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Preliminary Impact Analysis of SCE’s 04-05 HEES Program

Preliminary Impact Analysis of SCE’s 04-05 HEES Program. Population Regression using Consumption Trend of Participants’ Neighborhoods as Covariates John Peterson, Athens Research Carol Yin, Yinsight CALMAC, October 17, 2007. SCE’s long standing interest in energy savings from home audits.

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Preliminary Impact Analysis of SCE’s 04-05 HEES Program

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  1. Preliminary Impact Analysis of SCE’s 04-05 HEES Program Population Regression using Consumption Trend of Participants’ Neighborhoods as Covariates John Peterson, Athens Research Carol Yin, Yinsight CALMAC, October 17, 2007

  2. SCE’s long standing interest in energy savings from home audits • RER study (1997) of SCE’s 1993 & 1995 residential audit programs • Net 1st year savings: In Home (433 kWh), Telephone (154 kWh), Mail-In (123 kWh) • Ridge study (2002, on CALMAC.org) • Net 1st year savings: In Home (440 kWh), Telephone (185 kWh), Mail-In (123 kWh), Online (123 kWh) • BUT, savings estimates are outdated

  3. Benefits of using the ecological consumption trend • Consumption related variables arguably relatively homogeneous at block group/dwelling level • Accounts for weather changes and difficulties linking weather station data to small areas • Accounts for economic differences between areas, and changes over time within those relatively homogenous areas • Accounts for demographic features, cultural and behavioral trends

  4. Method • Models estimated at a gross population level: whether or not the customer has participated in 04-05 HEES • Preliminary analysis does NOT track causal flow of savings from recommendation adoptions • Filtered 04-05 participants on the basis of: • 12 months of pre-audit consumption data, 12 months of post-audit and post 10 month deadband data (others deadbands were tested). • The ecological consumption trend • Calculated from calendarized residential consumption data from 2002-2007 • Averaged by dwelling type (single family/other) within geography (block group/tract/zip) so that each participant compared with at least 100 neighbors • Ecological consumption data scaled on participant by participant basis, so pre-period average consumption of participant and neighbors matched.

  5. Preliminary Results Based on simple ecological adjustment “meatgrinder” • OVERALL RESULT FOR SCE: < 200 kWh per year. IN HOME AUDIT about 50% more savings than MAIL-IN. • LONG VERSION ON-LINE: Negligible savings, yet some evidence that install recommendations lead to savings. AUDIT - RECOMMENDATION KWH/DAY PATTERN IMPACT STDERR KWH/YR T_VAL ------------------------------------------------------------------------- IH_AUDIT, ANY REC-0.8621 0.0546 -314.7 -15.79 IH_AUDIT,PRACTICE -0.8621 0.0546 -314.7 -15.79 IH_AUDIT, INSTALL -0.8693 0.0561 -317.3 -15.50 MI_AUDIT, ANY REC-0.5802 0.0203 -211.8 -28.58 MI_AUDIT,PRACTICE -0.5805 0.0203 -211.9 -28.60 MI_AUDIT, INSTALL -0.8110 0.0260 -296.0 -31.19 OL_AUDIT, ANY REC 0.1005 0.0360 36.7 2.79 OL_AUDIT,PRACTICE 0.1010 0.0360 36.9 2.81 OL_AUDIT, INSTALL -0.2850 0.0554 -104.0 -5.14

  6. Remaining Issues and Recommendations • Ecological trend consumption approach is an effective way to isolate adjusted gross savings • Applicable to most consumption analyses of residential and small commercial programs • Still need to track savings that flow causally through adoptions of HEES recommendations • Need to understand measure-level impacts • But we have extreme collinearity due to… • …Too many measures. Huge numbers of recommendations were made to every 04-05 participant.

  7. More questions? John Peterson Athens Research petersjw@aol.com (626) 798-3147 Carol Yin Yinsight cyin@yinsight.net (626) 676-2198

  8. Ecological Consumption Adjustment Approach: Path Model of Role of Ecological Trend • 1. Almost no direct impacts of weather upon customer consumption. • 2. Virtually all of the weather impacts “flow through” the ecological consumption term. • 3. The net neighborhood trend term is very strong, 0.8931. • Since weather only accounts for about 21% of neighborhood trend, this means that a great deal of the neighborhood adjustment involves either non-weather variables or micro-climatic impacts that are not well captured by weather stations. This unique impact is .8865 * .8931 or 0.792.

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