SAE Model Overview. Eric Fox 2010 Energy Forecasting Week Las Vegas, Nevada Forecasters Forum/EFG Meeting

Transcription

1 SAE Model Overview Eric Fox 2010 Energy Forecasting Week Las Vegas, Nevada Forecasters Forum/EFG Meeting April 29 30, 2010

2 SAE Model Overview The Statistical Adjusted End Use Model (SAE) is a modeling approach that explicitly incorporates end use saturations and efficiencies, and housing thermalintegrity trends into monthly billed sales regression models End use data are developed for the residential and commercial electric and gas sectors End use data projections developed from the EIA Annual Energy Outlook (AEO) for nine census regions 2009, Itron Inc.

3 Residential & Commercial SAE Model Regions 2009, Itron Inc. 3

4 Objective Develop a theoretically sound econometric model that captures both impact of factors that affect short term energy use as well as long term structural changes > A single model for short term budget forecast and long term resource planning Monthly average use for budget planning (kwh/customer) Annual average use for resource planning (kwh/customer) 2009, Itron Inc.

5 Statistically Adjusted End Use (SAE) Model AC Saturation Central Room AC AC Efficiency Thermal Efficiency Home Size Income Household Size Price Heating Saturation Resistance Heat Pump Heating Efficiency Thermal Efficiency Home Size Income Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Income Household Size Price Cooling Degree Days Heating Degree Days Billing Days XCool XHeat XOther AvgUse = a + b XCool + b XHeat + b XOther + m c m h m o m e m 2009, Itron Inc. 5

6 End Use Model Framework Forms Basis for SAE Model Specification The end use central equation: Sales e = Households * Saturation e * UEC e Where: > Saturation e = the number of homes that own end use e > UEC e = the annual energy usage for end use e End use energy intensity: EI e = Saturation e * UEC e > Average annual usage per household h > Same as the index values used in the SAE model 2009, Itron Inc. 6

7 Unit Energy Consumption UEC = e Size Usage e Efficiency e e where: Size e Usage e = Average size of end use e = Measure of the intensity of appliance usage Efficiency e = Average efficiency for end use e 2009, Itron Inc. 7

8 Factors that Drive Usage Short Term Utilization Variables > Temperature > Hours of Light > Price > Household Size > Household Income > Business Activity (GSP) Long Term Structural Variables > Appliance Ownership (Saturation) > End use Efficiency > Housing Size (Square Footage) > Housing Shell Efficiency > End use Commercial Energy Intensity (kwh/square Feet) 2009, Itron Inc. 8

9 2009, Itron Inc. End Use Saturation Trends

10 End Use Efficiency Trends Central Air Conditioner Efficiency (SEER) Heat Pump Heating Efficiency (HSPF) Refrigerator Efficiency (Cubic Feet/KWh/Day) Electric Water Heater Efficiency (Energy Factor) Electric Cloths Dryer Lighting Efficiency i Efficiency (Days/KWh) (Lumens/Watt) 2009, Itron Inc.

11 Statistically Adjusted End Use Model Construct end use variables that incorporate both structural data and factors that drive stock utilization > Cooling = f (Saturation, ti Efficiency, i Utilization) Utilization = g (Weather, Price, Income, Household Size) Estimate monthly regression model using billed sales AvgUse t + ( b1 XHeatt ) + ( b2 XCoolt ) + ( b XOthert ) + t = b0 3 ε 2009, Itron Inc.

12 End Use Variable Heating XHeat = HeatIndex HeatUse y,m y y,m HeatIndex y = Structural Index y Weight Type Type Sat y Sat Type Type 05 Eff Type y Eff 05 Type HeatUse y, m = HDD HDD y, m 05 HHSize HHSize y, m Income Income y, m Pr ice Pr ice y, m , Itron Inc. 12

13 Residential XHeat Variable mer per Custo kwh , Itron Inc. 13

14 Residential XCool Variable kwh per Custom mer , Itron Inc. 14

15 XOther kwh per Custom mer , Itron Inc. 15

16 Regression Coefficients REGRESSION COEFFICIENTS Variable Coefficient i Standard d Error T-Statistic ti ti XHeat XCool XOther , Itron Inc. 16

17 Regression Statistics REGRESSION STATISTICS Adjusted Observations 104 Degrees of Freedom for Error 93 Adjusted R-Squared Durbin-Watson Statistic F-Statistic Probability (F-Statistic) 0 Standard Error of Regression Mean Absolute Deviation (MAD) Mean Absolute Percent of Error (MAPE) 2.69% 2009, Itron Inc. 17

18 Average Use Model Results 2, ,800 1,600 Actual Predicted kwh per Custome er 1,400 1,200 1, , Itron Inc. 18

19 Decomposition of Predicted Value Cooling Use Heating Use Base Use Sa ales (GWh) , Itron Inc. 19

20 Use Per Household Annual Long Term Res_AvgUse 14,000 12,000 10,000 8,000 6,000 4,000 2, , Itron Inc. 20

21 Residential Sales Forecast Res_GWh 3,500 3,000 2,500 2,000 1,500 1, , Itron Inc. 21

22 Summary SAE model provides a theoretically strong modeling framework to incorporate the long term impacts of end use saturation and end use efficiency trends. Models have proven to work well for both short termterm budget forecasting and long term planning. The SAE model is ideally suited for developing forecast scenarios for alternative economic and population assumptions, prices, and appliance saturation and efficiency trends. 2009, Itron Inc. 22