1. © 2007, Itron Inc. Statistically Adjusted End-Use Model Overview & Thoughts about Incorporating DSM into a Forecast May 4, 2009 Frank A. Monforte, Ph.D. Director, Forecasting Solutions

2. © 2007, Itron Inc.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. © 2007, Itron Inc.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. © 2007, Itron Inc.4 Statistically Adjusted End-use Modeling (cont.) Estimate monthly billed sales regression models:

5. © 2007, 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 Heating Degree Days Cooling Degree Days Billing Days XCool XHeat XOther

6. © 2007, Itron Inc.6 End-Use Variable - Heating

7. © 2007, Itron Inc.7 Residential Heating Weight Variable Weights Estimated heating energy use per household for each equipment type in the base year Where: Equipment TypeWeight (kWh) Electric Furnace/Room Resistance1,414 Electric Space Heating Heat Pump 325

8. © 2007, Itron Inc.8 Residential Structural Index Structural index accounts for > Change in housing square footage > Change in structural thermal integrity: Overall R-Value Where :

9. © 2007, Itron Inc.9 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 Efficiency (Days/KWh) Lighting Efficiency (Lumens/Watt) Changes in Residential Equipment Efficiency

10. © 2007, Itron Inc.10 Residential Heating Saturation Trends

11. © 2007, Itron Inc.11 Residential Heating Efficiency Trends

12. © 2007, Itron Inc.12 Residential XHeat Variable

13. © 2007, Itron Inc.13 End-Use Variable - Cooling

14. © 2007, Itron Inc.14 Cooling Saturation Trends

15. © 2007, Itron Inc.15 Cooling Efficiency Trends

16. © 2007, Itron Inc.16 Residential XCool Variable

17. © 2007, Itron Inc.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. © 2007, Itron Inc.18 XOther Variable

19. © 2007, Itron Inc.19 Electric Water Heater Index Where: WeightEstimated water heating energy use per household in the base year [763 kWh] MoMultMonthly multiplier for the water heating usage in month m

20. © 2007, Itron Inc.20 Other Appliances Index

21. © 2007, Itron Inc.21 Appliance Weights WeightsEstimated appliance energy use per household for each equipment type in the base year Where:

22. © 2007, Itron Inc.22 Residential Non HVAC End-Use Intensities TypeWeight (kWh) Electric cooking 380 Refrigerator 1,005 Second refrigerator 238 Freezer 424 Dishwasher 103 Electric clothes washer 96 Electric clothes dryer 780 TV Sets 348 Lighting 1,964 Miscellaneous electric appliances 2,930

23. © 2007, Itron Inc.23 Example of SAE Inputs (KWh/Household)

24. © 2007, Itron Inc.24 XOther

25. © 2007, Itron Inc.25 Residential & Commercial SAE Model Regions

26. © 2007, Itron Inc.26 Average Use Model Results Actual Predicted

27. © 2007, Itron Inc.27 Residential Sales Forecast Average Use (Kwh) Customers Sales (Gwh)

28. © 2007, Itron Inc.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. © 2007, Itron Inc.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. © 2007, Itron Inc. A1 A2A3A4 A8 B1 B2B3 B7 B8 Year 1Year 2Year 3Year 4Year 8 Year 9 C1 C2 C6 C7 C8 Year 10 A5 B4 Year 5 C3 A6 B5 Year 6 C4 A7 B6 Year 7 C5 Past Years Future Years Continuing Impact of Past Programs Historical Impact of Past Programs Aggregate Savings in Year 1 = A1 Aggregate Savings in Year 2 = A2 + B1 Aggregate Savings in Year 3 = A3 + B2 + C1 Understanding Past DSM Year to year impacts are needed for forecasting DSM impacts fade in the future

31. © 2007, Itron Inc. A1 A2A3A4 A8 B1 B2B3 B7 B8 Year 1Year 2Year 3Year 4Year 8 Year 9 C1 C2 C6 C7 C8 Year 10 A5 B4 Year 5 C3 A6 B5 Year 6 C4 A7 B6 Year 7 C5 Past Years Future Years Historical Impact of Past Programs D1 D2 D6 D7 D3 D4 D5 E6 E5 E1 E2 E3 E4 F1 F2 F3 F4 F5 G1 G2 G3 G4 H1 H2 H3 I1 I2 J1 Impact of Future Programs Continuing Impact of Past Programs Cumulative Impact of Past and Future Programs Understanding Future DSM Year to year impacts are needed for forecasting Future program are expect to replace the existing program drop-off

32. © 2007, Itron Inc. Forecast If there were No DSM programs Forecast with Future DSM Load Forecast Period Historical Period Measured Load Typical Forecast Process Subtract New DSM Programs and Goals

33. © 2007, Itron Inc. History Before DSM Forecast With Past DSM, but without Future DSM Forecast with Past and Future DSM Load Forecast Period Historical Period Measured Load Measured Load with DSM added back DSM Begins Method 1: Reconstitute Loads (1)Load + DSM history = f(x) – DSM history – DSM future Forecast No DSM, but includes National Trends

34. © 2007, Itron Inc. 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. © 2007, Itron Inc. History Before DSM Forecast With past DSM without Future DSM Forecast with Future DSM Load Forecast Period Historical Period Measured Load Method 2: Include DSM Variable DSM Begins Method 2: DSM Variable (2)Load = f(x,DSM history + DSM future )

36. © 2007, Itron Inc. 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. © 2007, Itron Inc. Method 3: DSM Trend Model A1 A2 A3A4 B1 B2B3 Year 1Year 2Year 3Year 4Year 8 Year 9 C1 C2 Year 10 A5 B4 Year 5 C3 Year 6 Year 7 Past Years Future Years Historical Impact of Past Programs D1 D2 E1 Trend Forecast of Cumulative Impacts with Stable Programs DSM Trend = g(x)

38. © 2007, Itron Inc. History Before DSM Forecast with Stable DSM calibrated to Utility Trends Forecast with Future DSM adjusted for trend Load Forecast Period Historical Period Measured Load DSM Begins Method 3: Model DSM Trend (3)Load = f(x) – DSM AboveTrend WhereDSM AboveTrend = DSM future – g(x)

39. © 2007, Itron Inc. 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. © 2007, Itron Inc. How do you construct a DSM Series to support Forecasting? 40

41. © 2007, Itron Inc. Refrigerator Replacement Program Stock Additions Program Ends December 2006 Program Ends December 2006

42. © 2007, Itron Inc. Refrigerator Replacement Program Stock Accounting Program Measure Life is 18 Years Program Time Period

43. © 2007, Itron Inc. Refrigerator Replacement Program Monthly Savings Monthly Usage Fractions convert Program Stock into Monthly Savings Impacts Monthly Usage Fractions convert Program Stock into Monthly Savings Impacts

44. © 2007, Itron Inc. Total DSM Program Stock and Savings Impacts Historical Time Period

45. © 2007, Itron Inc. CFL Distribution Program CFL Program Measure Life is 11 Years

46. © 2007, Itron Inc. CFL Distribution Program – Double Counting Issue SAE Lighting UPC Index (KWh/Year) SAE Lighting UPC Index (KWh/Year) DSM Savings Path is computed against a Dynamic Baseline, resulting from the Standards. DSM Savings Path is computed against a Dynamic Baseline, resulting from the Standards.