion of a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, submitted on August 14, 2019. It includes data from a study by Navigant, with some redacted confidential information.
568 144 314 439 226 340 372 165 547 139 147 122 130 This table excludes responses of 'Don't know'. 12 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 12 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast...
AI summary The document includes a table with numerical data and mentions a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045, both prepared by Navigant. It also references a redacted confidential document and a filing date of August 14, 2019.
1.0 1.1 1.0 .9 1.1 .9 1.2 1.1 .9 .8 Responses of 'Don't know' were excluded from calculation of the mean. 13 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 13 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load F...
AI summary The text includes a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period 2021-2045. It references a document from Navigant and mentions the date of filing as August 14, 2019.
3.5 3.5 3.0 3.4 3.8 2.9 3.7 3.8 2.9 3.5 3.5 3.8 3.5 3.4 3.6 3.4 4.1 3.8 3.5 3.7 Responses of 'Don't know' were excluded from calculation of the mean. 14 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 14 of 54 REDACTED (C...
AI summary The text includes a series of numerical values and a reference to a load forecast report and energy efficiency study. It mentions the exclusion of 'Don't know' responses from a mean calculation and references a confidential document from Synapse IR-30 and a study by Navigant.
83 438 150 269 404 209 535 580 243 368 467 222 409 414 200 623 135 145 133 135 15 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 15 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-3...
AI summary The document includes a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period 2021-2045. It is part of a regulatory proceeding and includes confidential information that has been redacted.
3.5 3.2 2.7 3.6 3.5 2.5 3.8 3.9 2.2 2.1 4.5 4.3 3.4 3.3 3.6 3.3 4.6 5.4 3.4 2.5 Responses of 'Don't know' were excluded from calculation of the mean. 16 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 16 of 54 REDACTED (C...
AI summary The text presents numerical data and references a load forecast report and an energy efficiency and demand response potential study, indicating a focus on energy forecasting and efficiency initiatives in Nova Scotia. The data appears to be part of a regulatory proceeding, likely related to energy planning and resource assessment.
.3 .3 .4 .4 .1 .3 .3 .3 .3 .2 .3 .3 .3 .2 .4 .2 .4 .3 .1 .1 Responses of 'Don't know' were excluded from calculation of the mean. 17 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 17 of 54 REDACTED (CONFIDENTIAL INFORMAT...
AI summary The text includes a load forecast report and an energy efficiency and demand response potential study for Nova Scotia from 2021 to 2045, with a mention of a redacted confidential document and a page reference from a study by Navigant.
.5 .7 .6 .5 .6 .5 .6 .6 .8 .7 Responses of 'Don't know' were excluded from calculation of the mean. 18 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 18 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecas...
AI summary The document contains a portion of a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, dated August 14, 2019. It includes statistical data and a reference to a redacted attachment from Synapse IR-30.
.3 .3 .3 .2 .3 .2 .2 .4 .4 .3 Responses of 'Don't know' were excluded from calculation of the mean. 19 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 19 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecas...
AI summary The text includes statistical data and references to a load forecast report and an energy efficiency and demand response potential study, with a mention of a redacted confidential document and a page reference from a 2023 report.
20.0 17.8 14.0 17.5 15.2 22.6 21.2 17.4 15.1 Responses of 'Don't know' or above 85 were excluded from calculation of the mean. 21 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 21 of 54 REDACTED (CONFIDENTIAL INFORMATION...
AI summary This document contains a table of numerical data and a narrative research section from a 2019 filing related to a load forecast report and energy efficiency study for Nova Scotia, with confidential information redacted. The table appears to present statistical values, and the text references a study conducted by Navigant.
3.4 3.6 2.7 3.2 3.1 4.4 4.0 3.7 3.6 Responses of 'Don't know' or above 85 were excluded from calculation of the mean. 22 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 22 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED)...
AI summary The document includes a table with numerical data and a narrative research section from a load forecast report. It references a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045, and mentions a report by NAVIGANT.
.6 .5 .5 .8 .4 .6 1.0 .3 .3 Responses of 'Don't know' or above 85 were excluded from calculation of the mean. 23 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 23 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Lo...
AI summary This document includes a table of numerical values and mentions the exclusion of 'Don't know' responses and those above 85 from mean calculation. It also references a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study, with a mention of NAVIGANT as a contributor.
358 398 142 253 363 190 503 520 238 338 433 206 372 386 177 581 119 134 126 119 24 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 24 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-...
AI summary This document is part of a regulatory proceeding related to energy efficiency and demand response potential in Nova Scotia for the period 2021-2045. It includes a load forecast report and an appendix from a study conducted by Navigant. The text contains redacted confidential information and is part of a filing dated August 14, 2019.
.1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .0 Responses of 'Don't know' or above 5 were excluded from calculation of the mean. 25 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 25 of 54 REDACTED (CONFIDENTI...
AI summary The document is a part of a 2023 Load Forecast Report and includes an attachment from a Nova Scotia Energy Efficiency and Demand Response Potential Study covering 2021-2045. It references a study conducted by NAVIGANT and was filed on August 14, 2019.
3.6 6.1 5.5 2.3 8.8 1.9 4.8 4.8 4.2 3.7 This question was randomly posed to approximately one in four respondents. 27 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 27 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a table of numerical data and references a survey where one in four respondents were randomly asked a question. It also references a load forecast report and a study on energy efficiency and demand response potential in Nova Scotia, conducted by Navigant.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 31 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 31 of 54 REDACTED (...
AI summary The text includes a table with numerical data and references to a load forecast report and an energy efficiency and demand response potential study. It also mentions a document filed on August 14, 2019, and includes a redacted section with confidential information removed.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 32 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 32 of 54 REDACTED (...
AI summary This document includes a table with numerical data and a narrative research section from a 2019 filing. It references a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045, with a redacted section indicating confidential information has been removed.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 33 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 33 of 54 REDACTED (...
AI summary This document contains a table with numerical data and references to a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It also includes a redacted page from a legal or regulatory proceeding, filed on August 14, 2019.
7.4 8.3 9.2 4.1 8.7 7.2 7.3 6.7 8.4 7.8 This question was randomly posed to approximately one in nine respondents. 35 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 35 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a set of numbers, a statement about a question posed to respondents, and references to a load forecast report and energy efficiency study. It also references a redacted document and a report by Navigant.
3.9 3.6 8.7 1.8 5.8 3.0 4.4 2.2 3.3 3.5 This question was randomly posed to approximately one in nine respondents. 36 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 36 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a statistical reference and a narrative research report titled '2023 Load Forecast Report Synapse IR-30 Attachment 1' and 'Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B-3', with a mention of the entity 'NAVIGANT'.
7.1 6.8 9.2 3.4 8.3 6.6 7.2 7.8 7.0 8.4 This question was randomly posed to approximately one in nine respondents. 37 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 37 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a set of numerical values and a reference to a survey question posed to approximately one in nine respondents. It also references a load forecast report and an energy efficiency and demand response potential study, with a mention of the firm NAVIGANT.
5.5 5.5 8.6 3.4 7.6 4.6 5.1 5.1 5.8 5.7 This question was randomly posed to approximately one in nine respondents. 38 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 38 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a set of numerical data and mentions a survey conducted with a sample size of approximately one in nine respondents. It also references a load forecast report and an energy efficiency and demand response potential study, with a mention of NAVIGANT as the entity involved.
6.8 7.8 8.9 3.3 8.6 6.3 6.3 8.0 7.3 6.8 This question was randomly posed to approximately one in nine respondents. 40 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 40 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a series of numerical values, a statement about a survey question posed to one in nine respondents, and a reference to a load forecast report and energy efficiency study. The report is from Naviant and was filed on August 14, 2019.
5.7 6.3 8.6 2.6 8.0 4.7 5.4 5.6 5.7 5.6 This question was randomly posed to approximately one in nine respondents. 41 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 41 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a table of numerical data and a narrative research section from a load forecast report, mentioning a study on energy efficiency and demand response potential in Nova Scotia for 2021-2045. The document was filed on August 14, 2019, and references a study conducted by NAVIGANT.
6.9 7.4 8.9 3.6 8.5 6.5 7.1 7.6 7.1 8.1 This question was randomly posed to approximately one in nine respondents. 42 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 42 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a set of numbers and a statement about a survey question posed to one in nine respondents. It also references a redacted document titled '2023 Load Forecast Report' and mentions a study on energy efficiency and demand response potential in Nova Scotia for 2021-2045, conducted by Naviant.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 43 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 43 of 54 REDACT...
AI summary The text includes a table of numerical data and a reference to a load forecast report and energy efficiency study. It also mentions a redacted confidential document and a page number from an attachment. The context suggests a regulatory proceeding involving energy forecasting and efficiency analysis.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 44 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 44 of 54 REDACT...
AI summary The document includes a table with numerical data, a narrative research section, and a redacted portion of the 2023 Load Forecast Report and the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It also references an attachment and appendix from the report.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 45 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 45 of 54 REDACT...
AI summary The text includes a table with numerical data and mentions a '2023 Load Forecast Report' and a 'Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045' from Appendix B-3. It also includes a redacted section and a reference to a Synapse IR-30 attachment.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 46 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 46 of 54 REDACT...
AI summary The text includes a table of numbers and a reference to a load forecast report and an energy efficiency and demand response potential study, both related to Nova Scotia. The document is redacted and contains information filed on August 14, 2019.
2.1 1.9 2.1 2.1 1.9 1.9 2.0 2.0 1.9 2.1 2.0 2.1 2.0 2.0 2.0 2.0 2.0 2.1 2.0 1.9 47 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 47 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-...
AI summary The text includes a table of numerical data and references to a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045, with a mention of an appendix from Navigant. The content appears to be part of a regulatory proceeding document.
86 140 41 62 123 71 133 110 116 92 120 31 97 129 51 175 25 20 18 41 48 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 48 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachmen...
AI summary The text includes a table from a 2019 residential electricity usage survey conducted by Navigant, part of a larger study on energy efficiency and demand response potential in Nova Scotia for 2021-2045. The table is part of the 2023 Load Forecast Report and is referenced as Attachment 1, Page 289 of 355.
20 31 15 34 3 17 33 32 20 27 34 12 28 24 14 38 14 6 5 5 49 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 49 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 290...
AI summary This document contains a table from a 2019 electricity usage survey focusing on residential consumers in Nova Scotia. It is part of a larger study on energy efficiency and demand response potential for the years 2021-2045, and is associated with a load forecast report from 2023.
258 454 548 220 782 167 169 142 156 UNWEIGHTED SAMPLE SIZE (#) 1002 464 135 403 450 548 202 333 467 246 651 681 321 428 573 268 458 544 218 784 169 170 150 153 50 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 50 of 54 R...
AI summary The text includes statistical data and a reference to a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045. It also mentions a redacted confidential document and a page reference from a Synapse IR-30 attachment.
9 528 189 329 452 235 640 679 291 428 573 268 452 518 215 755 167 170 150 153 51 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 51 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30...
AI summary The document is a redacted portion of a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for Nova Scotia, covering the period 2021-2045. It includes information from Synapse IR-30 and an attachment from Navigant.
4 300 157 203 217 148 388 384 193 126 572 155 263 314 133 445 122 116 58 67 52 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 52 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 A...
AI summary The document includes a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period from 2021 to 2045. It is part of a regulatory proceeding and includes a redacted attachment from Navigant.
.6 43.4 37.3 38.3 46.6 55.1 38.5 42.5 . 53.7 36.0 34.9 44.1 40.9 46.1 41.3 12.2 28.9 43.8 88.3 53 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 53 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Rep...
AI summary The text includes a load forecast report and a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045, with a reference to a confidential document from Synapse IR-30 Attachment 1, Page 294 of 355.
672 330 423 572 258 454 548 220 782 167 169 142 156 UNWEIGHTED SAMPLE SIZE (#) 1002 464 135 403 450 548 202 333 467 246 651 681 321 428 573 268 458 544 218 784 169 170 150 153 54 Narrative Research . NAV002-1000 Date Filed: August 14, 2019...
AI summary The document presents a load forecast report and energy efficiency and demand response potential study for Nova Scotia, covering the period from 2021 to 2045. It includes data on sample sizes and references a 2023 Load Forecast Report by Synapse and an appendix from Navigant.
188 116 22 50 110 78 53 49 43 45 62 37 59 109 44 1 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 1 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 296 of 355 N...
AI summary The text presents a table from a 2019 electricity usage survey for businesses in Nova Scotia, asking about the number of power bars at their location. The table is part of a larger study on energy efficiency and demand response potential for the years 2021-2045.
12.4 5.8 8.0 29.3 3.9 13.3 33.1 11.7 14.7 11.7 Responses of greater than 225 were excluded from calculation of the mean. 2 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 2 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED...
AI summary The text includes statistical data, a narrative research section, and a redacted table from a load forecast report. It references a survey on smart power bars and mentions a study on energy efficiency and demand response potential in Nova Scotia.
158 99 18 41 93 60 43 45 36 40 57 29 49 90 41 3 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 3 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 298 of 355 Nova...
AI summary The document presents a table from a 2019 electricity usage survey for businesses in Nova Scotia, asking about the number of computer servers onsite. The table is part of a study on energy efficiency and demand response potential for 2021-2045.
122 70 18 34 71 48 32 41 28 28 46 29 41 75 32 5 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 5 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 300 of 355 Nova...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and an appendix from a study on Nova Scotia Energy Efficiency and Demand Response Potential for 2021-2045. It references a table from 2019 Electricity Usage Surveys focusing on the number of hot water heaters in business locations in Nova Scotia.
174 104 19 51 101 69 50 50 38 44 59 35 59 101 45 7 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 7 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 302 of 355 N...
AI summary The text provides a table from a 2019 electricity usage survey for businesses in Nova Scotia, focusing on building characteristics. It is part of a larger study on energy efficiency and demand response potential for the period 2021-2045.
175 105 21 49 101 70 51 49 38 44 59 34 60 100 44 8 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 8 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 303 of 355 N...
AI summary The text presents a table from a 2019 electricity usage survey for businesses in Nova Scotia, focusing on the use of variable frequency drives (VFDs) in heating, cooling, and ventilation systems. It is part of a larger study on energy efficiency and demand response potential.
17 35 79 42 36 33 32 30 45 31 39 79 34 MEAN .3 .2 .2 .6 .1 .4 .1 .5 .4 .1 .1 .6 .4 .3 .3 9 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 9 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Syna...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, which asks how many of each type of equipment or system the business has, with a condition based on a response to question Q10C. The table is part of a larger study on energy efficiency and demand response potential in Nova Scotia.
2019 Electricity Usage Surveys - Business TABLE 11B: [IF Q10C 'Yes', OR CODED AS ZERO IF Q10C 'No'] How many of each does your business have? Door heater controls such as anti-sweat heaters (ASH)? These are electric resistance heaters that...
AI summary The text refers to a 2019 electricity usage survey for businesses, specifically asking about the number of door heater controls (anti-sweat heaters) in refrigerated cases, which are commonly found in grocery and convenience stores.
131 77 16 38 81 46 39 41 30 31 48 30 43 83 36 MEAN .4 .3 1.2 .3 .5 .3 .4 .4 .5 .1 .4 .6 .6 .5 1.1 Responses of greater than 6 were excluded from calculation of the mean. 10 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page...
AI summary The text presents statistical data and references a 2019 Electricity Usage Survey for businesses in Nova Scotia, including a table from a load forecast report and a study on energy efficiency and demand response potential. It also includes a redacted section and a reference to a document filed on August 14, 2019.
37 77 43 35 36 26 31 48 26 39 79 36 MEAN .4 .5 .6 .1 .4 .3 .3 .3 .8 .1 .4 .7 .4 .4 .6 Responses of greater than 6 were excluded from calculation of the mean. 11 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 11 of 51 RED...
AI summary The text includes statistical data and a reference to a 2019 Electricity Usage Surveys - Business table from a load forecast report, as well as a study on Nova Scotia Energy Efficiency and Demand Response Potential for 2021-2045. The data appears to be related to energy usage research.
66 11 27 60 43 27 30 31 24 40 29 30 71 30 MEAN 1.1 1.2 1.8 .5 1.2 1.0 .8 .6 2.0 .3 .9 1.9 1.6 1.1 1.4 12 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 12 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forec...
AI summary The document includes a table and narrative from a 2019 electricity usage survey for businesses in Nova Scotia, discussing load forecasting and energy efficiency studies. The table presents data on responses as a proportion of specific items, with some statistics provided, but much of the content is redacted.
46 37 20 29 23 17 33 21 27 51 24 Responses of greater than 1,000 bulbs were excluded from this table. 13 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 13 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forec...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, showing responses as a proportion of specific items, with some data redacted. It references a load forecast report and an energy efficiency and demand response potential study for Nova Scotia from 2021-2045.
21 SAMPLE SIZE (#) 174 104 18 52 98 69 51 53 38 40 60 36 57 97 43 MEAN .8 1.2 .5 .3 1.0 .7 .4 .4 2.5 .4 .6 2.3 1.1 1.1 1.4 Responses of greater than 50 were excluded from calculation of the mean. 17 Narrative Research . NAV002-1000 Date Fi...
AI summary The text presents statistical data related to a study on energy efficiency and demand response potential in Nova Scotia for the period 2021-2045. It includes sample sizes and mean values, with responses over 50 excluded from the mean calculation. The study is part of a load forecast report and is associated with Synapse IR-30 Attachment 1.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 25 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 25 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 320 of 355 No...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, part of a study on energy efficiency and demand response potential in Nova Scotia for 2021-2045. It is part of a load forecast report and is labeled as confidential.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 26 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 26 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 321 of 355 No...
AI summary The text includes a table reference from a 2019 electricity usage survey for businesses, part of a load forecast report and energy efficiency study for Nova Scotia. The table is labeled 'TABLE 23E' and appears in Appendix B-4 of the document.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 27 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 27 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 322 of 355 No...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, part of a load forecast report and energy efficiency study. It references a 2023 Load Forecast Report and an appendix from a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 28 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 28 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 323 of 355 No...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, part of a load forecast report and energy efficiency study for Nova Scotia. The data is redacted and appears in a legal or regulatory proceeding context.
14 25 18 8 12 18 2 18 125 2 17 70 10 13 10 42 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 42 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 337 of 355 Nova...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and an appendix from a 2021-2045 Nova Scotia Energy Efficiency and Demand Response Potential Study. It references a 2019 Electricity Usage Survey for businesses, focusing on square footage of company properties.
12 16 5 12 23 0 0 50 12 13 13 square feet More than 25,000 square feet 14 17 12 5 15 9 5 7 33 0 0 50 12 14 8 SAMPLE SIZE (#) 148 92 17 39 86 58 44 43 39 45 63 40 49 93 40 43 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page...
AI summary The text includes a table with numerical data and a reference to a load forecast report and an energy efficiency and demand response potential study conducted by Navigant. The report was filed on August 14, 2019, and is part of a larger document related to Nova Scotia energy planning.
8 8 0 8 6 2 4 11 Wood/pellets/chips 2 2 0 4 4 0 6 2 0 2 3 0 3 1 2 SAMPLE SIZE (#) 165 98 19 48 96 62 50 48 39 41 59 34 63 111 45 47 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 47 of 51 REDACTED (CONFIDENTIAL INFORMATI...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and references a 2019 Electricity Usage Surveys - Business table, indicating data collection and analysis related to energy usage and forecasting.
149 88 17 44 90 55 46 46 34 38 55 34 57 103 44 48 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 48 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 343 of 355 N...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and references a 2019 Electricity Usage Survey focused on business heating systems in Nova Scotia. It mentions a study on energy efficiency and demand response potential for 2021–2045.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 349 of 355 This page is intentionally left blank . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30...
AI summary The document contains redacted pages from the 2023 Load Forecast Report, including appendices related to energy efficiency model inputs, outputs, and loadshape disaggregation. These pages are part of a regulatory proceeding and were filed electronically.
76 2019 111 163 52 2020 96 152 56 2021 94 158 64 2022 155 146 -9 Date Filed: June 20, 2023 NSPI (Synapse) IR-34 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Ene...
AI summary NSPI has been requested by Synapse Energy Economics to provide detailed data, models, and calculations related to the 2023 Load Forecast Report, including residential model parameters, end-use intensity data, and factors influencing heat and cooling usage.
Other Indices (kWh / HH) Year ResIndices.EWHeat ResIndices.ECook ResIndices.Ref1 ResIndices.Ref2 ResIndices.Frz ResIndices.Dish ResIndices.CWash ResIndices.EDry ResIndices.TV ResIndices.Light ResIndices.PV ResIndices.Misc AContrib2Sales.Ot...
AI summary The text presents a table showing various energy usage indices across different years, including residential heating, cooking, refrigeration, and other household activities. It provides detailed data on kilowatt-hours per household for each year from 2013 to 2020.
0.00 0.00 307.60 9,985.61 2028 0.00 0.00 307.60 10,089.95 2029 0.00 0.00 307.60 10,105.96 2030 0.00 0.00 307.60 10,154.71 2031 0.00 0.00 307.60 10,204.20 2032 0.00 0.00 307.60 10,294.33 2033 0.00 0.00 307.60 10,301.11 REDACTED (CONFIDENTIA...
AI summary The text presents a table with financial data and load forecast report information, including years, indices for heating, cooling, and others, as well as various factors like energy efficiency savings and contributions to sales. The document also includes a reference to the 2023 Load Forecast Report, Synapse IR-35, Attachment 1, Page 5 of 8.
Other Indices (kWh / HH) Year SmlGenIndices.Vent SmlGenIndices.EWHeat SmlGenIndices.Cooking SmlGenIndices.Refrig SmlGenIndices.Light SmlGenIndices.Office SmlGenIndices.Misc 2013 14,231.95 1,796.55 2,112.53 24,703.61 69,483.97 13,439.59 37,...
AI summary The text presents a table of energy usage indices across various categories from 2013 to 2023, showing a general decline in kilowatt-hours per household (HH) for most categories over time.
42.5 412.1 (22.9) 389.2 2033 12,854.9 29,096.4 374.0 (16.5) 68.1 51.6 425.6 (24.7) 400.9 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 11 of 11 Small Gen load post regression Average...
AI summary The text presents load forecast data and regression analysis for Small Gen load in 2023 and 2033, including average use, solar generation, DSM captured by end uses, and changes in various metrics such as heating and cooling inputs.
60,706 1.26 0.855 0.070 4,578 2033 71,670 1.32 0.855 0.070 5,662 Change to 18.9% 4.8% 0.0% 0.0% 23.7% Xcool Inputs Cooling CoolUse Var Coefficient Scaling FactTotal Xcool 2023 36,324 1.34 0.302 0.035 514 2033 35,035 1.64 0.302 0.035 607 Ch...
AI summary The text presents numerical data related to load forecasting, including projections for 2023 and 2033, with percentages of change. It includes sections such as 'Xcool Inputs' and 'Xother Inputs' detailing various energy usage factors. The document is part of the 2023 Load Forecast Report (NSUARB M11108), and includes responses from NSPI to Synapse Energy Economics information requests.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-37: 2 3 Appendix B: General Service Model (pp 15-20) 4 5 (a) Please provide in electronic spreadsheet f...
AI summary NSPI responded to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. The response includes references to attachments and forecasting software used by NS Power for calculations.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Attachment 7 - General Model, filed electronically. The components of the calculations 2 are provided in Attachmen...
AI summary The 2023 Load Forecast Report discusses factors influencing load growth, including economic growth, non-manufacturing GDP, and annual cooling degree days (CDD). The report references Attachment 1 for detailed calculations and sources of data.
Heating Indices (kWh / HH) Cooling Indices (kWh / HH) Year GenIndices.Heating AContrib2Sales.GenHeatUse GenIndices.Cooling AContrib2Sales.GenCoolUse 2013 604,692.95 1.16 329,531.46 1.14 2014 595,455.52 1.14 327,385.43 1.00 2015 581,601.60...
AI summary The text presents data on heating and cooling indices (kWh per heating and cooling degree day) from 2013 to 2031, showing trends in energy use for heating and cooling over time.
1.61 2030 585,272.00 1.38 303,964.26 1.65 2031 600,353.90 1.39 303,105.77 1.69 2032 615,208.12 1.41 302,363.06 1.72 2033 635,079.81 1.42 301,719.53 1.76 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-37 At...
AI summary The text presents a table with numerical data for years 2030 to 2033, including values such as 585,272.00 and 303,964.26, along with corresponding percentages. The document is part of a 2023 Load Forecast Report by Synapse, and a section is redacted due to confidentiality.
Other Indices (kWh / HH) Year GenIndices.Vent GenIndices.EWHea GenIndices.Cooking GenIndices.RefrigGenIndices.Ligh GenIndices.OfficeGenIndices.Misc GenIndices.PV AContrib2Sales.GenOtherUse 2013 131,514.27 18,158.85 19,521.39 228,280.44 678...
AI summary The text presents a table with energy consumption indices across various categories from 2013 to 2023, showing a general decline in most indices over time, with some fluctuations in specific years.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-37 Attachment 1 Page 4 of 9 Regression Sales Results Out of Monthly Model (kWh / HH) Year AContrib2Sales.GenMetrixSales 2013 2,467,465.60 2014 2,414,804.67 20...
AI summary The document presents a load forecast report with historical and projected electricity sales data from 2013 to 2033, including regression sales results and various indices related to heating, cooling, and other factors. It includes data for specific months and years, indicating trends in energy consumption.
1 2032 2,464,948.2 2,464.9 (64.1) 226.4 (42.3) 120.0 2,585.0 (161.0) 2,424.0 2033 2,481,686.8 2,481.7 (64.1) 272.4 (49.6) 158.7 2,640.4 (173.6) 2,466.8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-37 Att...
AI summary The text presents load forecast data and regression analysis for 2023 and 2033, including demand load post-regression, generation demand, and intensities. It includes figures related to EV, solar, and DSM, along with changes in percentages between the two years.
Coefficient Total Xheat 2023 548,425 1.30 0.748 533,784 2033 635,080 1.42 0.748 672,713 Change to 17.2% 8.8% 0.0% 26.0% Xcool Inputs Cooling CoolUse Va Coefficient Scaling FactTotal Xcool 2023 312,580 1.38 0.714 0.370 114,190 2033 301,720...
AI summary The document presents load forecast data for heating, cooling, and other energy uses in 2023 and 2033, including percentage changes in coefficients and total values. It is part of the 2023 Load Forecast Report (NSUARB M11108) and includes NSPI's responses to Synapse Energy Economics information requests.
NTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 5 of 19
AI summary The text references the 2023 Load Forecast Report by Synapse, specifically Attachment 1, Page 5 of 19. It is part of a regulatory proceeding, though no specific details about the content or discussion are provided in the excerpt.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 15 of 19
AI summary The text is a page from a 2023 Load Forecast Report by Synapse Energy Economics, with confidential information redacted. It is part of Attachment 1 and appears on page 15 of 19.
Year Month ResEndUse.ResOther NResEndUse.SmlGenOther NResEndUse.GenOther Sales.SmlInd Sales.MedInd Sales.Unm mVars.OthrUse mVars.Days mVars.Other_AvgMW 2016 8 210,389.6 14,951.9 150,753.3 20,128.4 38,165.2 6,960.4 441,348.8 31.0 593.2 2016...
AI summary The text presents a table of monthly energy usage and sales data from 2016 to 2017, including metrics such as residential and non-residential end-use energy, sales by industry size, and other variables like average megawatts and days. It provides quantitative insights into energy consumption patterns over time.
144,296.5 20,205.2 40,063.6 6,691.4 437,107.4 31.0 587.5 2023 1 248,932.5 16,292.2 146,008.2 26,090.8 41,014.8 6,670.3 485,008.8 31.0 651.9 2023 2 191,449.1 14,343.2 133,461.6 22,627.0 40,257.9 6,514.8 408,653.6 28.0 608.1 2023 3 211,481.1...
AI summary The text presents numerical data across multiple columns and rows, likely representing financial or operational metrics over time. It includes a reference to the 2023 Load Forecast Report by Synapse Energy Economics, with a note that some information has been redacted as confidential.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 17 of 19
AI summary The text references a confidential portion of the 2023 Load Forecast Report by Synapse Energy Economics, specifically Attachment 1, Page 17 of 19. No further details are provided due to the removal of confidential information.
146,225.0 23,501.1 40,553.1 6,703.1 435,887.7 31.0 585.9 2026 8 228,107.7 15,948.5 146,383.0 21,292.5 41,109.8 6,707.6 459,549.1 31.0 617.7 2026 9 233,630.1 17,016.5 141,778.7 21,657.8 40,573.4 6,709.1 461,365.6 30.0 640.8 2026 10 226,819....
AI summary The text presents a series of numerical data points, likely related to financial or operational metrics, spanning multiple years and months. The data includes values such as costs, revenues, and other quantitative figures. The document is part of a load forecast report, with a reference to 'Synapse IR-41 Attachment 1 Page 18 of 19', and contains redacted confidential information.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 18 of 19
AI summary The text is a page from a 2023 Load Forecast Report by Synapse Energy Economics, with confidential information removed. It is part of an attachment in a regulatory proceeding document.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 19 of 19
AI summary The text is a page from the 2023 Load Forecast Report by Synapse Energy Economics, specifically Attachment 1, Page 19 of 19. It contains confidential information that has been removed.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests CONFIDENTIAL (Attachment Only) 1 Request IR-43: 2 3 Appendix D: Forecast Sensitivity Analysis 4 5 (a) Please provide the model output...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report, including model outputs, Oracle input data, historical data, and source data for figures. Attachments were provided for each request, with some information marked as confidential.
E3 generates forecast of Trip data Charger & EV Demographics Driver Charging attributes Costs & Tariffs transportation load shape based on simulations of EV driving and charging behavior, using travel 1. EV Driving & Charging Simulation...
AI summary E3 models transportation load shape by simulating EV driving and charging behavior using travel survey data, considering factors such as vehicle type, charging access, and cost. The model generates normalized load shapes by vehicle type and charging location, and estimates charging session statistics and costs.
2023 Load Forecast Report Synapse IR-44 Attachment 1 Page 8 of 8 Heating Equipment Stock Rollover E3’s electrification study scenarios ultimately yield near-complete electrification of residential and commercial buildings by 2050; to ach...
AI summary The document discusses E3’s electrification study scenarios, which predict near-complete electrification of residential and commercial buildings by 2050, primarily through heat pump adoption by the 2030s. The analysis highlights the role of stock rollover in influencing the pace of heat pump adoption and aligns growth in heat pumps with NSP forecasts.
Please refer to Section 10 at page 77. Date Filed: June 20, 2023 NSPI (Synapse) IR-45 Page 3 of 4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information...
AI summary The document discusses recommendations related to the 2023 Load Forecast Report, including hybrid scenarios for heating and strategies to monitor and mitigate commercial heating load growth. It references Section 10 on page 79 of the document.
1 • Given the continued population growth in Nova Scotia and 2 ongoing housing shortage in the province, re-evaluate the use of 3 housing completions for the near-term; 4 5 • Given the current inflationary environment, evaluate the use of...
AI summary The text outlines directives related to housing completions, income metrics, and household characteristics for energy demand analysis. It requests clarification on how these directives are incorporated into a report and references a response and table for further details.