Statistically Adjusted End-Use Forecasting Incorporating End-Use Structure in an Econometric Model

Transcription

1 Statistically Adjusted End-Use Forecasting Incorporating End-Use Structure in an Econoetric Model

2 Residential Forecast Model- Typical Specification Regression Model Ten to fifteen years of historical onthly data Specification AvgUse t = f (Price t, Inc t, HHSize t, CDD t, HDD t, Trend t ) Cust t t = f (HH t, Building Perits t, Vacancy Rate t ) Res Sales t = Cust t AvgUse t Drop in variables and see what happens Forecast with noral HDD and CDD

3 Advantages Established Methodology Well suited for identifying sales trends and forecasting sales into the near-ter Can account for short-ter changes in econoic conditions Can be used for weather noralizing sales and deand Relatively easy to estiate and aintain

4 Disadvantages Often difficult to get the right sign and size on the odel coefficients Difficult to capture all relevant econoic variables Doesn t account for structural changes changing technology saturation and efficiency trends Generally, not useful, for forecasting long-ter energy requireents

5 Issue Can we incorporate end-use structure that itigates onthly sales odeling disadvantages? We want to account for: Econoic ipacts incoe, household size, household growth Price ipacts Structural changes saturation and efficiency trends Weather ipacts Appropriate interaction of these variables One odel for short and long-ter forecasting

6 Statistically Adjusted End-Use (SAE) Model Ipose odel structure through SAE specification Construct high-level end-use variables: Heating, Cooling, Other Use Ebed forecast drivers into the constructed variables End-use saturation and efficiency trends HDD, CDD, price, incoe, and household size Estiate average use regression odel using the constructed end-use variables AvgUse t = B 0 + B 1 XCool t + B 2 XHeat t + B 3 XOther t + e t

7 Statistically Adjusted End-Use (SAE) Model AC Saturation Central Roo AC AC Efficiency Theral Efficiency Hoe Size Incoe Household Size Price Heating Saturation Resistance Heat Pup Heating Efficiency Theral Efficiency Hoe Size Incoe Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Incoe Household Size Price Cooling Degree Days Heating Degree Days Billing Days XCool XHeat XOther Sales = a + b XCool + b XHeat + b XOther + c h o e

8 End-Use Variable - Heating XHeat = HeatIndex HeatUse y, y y, HeatIndex y = Structural Indexy Weight Type Type Sat Sat Type y Type 01 Eff Eff Type y Type 01 HeatUse y, = HDD HDD y, 01 HHSize HHSize y, Incoe Incoe y, Pr ice Pr ice y,

9 Residential Heating Saturation Trends 40% Furnace/Resistance HPHeat 30% Saturation 20% 10% 0%

10 Residential Heating Efficiency Trends Furnace/Resistance HPHeat Efficiency [BTU/Wh]

11 Residential XHeat Variable kwh per Custoer

12 End-Use Variable - Cooling XCool = CoolIndex CoolUse y, y y, CoolIndex y = StructuralIndexy Weight Type Type y Sat Sat Type y Type 01 Eff Eff Type y Type 01 CoolUse y, CDD = CDD y, 01 HHSize HHSize y, Incoe Incoe y, Pr ice Pr ice y, by 0.10

13 Cooling Saturation Trends 100% 75% Saturation 50% CAC HPCool RAC 25% 0%

14 Cooling Efficiency Trends 15.0 CAC HPCool RAC 12.0 Efficiency [BTU/Wh]

15 Residential XCool Variable kwh per Custoer

16 Factors Ipacting Other Use Nonweather-sensitive end-use saturation and efficiency trends Nuber of billing days Hours of light Household size and incoe Water teperature Prices

17 Appliance Indices 2500 Refrigerators & Freezers Water Heating Lighting 2000 kwh per Custoer

18 XOther kwh per Custoer

19 Regression Coefficients REGRESSION COEFFICIENTS Variable Coefficient Standard Error T-Statistic XHeat XCool XOther

20 Regression Statistics REGRESSION STATISTICS Estiation Period: Jan 1994 to Sep 2003 Adjusted Observations Degrees of Freedo for Error Adjusted R-Squared Durbin-Watson Statistic F-Statistic Probability (F-Statistic) Standard Error of Regression Mean Absolute Deviation (MAD) Mean Absolute Percent of Error (MAPE) %

21 Average Use Model Results 2,000 1,800 1,600 Actual Predicted kwh per Custoer 1,400 1,200 1,

22 Decoposition of Predicted Value Cooling Use Heating Use Base Use Sales (GWh)

23 Use Per Household- Annual Long-Ter Res_AvgUse 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

24 Residential Sales Forecast Res_GWh 3,500 3,000 2,500 2,000 1,500 1,

25 SAE Model Specification Conclusions The odel specification generally proves to work extreely well in explaining historical sales trend. By iposing odel structure (elasticities) we can capture the appropriate ipacts of changes in econoic conditions. Appliance saturation and efficiency trends are ebedded in the odel structure. The odel specification allows us to decopose the onthly and annual forecasts into the priary end-use coponents.

26 SAE Model Specification Conclusions The SAE odeling approach allows us to develop forecast scenarios for alternative econoic assuptions, prices, and appliance saturation and efficiency trends. By design, the SAE odel calibrates into actual sales. We can use the sae odel for forecasting both shortter and long-ter energy requireents.

27 Exercise: Build a Residential SAE odel Open Wkshp_Res.nd Insert a regression odel Regress residential average use on XHeat, XCool, and XOther Estiate the ode fro 1997 to 2007 Forecast through 2015 Insert a graph object Copare the SAE average use forecast with your current residential average use forecast How are they different? Why? See if you can iprove the odel fit with needed binaries and AR or MA corrections Calculate end-use sales forecasts Create a onthly transforation table Estiate end-use forecasts HeatSales = B1 * XHeat * Custoers CoolSales = B2 *XCool * Custoers OtherSales = B3 * XOther *Custoers