SAE Model Objective

1. Statistically Adjusted End-Use Model Overview & Thoughts about Incorporating DSM into a ForecastMay 4, 2009Frank A. Monforte, Ph.D.Director, Forecasting Solutions

2. SAE Model Objective • Develop an approach that incorporates the best characteristics of an econometric and end-use modeling framework • We want to account for: • Economic impacts • Income • Household size • Household growth • Price impacts • Structural changes • Saturation and efficiency trends • Housing square footage • Thermal shell integrity improvements • Weather Impacts • Appropriate impact of these variables • Ideally, one model for short and long-term forecasting

3. Statistically Adjusted End-use Modeling • 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)

4. Statistically Adjusted End-use Modeling (cont.) Estimate monthly billed sales regression models:

5. Statistically Adjusted End-Use (SAE) Model Heating Saturation Resistance Heat Pump Heating Efficiency Thermal Efficiency Home Size Income Household Size Price AC Saturation Central Room AC AC Efficiency Thermal Efficiency Home Size Income Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Income Household Size Price Heating Degree Days Billing Days Cooling Degree Days XOther XCool XHeat

6. End-Use Variable - Heating

7. Residential Heating Weight Variable Weights Estimated heating energy use per household for each equipment type in the base year Where:

8. Residential Structural Index Structural index accounts for • Change in housing square footage • Change in structural thermal integrity: Overall R-Value Where:

9. Changes in Residential Equipment Efficiency Heat Pump Heating Efficiency (HSPF) Central Air Conditioner Efficiency (SEER) Electric Water Heater Efficiency (Energy Factor) Refrigerator Efficiency (Cubic Feet/KWh/Day) Electric Cloths Dryer Efficiency (Days/KWh) Lighting Efficiency (Lumens/Watt)

10. Residential Heating Saturation Trends

11. Residential Heating Efficiency Trends

12. Residential XHeat Variable

13. End-Use Variable - Cooling

14. Cooling Saturation Trends

15. Cooling Efficiency Trends

16. Residential XCool Variable

17. Factors Impacting Other Use • Nonweather-sensitive end-use saturation and efficiency trends • Number of billing days • Hours of light • Household size and income • Water temperature • Prices

18. XOther Variable

19. Electric Water Heater Index Where: Weight Estimated water heating energy use per household in the base year [763 kWh] MoMult Monthly multiplier for the water heating usage in month m

20. Other Appliances Index

21. Appliance Weights Weights Estimated appliance energy use per household for each equipment type in the base year Where:

22. Residential Non HVAC End-Use Intensities

23. Example of SAE Inputs (KWh/Household)

24. XOther

25. Residential & Commercial SAE Model Regions

26. Average Use Model Results Predicted Actual

27. Residential Sales Forecast Average Use (Kwh) Customers Sales (Gwh)

28. SAE Model Specification Conclusions • The model specification generally proves to work well in explaining historical sales trend. • By imposing model structure (elasticities), we can capture the appropriate impacts of changes in economic conditions. • Appliance saturation and efficiency trends are embedded in the model structure. • Integrates end-use structural indices that will stand-up to scrutiny in a regulatory environment • Allows us to decompose the monthly and annual forecasts into the primary end-use components

29. SAE Model Specification Conclusions • The SAE modeling approach allows us to develop forecast scenarios for alternative economic assumptions, prices, and appliance saturation and efficiency trends. • SAE models are significantly easier to maintain and update than traditional end-use models. • By design, the SAE model “calibrates” into actual sales. We can use the same model for forecasting both short-term and long-term energy requirements.

30. Understanding Past DSM • Year to year impacts are needed for forecasting • DSM impacts fade in the future Aggregate Savings in Year 1 = A1 Aggregate Savings in Year 2 = A2 + B1 Aggregate Savings in Year 3 = A3 + B2 + C1 Continuing Impact of Past Programs Historical Impact of Past Programs C2 C3 C4 C5 C1 C6 B2 B3 B4 B5 B6 B1 C7 B7 A2 A3 A4 A5 A6 A7 C8 A1 B8 A8 Year 10 Year 9 Year 1 Year 2 Year 3 Year 4 Year 7 Year 8 Year 5 Year 6 Past Years Future Years

31. Understanding Future DSM Cumulative Impact of Past and Future Programs • Year to year impacts are needed for forecasting • Future program are expect to replace the existing program drop-off J1 I1 H1 G1 I2 H2 G2 F2 F1 H3 G3 F3 E2 E3 E1 Impact of Future Programs F4 G4 E4 Historical Impact of Past Programs D2 D3 D4 D1 F5 D5 E5 C2 C3 C4 C5 C1 C6 D6 E6 Continuing Impact of Past Programs B2 B3 B4 B5 B6 B1 C7 D7 B7 A2 A3 A4 A5 A6 A7 C8 A1 B8 A8 Year 10 Year 9 Year 1 Year 2 Year 3 Year 4 Year 7 Year 8 Year 5 Year 6 Past Years Future Years

32. Typical Forecast Process Forecast If there were No DSM programs Load Forecast with Future DSM Measured Load Subtract New DSM Programs and Goals Historical Period Forecast Period

33. Method 1: Reconstitute Loads (1) Load + DSMhistory = f(x) – DSMhistory –DSMfuture Forecast No DSM, but includes National Trends Measured Load with DSM added back Forecast with Past and Future DSM Load Forecast With Past DSM, but without Future DSM Measured Load History Before DSM DSM Begins Historical Period Forecast Period

34. Method 1 Issues DSM measurement accuracy will impact model coefficients. Long history of DSM results in model based on potentially “unrealistic” history. SAE national trends include DSM which is still included on the right-hand-side.

35. Method 2: DSM Variable Method 2: Include DSM Variable (2) Load = f(x,DSMhistory + DSMfuture) Forecast With past DSM without Future DSM Forecast with Future DSM Load Measured Load History Before DSM DSM Begins Historical Period Forecast Period

36. Method 2 Issues DSM variable coefficient must past statistical test. DSM coefficient is used for future DSM programs DSM investment patterns that are similar to national trends will result in a “0” coefficient.

37. Method 3: DSM Trend Model DSM Trend = g(x) Historical Impact of Past Programs Trend Forecast of Cumulative Impacts with Stable Programs E1 D2 D1 C2 C3 C1 B2 B3 B4 B1 A2 A3 A4 A5 A1 Year 10 Year 9 Year 1 Year 2 Year 3 Year 4 Year 7 Year 8 Year 5 Year 6 Past Years Future Years

38. Method 3: Model DSM Trend (3) Load = f(x) – DSMAboveTrend Where DSMAboveTrend = DSMfuture – g(x) Forecast with Stable DSM calibrated to Utility Trends Load Forecast with Future DSM adjusted for trend Measured Load History Before DSM DSM Begins Historical Period Forecast Period

39. Method 3 Issues DSM Trend model may not reasonably capture underlying DSM actually represented in the forecast model. Trend model may not capture measure life of DSM technologies or differences in programs

40. How do you construct a DSM Series to support Forecasting?

41. Refrigerator Replacement Program Stock Additions Program Ends December 2006

42. Refrigerator Replacement Program Stock Accounting Program Time Period Program Measure Life is 18 Years

43. Refrigerator Replacement Program Monthly Savings Monthly Usage Fractions convert Program Stock into Monthly Savings Impacts

44. Total DSM Program Stock and Savings Impacts Historical Time Period

45. CFL Distribution Program CFL Program Measure Life is 11 Years

46. CFL Distribution Program – Double Counting Issue SAE Lighting UPC Index (KWh/Year) DSM Savings Path is computed against a Dynamic Baseline, resulting from the Standards.