Measures that Save The Most Energy

1. Measures that Save The Most Energy Jackie Berger David Carroll ACI New Jersey Home Performance Conference March 5, 2010

2. Session Outline • Introduction • Measuring Energy Savings – Projections • Measuring Energy Savings – Billing Data • Average Savings by Type of Measure • Energy Education Savings Potential • Maximizing Measure Savings • Conclusions

3. Introduction - Perspective • Evaluator’s Perspective • Based on findings from: • Program design research • Survey research • In-field research • Energy impacts

4. Introduction - Scope • Sources • APPRISE evaluation studies • Blasnik and Associates evaluation studies • Dalhoff and Associates evaluation studies • ECW plug load study • Geographic scope • Northeast • Midwest • Mountain

5. MEASURING ENERGY SAVINGSProjections

6. Projected Savings vs. Measured Savings • Value of projections • Projection methodology • Issues with projections • Comparison of projected savings to measured savings

7. Projections vs. Impacts

8. Projections vs. Impacts • Basic Projection Methodology • Assumptions • Measure installation rates • Measure retention rates • Pre installation usage • Measure effectiveness

9. Projections vs. Impacts • Basic Projection Methodology • Calculation • Average household saving = Measure Installation Rate * Measure Retention Rate * (Pre Installation Usage – Post Installation Usage)

10. Projections vs. Impacts • Basic Projection Methodology • Calculation • Pre Installation Usage per bulb per hour = 60 watts * .001 = .06 kWh • Post Installation Usage per bulb per hour = 13 watts * .001 = .013 kWh • Change per Bulb per hour =.06 - .013 = .047 kWh

11. Projections vs. Impacts • Basic Projection Methodology • Calculation • Change per bulb per day = .047 kWh * 2.5 hours/day = .1175 kWh/day • Change per bulb per year = . 1175 kWh/day * 365 days = 43 kWh/year

12. Projections vs. Impacts • Basic Projection Methodology • Calculation • Number installed per home = 43 kWh * 8 bulbs = 344 kWh • Retention rate = 344 kWh *.8 = 275 kWh saved per home per year

13. Projections vs. Impacts So simple, what could go wrong… • Incorrect assumptions • Measure installation rate • Measure retention rate • Bulbs left for occupants to install • Bulbs removed • Bulbs broken • Existing bulb kWh • Hours of use

14. Projections vs. Impacts So simple, what could go wrong… • Interactions • Adding up individual measure savings can overstate results • Need to account for reduced heat gain from CFLs • Increase heating usage • Reduce cooling usage

15. Projections vs. Impacts

16. Projections vs. Impacts • Source: M. Blasnik and Associates.

17. Projections vs. Impacts How far are we off with the projections? • Evaluations that measure actual usage impacts usually find 50% to 70% of projected savings • NEAT Audit – measured savings were 57% and 54% of projected savings (Sharp, 1994 and Dalhoff, 1997) • Ohio electric baseload savings were 58% to 68% of projected • NJ electric baseload savings were 60% - 69% of projected Source: M. Blasnik and Associates.

18. MEASURING ENERGY SAVINGSBilling Data

19. Average Savings by Measure Type • Methodology for developing measured savings • Methodology for attribution of savings to measures • Evaluation findings – electric baseload • Evaluation findings – space heating measures

20. Usage Impact Analysis • Usage Impact Methodology • Obtain pre and post energy usage data for program participants • Use regression model to adjust usage for changes in weather from “normal weather year” • Construct weather normalized change in usage for treated households • Construct weather normalized change in usage for comparison households

21. Usage Impact Analysis • Usage Impact Methodology • Run regression to determine measure specific impacts Usage change = α + β * household characteristics + γ1* measure1 + γ2* measure2 + γ3* measure3 + μ

22. Measure Savings – Evaluation Findings 1M. Blasnik and Associates. 2APPRISE.

23. Measure Savings – Evaluation Findings Source: M. Blasnik and Associates.

24. Measure Savings – Evaluation Findings Source: M. Blasnik and Associates.

25. Measure Savings – Evaluation Findings 1M. Blasnik and Associates 2APPRISE

26. ENERGY EDUCATIONPotential Savings

27. Potential for Education • Major opportunities • Potential vs. realization • Successful models

28. Potential Education Savings AC – 72 to 75 degrees, heating 72 to 70 degrees

29. ECW Plug Load Study • Telephone survey and mailed appliance survey • 50 site visits • Household survey • Electronics inventory • Metering (5-30 appliances per home) • Metered for one month • 6-minute intervals • Computers, televisions, audio, telephone, • HVAC – space heaters, dehumidifiers, room AC, fans, humidifiers • Kitchen appliances

30. ECW Plug Load StudyPotential Education Savings

31. ECW Plug Load Study Potential Education Savings

32. ECW Plug Load StudyPotential Education Savings • Saving Strategies • Power management • Unplug • Turn off • Use timer • Use power strip • Assessment • Potential savings • Motivation

33. Education Impacts

34. Education Impacts

35. Behavioral Impacts

36. Recap • Projected savings tend to overestimate • Billing data are critical • Potential for savings from education

37. Maximizing Savings • Programs that save the most: • Target measures to the highest use households • Install measures in a way that maximizes effectiveness • With an understanding of what is going on in this house

38. Targeting

39. Targeting 1M. Blasnik and Associates. 2APPRISE.

40. Measure Effectiveness • Duct Sealing • Ducts outside envelope = High Savings • Ducts inside envelope = Low/No Savings • Ducts in basement or crawl space = It Depends • Insulation • With properly sealed envelope = High Savings • Without air sealing = Low Savings

41. Focus on This House • Example – Baseload Job in Massachusetts House • Pre-visit Information: Annual electric usage of 10,000 kWh • On-Site Measurement: 6,000 kWh for appliances / 4,000 kWh for space heater • Problem: Program only pays for baseload measures • Solution: Install cfls, encourage behavioral changes, and refer to electric heat program

42. Maximizing Savings • Programs that save the most per dollar spent: • Spend lots more when there are more opportunities • Spend substantially less when there are fewer opportunities

43. Targeting

44. Maximizing Savings • Programs that save the most per dollar spent: • Conduct tests to focus resources and time • Use models as a guide for action

45. Testing • Field inspections of New Jersey programs found that better testing was needed to … • Find and isolate sources of infiltration in complex structures (enclosed porch, addition, sun room) • Identify unobservable leaks in ductwork outside the thermal envelope

46. Testing • Blasnik refrigerator study found that testing is needed, but more is not necessarily better … • Low Savings / Net Benefits • Rating Protocol = $101 • 1 Hour Metering = $111 • 2 Week Metering = $135 • High Savings / Net Benefits • Rating Protocol = $419 • 1 Hour Metering = $414 • 2 Week Metering = $445

47. Audit Tools / Modeling • Benefits • Clarify decision rules on measure installation • Improve consistency across program • Barriers • Data entry can be a communications barrier • Reconciliation is poorly understood

48. Financial Decision Rules • Spending Limits • Do they focus delivery on highest saving measures or restrict delivery of cost-effective measures? • Spending Goals • Do they ensure comprehensiveness or encourage a program to over-invest? • Spending Targets • Do they furnish flexibility or result in over-investment in some homes and under-investment in others?

49. Recommendations • Usage Data – Essential for good decision-making • Decision Criteria - Field staff need a good tool for determining which measures to install • Financial Guidelines – Should vary with energy savings potential and should be expressed as a range

50. Contact Information • Jackie Berger, 609-252-8009, jackie-berger@appriseinc.org • David Carroll, 609-252-8010, david-carroll@appriseinc.org