Electric / Gas / Water VELCO Long-Term Demand Forecast Methodology Overview Eric Fox Oleg Moskatov Itron, Inc. April 17, 2008 Overview Methodology SAE energy model Hourly Load and Peak Demand Model Assumptions Weather data Normal weather Economic drivers End-use saturation and efficiency trends Price Preliminary Results Recent peak and energy trends Hourly load build-up results Peak and energy forecast Knowledge to Shape Your Future Page 2 Forecasting Approaches Three general approaches are used for forecasting longterm peak demand:
Load factor model Load factor = Average Demand / Peak Demand Peak Forecast = Energy Forecast / Hours * Load Factor Generalized econometric model Peak Forecast = (peak-day weather, customers, economic activity) Build-up approach Combine class energy forecasts with hourly profiles Aggregate to system load Find system peak Load factors and econometric models are adequate for short-term forecasting, but cant capture the impact of changing load diversity on long-term peak demand. Knowledge to Shape Your Future Page 3 Build-up Forecast Approach Develop long-term energy forecasts by customer class Residential, Commercial, Industrial, and Other Combine class energy forecasts with class hourly load profile models Evaluate using end-use hourly load and energy estimates Aggregate class profiles to generate a long-term system forecast and extract the monthly system peak demand Calibrate to weather-normalized 2008 demand estimates Knowledge to Shape Your Future Page 4 System Peak and Energy Winter Peak MW
0.696 2002 - 07 1.0% 0.6% 0.8% -0.1% Knowledge to Shape Your Future Page 5 Summer Peak 2002 2003 2004 2005 2006 2007 2008 Knowledge to Shape Your Future Date Peak 3-Jul 26-Jun 9-Jun 19-Jul 2-Aug 3-Aug Aug (MW) AvgTemp 1,023
83.5 1,001 81.0 985 76.5 1,074 81.5 1,118 84.0 1,073 84.5 1,089 82.3 CDD65 18.5 16.0 11.5 16.5 19.0 19.5 17.3 Page 6 System Peak Demand Analysis Daily Peak Demand (MW) 2002 to 2007 Significantlyless lesstemperaturetemperatureSignificantly sensitiveload loadthan thancompared comparedwith with sensitive otherregions regions other
Knowledge to Shape Your Future Page 7 Winter and Summer Monthly Peaks (MW) Butnot notsurprisingly, surprisingly,peaks peaks But aredriven drivenby byheating heatingand and are coolingdemand demand cooling Knowledge to Shape Your Future Page 8 System Peak Demand (Weekdays vs. Weekends) Summerpeak peakdemand demandalways always Summer fallsduring duringthe theweek weekcapturing capturing falls bothcommercial
commercialand andresidential residential both coolingloads loads cooling Winterpeaks peaksalso alsotend tendtoto Winter fallduring duringthe theweek-days, week-days, fall butwinter winterweek-end week-endpeaks peakscan can but benearly nearlyas ashigh highon oncold colddays days be Knowledge to Shape Your Future Page 9 Monthly peak demand (MW) Summerpeak:
peak:10 10MW MWper peryear year Summer Winterpeak: peak:66MW MWper peryear year Winter Since2002, 2002,peak peakdemand demandhas hasbeen been Since increasingroughly roughly1.0% 1.0%per peryear year increasing Knowledge to Shape Your Future Page 10 System Monthly Load Factor LoadFactor Factor Load MovingAverage Average Moving
Trend Trend Theload loadfactor factorappears appearstotobe be The trendingdown downslightly slightly trending implyingpeak peakdemand demandisisgrowing growing implying slightlyfaster fasterthan thanenergy energy slightly Knowledge to Shape Your Future Page 11 Peak-Day System Hourly Load Profile (MW) System System Commercial Commercial Residential Residential
Industrial Industrial Smalldifferences differencesinincustomer customerclass class Small loadgrowth growthcan canhave haveaa load significantimpact impacton onthe thepeak peak significant andits itstiming timing and Knowledge to Shape Your Future Page 12 Peak-Day Residential Load Profile (MW) Residential Residential BaseUse Use Base Cooling Cooling Changesininend-use
end-usesales sales Changes growthininturn turnimpact impact growth customerclass classhourly hourlyload load customer Knowledge to Shape Your Future Page 13 Build-Up Model Combine energy forecast and hourly class profiles using MetrixLT Need class and end-use energy and profile forecasts Monthly/Annual Monthly/Annual Energy Forecast Forecast Energy Class or or Class End Use Use Profiles Profiles End Hourly Hourly System Forecast Forecast System Knowledge to Shape Your Future
Long-Run Load Shape Forecasting System Hourly And Peak Forecast Page 14 Long-Term Energy Forecasting Model that can account for economic changes as well as long term structural changes Economic impacts income, household size, household growth Price impacts Structural changes saturation, efficiency, floor space, and thermal integrity trends Weather impacts Appropriate interaction of these variables Approaches End-Use Modeling Framework REEPS and COMMEND Statistically Adjusted End-Use Model Econometric model specification Knowledge to Shape Your Future Page 15 SAE Modeling Approach Blend end-use concepts into an econometric modeling framework: Average Use = Heating + Cooling + Other Use Define components in terms of its end use structure:
Cooling = f (Saturation, Efficiency, Utilization) Utilization = g (Weather, Price, Income, Household Size) Leverage off of EIA census region end-use forecasts Adjust for known differences in service area saturations Knowledge to Shape Your Future Page 16 Residential & Commercial SAE Model Regions Knowledge to Shape Your Future Page 17 Statistically Adjusted End-use Modeling (cont.) Estimate model using Ordinary Least Squares: AvgUse t b0 b1 XHeatt b2 XCoolt b3 XOthert t Knowledge to Shape Your Future Page 18 Residential Cooling End Use XCooly,m CoolIndex y CoolUse y,m Sat Type y Type Eff y Type CoolIndex y Structural Index y UEC 01
Type Sat Type 01 Type Eff 01 CoolUsey ,m Knowledge to Shape Your Future Pr ice y ,m Pr ice01 0.15 Incomey ,m Income01 0.20 HHSizey ,m HHSize01 0.20 CDDy ,m CDD01
1999 2002 2005 2008 2011 2014 2017 2020 2023 Efficiency for cooling equipment is given for the total US Seasonal Energy Efficiency Ratio (SEER) is defined as a ratio of the total cooling of a central unitary air conditioner or a unitary heat pump in Btu during its normal annual usage period for cooling and the total electric energy input in watt-hours during the same period Knowledge to Shape Your Future Page 21 Residential Cooling Index (Annual kWh) 1,000 900 800 700 600
1990 Knowledge to Shape Your Future 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 Page 22 Residential XCool Variable Monthly cooling requirements (kWh) Average cooling use increases with increasing air conditioning saturation Knowledge to Shape Your Future Page 23 Residential XHeat Variable Monthly heating requirement (kWh)
Average heating use declines with declining heating saturation Knowledge to Shape Your Future Page 24 Residential Non HVAC End-uses XOthery ,m OtherEqpIndex y ,m OtherUsey ,m OtherUse y ,m Knowledge to Shape Your Future Pr ice y ,m Pr ice01 0.15 Income y ,m Income01 0.20 HHSize y ,m HHSize01 0.20 BDays y ,m 31
Page 25 Residential Non HVAC End-uses (cont.) OtherIndexy ,m Knowledge to Shape Your Future Type Sat y 1 Type UEC y UEC 01Type Type Sat 01 1 Type UEC 01
MoMultType m Page 26 Residential XOther Variable Monthly base use requirement (kWh) Knowledge to Shape Your Future Page 27 Impact of 2007 Energy Act - Lighting New England Lighting UEC (2007-2008) NEC Light07 Light08 2,100 2007 Energy Independence and Security Act introduces a number of new appliance efficiency standards
1,800 1,500 Currentlighting lighting Current standards standards 1,200 900 Newlighting lighting New standards standards 600 300 2006 2009 2012 2015 2018 2021 2024 2027 2030 New England XOther
New lighting standards have the most significant impact on residential load Lighting accounts for approximately 20% of residential other use Knowledge to Shape Your Future Sharpdrop drop Results sharp Sharp inin ininaasharp Results electric sales drop in base use electric sales drop in base use results results Page 28 New England Residential Forecast Comparison (GWh) NEC07 NEC08 10,000 8,000
6,000 Residential energy use Residential energy use 2.5%lower lowerbyby2013 2013 isis2.5% 4,000 2,000 0 2001 2004 2007 2010 2013 2016 2019 2022 2025 2028 Due to the high lighting replacement rate, residential electric sales drop off quickly once the new standards go in place. Knowledge to Shape Your Future Page 29 Estimated SAE Model Residential Average Use Variable Res_StrucVars.WtXHeat Res_StrucVars.WtXCool Res_StrucVars.XOther MBin.Jul99 MBin.Feb05 MBin.Feb07
Knowledge to Shape Your Future Coefficient StdErr T-Stat P-Value 0.933 0.036 26.262 0.00% 0.709 0.04 17.529 0.00% 0.929 0.015 61.84 0.00% -77.244 26.815 -2.881 0.49% -60.864 25.474 -2.389 1.88% -76.892 25.61 -3.002 0.34% Regression Statistics Iterations Adjusted Observations Deg. of Freedom for Error R-Squared Adjusted R-Squared Durbin-Watson Statistic AIC BIC F-Statistic Prob (F-Statistic) Log-Likelihood Model Sum of Squares Sum of Squared Errors Mean Squared Error Std. Error of Regression Mean Abs. Dev. (MAD) Mean Abs. % Err. (MAPE) Ljung-Box Statistic Prob (Ljung-Box) Skewness
Kurtosis Jarque-Bera Prob (Jarque-Bera) 1 107 100 0.914 0.908 1.743 6.51 6.685 151.173 0 -488.52 667649 63092 630.92 25.12 19.85 3.29% 57.45 0.0001 0.015 2.468 1.3 0.3325 Page 30 Predicted Vs. Actual Average Use Knowledge to Shape Your Future Page 31 Residential Sales Forecast by End-Use (GWh) 250,000
Heating Heating 200,000 150,000 100,000 Cooling Cooling BaseUse Use Base 50,000 0 1999 Knowledge to Shape Your Future 2000 2001 2002 2003 2004 2005 2006 2007 2008
2009 2010 2011 2012 Page 32 Residential End-Uses (EIA) Heating electric resistance, heat pump Cooling CAC, room air conditioning, heat pump Other Use Water heating Cooking Refrigeration Second refrigerator Freezer Dishwasher Clothes washer
Dryer Microwave Color TV Lighting Miscellaneous Knowledge to Shape Your Future Page 33 Commercial End-Uses (EIA) Heating Cooling Other Use Ventilation Water heating Cooking Refrigeration Lighting Miscellaneous Knowledge to Shape Your Future Page 34 Vermont Monthly Sales Forecast Models Customer Classes
Residential Commercial Industrial Other Monthly residential and commercial class models are estimated using an SAE specification The industrial sales model estimated using a generalized econometric model We assume historical DSM activity is embedded in the sales data and thus in the estimated models Knowledge to Shape Your Future Page 35 Data Sources Monthly Sales, Customer and Revenue Data Energy Information Agency January 1999 to November 2007 Depending on system loss factor, sales data account for 95% to 97% of delivered system energy Weather Data Historical daily maximum and minimum temperatures Burlington Airport, 1970 to current Evaluated other weather stations, however, there were too many holes in the data series
Burlington-based HDD and CDD explain state-level sales well. Price Data Price series was calculated from reported revenues, sales, and Vermont CPI Price calculated as a 12-month moving average of the prior twelve-month average rate (real basis) Assume constant real price in the forecast Knowledge to Shape Your Future Page 36 Data Sources Economic Data Fall 2007 Vermont economic forecast by Economy.com Population, number of households, real personal income Gross State Product, manufacturing output, non-manufacturing and manufacturing employment Final forecast will be based on Economy.coms current state economic forecast End-Use Saturation and Efficiency Trends Developed from the 2007 EIA Energy Outlook Forecast for New England Currently updating efficiency projections to reflect the recently passed energy bill End-use saturation trends will be calibrated against recent state and Burlington Electric residential saturation surveys Knowledge to Shape Your Future Page 37 Long-Term Vermont Economic Projections Year 1998 1999 2000
Page 41 Class Hourly Profile Data Sources Load Data Burlington Electric Load Research Data Residential Small General Service Large General Service Other Load Research Data Industrial Street Lighting Daily maximum and minimum temperatures Burlington Airport Daily calendar Day of the week, month, holiday, hours of sunlight Knowledge to Shape Your Future Page 42 Class Hourly Profile Models Class Hourly Model Structures Twenty-four hourly regression models HDD and CDD Month, Day of the Week, Holidays Hours of Light Estimation Period January 1, 2004 to December 31, 2006 Knowledge to Shape Your Future Page 43 Calculation of Daily Normal Weather Ten years daily average temperature for Burlington
1998-2007 Rank and Average approach Daily average temperatures ranked from highest to lowest and within each year then averaged across all 10 years Map daily normal ranked weather data to a typical daily weather pattern Typical year weather pattern calculated from historical daily weather data Map the ranked temperature data to the typical year weather pattern Used MetrixLT for calculating daily normal temperature series Knowledge to Shape Your Future Page 44 Chaotic Daily Normal Weather Series Daily normal weather series mapped to the average tenyear weather pattern Knowledge to Shape Your Future Page 45 Residential Load Model (kW per customer) Knowledge to Shape Your Future Page 46 Large General Service Load Model Knowledge to Shape Your Future Page 47 Residential End-Use Profiles
Cooling, heating, and other use profiles estimated from end-use weather response models Data is based on building simulation runs Models simulated for 2004 to 2007 using Burlington actual weather End-use profiles scaled to residential profile model Knowledge to Shape Your Future Page 48 Residential Cooling Profile Knowledge to Shape Your Future Page 49 Residential Heating Profile Knowledge to Shape Your Future Page 50 Load Build-up Comparison UncalibratedComparison Comparison Uncalibrated Build-up Build-up System System CalibratedComparison Comparison Calibrated
Build-up Build-up System System Knowledge to Shape Your Future Page 51 Preliminary Forecast (No Additional DSM) Preliminary Forecast Summary Year 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 % Change 2008 - 2012 2008 - 2017 Energy (MWh) 6,025,111 6,080,086 6,138,657 6,194,323 6,259,013 6,299,108 6,348,971 6,398,778 6,461,555 6,506,521
Summer Peak (MW) 1,089 1,104 1,118 1,130 1,141 1,154 1,165 1,177 1,188 1,203 Winter Peak (MW) 1,070 1,075 1,088 1,087 1,101 1,112 1,119 1,121 1,122 1,119 1.0% 0.9% 1.2% 1.1% 0.7% 0.5% Based on EIA saturation projections Knowledge to Shape Your Future Page 52 Class Coincident Demand
My name is Nazia and I belong to the village which is. located in Union Council Usman Koria. I am at no.1. in sibling. I have four brothers and five sisters. Source. of income is very less than expenditure. My...
Work in the 21st Century Chapter 2 Research Methods and Statistics in I-O Psychology * * Reliability (cont'd) Equivalent forms reliability Calculated by correlating measurements from a sample of individuals who complete 2 different forms of same test Internal consistency...
(There should be three for the expository speech). Organize the main points in a consistent pattern the audience can follow. Outline all material you plan to use in the speech. Planning the Conclusion. Emphasize the key idea(s) of the speech.
Facultative anaerobes . gather mostly at the top, since aerobic respiration is most beneficial; but as lack of oxygen does not hurt them, they can be found all along the test tube.4: Microaerophiles. gather at upper part of test tube,...
This online interface is part of the modelling tools for sustainabledevelopment. It is not a model, butprovideseasyaccess to the methodology behindONSSET, the datasetsused, and the results obtained for a set of predefined scenarios and model runs. The developmentof the interface...
Human African trypanosomiasis, sleeping sickness is a parasitic disease of people and animals, caused by protozoa of the species Trypanosoma brucei and transmitted by the tsetse fly. covering about 36 countries and 60 million people.
The Interwar Period 1920's 1930s' ... who ordered that Soviet tanks move in to stop it. In . 1968. the . Prague Spring . occurred in . Czechoslovakia. This was an attempt to introduce democratic reforms from its new leader,...
Must All Good Things Come to an End? Lewinter & Widulski The Saga of Mathematics * Lewinter & Widulski The Saga of Mathematics * John Napier (1550 - 1617) A Scotsman famous for inventing logarithms. He used this lattice multiplication...
Ready to download the document? Go ahead and hit continue!