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Comparison of Pooled and Household-Level Usage Impact Analysis

Comparison of Pooled and Household-Level Usage Impact Analysis. Jackie Berger Ferit Ucar IEPEC Conference – August 14, 2013. Presentation Outline. Motivation Billing Analysis Usage Impact Models Model Results Summary and Next Steps. 2. MOTIVATION. 3. Usage Impacts.

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Comparison of Pooled and Household-Level Usage Impact Analysis

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  1. Comparison of Pooled and Household-Level Usage Impact Analysis Jackie Berger Ferit Ucar IEPEC Conference – August 14, 2013

  2. Presentation Outline • Motivation • Billing Analysis • Usage Impact Models • Model Results • Summary and Next Steps 2

  3. MOTIVATION 3

  4. Usage Impacts • Were expected energy savings results obtained? • Are the treatments cost-effective? • Should measure selection procedures be revised? • Should installation procedures be reviewed? • Should contractors be re-trained? 4

  5. Analysis Method • Goal: develop most accurate estimate of program savings. • Weigh costs and benefits of various approaches to measurement. • Consider possible causes of mis-measurement or bias. 5

  6. What Are You Measuring? 6

  7. Analysis Approaches 7

  8. Billing Analysis 8

  9. Data Requirements 9

  10. Challenges 10

  11. Treatment and ComparisonGroup Example 2010 2012 2011 Treatment Group Pre-Treatment Period Post-Treatment Period SERVICE DELIVERY DATE Comparison Treated Year Before Post Yr 1 – Quasi Pre Post Yr 2 – Quasi Post SERVICE DELIVERY DATE ComparisonTreated Year After Pre Yr 1 – Quasi Pre PreYr 2 – Quasi Post SERVICE DELIVERY DATE 11

  12. Usage Impact Models 12

  13. House-by-House 13

  14. Pooled Analysis Fit=αi+ β1* Hit+ β2*POSTt+β3*POSTt*Hit+εit • Fit = average daily usage during the pre- and post-treatment periods • Hit = average daily base 60 HDDs • POSTt = a dummy variable that is 0 in the pre-period and 1 in the post-period • εit = estimation error term • PRE USAGE • αi = average daily baseload usage in pre-treatment period. • β1 = average daily usage per HDD in the pre-treatment period. • POST USAGE • αi + β2= average daily baseload usage in the post-treatment period. • β1 + β3= average daily usage per HDD in the post-treatment period. • SAVINGS • β2 = average daily baseload savings • β3 = heating usage savings per HDD. 14

  15. Advantages 15

  16. Disadvantages 16

  17. When to Use 17

  18. Model Results 18

  19. Program 1 ResultsGas Heating Jobs 19

  20. Program 2 ResultsGas Heating Jobs 20

  21. Program 1 ResultsElectric Baseload Jobs 21

  22. Program 2 ResultsElectric Baseload Jobs 22

  23. Program 1 ResultsElectric Heating Jobs 23

  24. Program 2 ResultsElectric Heating Jobs 24

  25. Program 1 ResultsHousehold Characteristics 25

  26. Program 1 ResultsHousehold Characteristics Major measures include refrigerators, air conditioner, and water heater replacements. 26

  27. Program 1 ResultsHousehold Characteristics 27

  28. Summary and Next Steps 28

  29. Summary • Overall savings results fairly consistent • Differences between models rarely statistically significant • Gas usage results were more consistent • Electric baseload varied most 29

  30. Conclusions • Sources and potential biases caused by large data attrition should be explored. • When additional analysis is desired for many subgroups and data attrition is low, house-by-house may be favored. • When data attrition is high and only overall usage results are desired, the pooled regression may be preferred. 30

  31. Next Steps • Additional exploration of differences. • Explore deletion of various types and numbers of observations from house by house. • Compare results with different levels of attrition. • Test different functional forms for the pooled model. 31

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