Measures that Save The Most Energy

1. Measures that Save The Most Energy Jackie Berger David Carroll ACI New Jersey Home Performance Conference January 25, 2007

2. Session Outline • Introduction • Key Concepts • Projected Savings vs. Measured Savings • Average Savings by Type of Measure • Energy Education Savings Potential • Maximizing Measure Savings • Conclusions

3. Introduction - Focus • Focus – Measures that save the most… • Energy per measure • Energy per household • Energy per dollar spent • Other Important Issues • Measures that save the most… • Electricity, gas, or fuel oil • Greenhouse gas emissions • Lives • Measures that furnish the greatest… • Avoided costs • Economic benefits • Note: Dollars saved vary with energy price

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

5. Key Concepts • Measures that save the most: • Target highest use households • In a way that maximizes effectiveness • With an understanding of what is going on in this house • Measures that save the most per dollar spent • Balance delivery costs with energy saving opportunities • Spend less when there are fewer opportunities • Spend more when there are more opportunities • We recommend using “measured savings”

6. Targeting

7. Measure Effectiveness • Duct Sealing • Ducts outside envelope = High Savings • Ducts inside envelope = Low/No Savings • Ducts in basement = ???? • Ducts in crawl space = ???? • Insulation • With properly seal envelope = High Savings • Without air sealing = Low Savings

8. Focus on This House • Example – Baseload Job in Massachusetts House • Previsit 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

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

10. Projections vs. Impacts

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

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

13. 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

14. 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

15. 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

16. 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

17. Projections vs. Impacts

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

19. 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.

20. 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

21. 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

22. 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

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 Source: M. Blasnik and Associates.

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

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

28. Education Impacts

29. Education Impacts

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

31. Maximizing MeasureSavings • Look at energy bills • Do diagnostic tests /take measurements • Apply bills, tests, and measurements to decision criteria • Build a package of measures that are complimentary and complete

32. Energy Bills

33. Tests and Measurements • Determine how each system is performing • HVAC • Thermal envelope • Heating / cooling distribution • Equipment Performance • Controls • Other end uses • Refrigerator metering • Water flow test

34. Decision Criteria • Technical • Audit Tool (e.g. NEAT audit) • Priority List • Rule of Thumb • Financial • Spending limit • Spending goal • Spending target • Financial incentives

35. Issues - Technical • No Usage Data • Decisions without most essential data • Limited Usage Data • Decisions without information on seasonality • Audit Models • Data entry sometimes gets in the way of investigating source / causes of usage problems • Note: Data entry on baseline conditions and treatments is essential for program management

36. Issues - Financial • 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 Target • Do they furnish flexibility or result in over-investment in some homes and under-investment in others?

37. 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

38. Conclusions • Insulation / Air Sealing / Duct Sealing – In Northeast and Midwest, well-designed and implemented programs are big energy savers • Electric Baseload – Refrigerators and cfls save lots of energy, particularly if highest using households are targeted • Energy Education Potential – Behavioral changes have the potential to achieve large energy savings, but we have not seen any programs with significant measured energy savings

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