Measures that Save The Most Energy

Jackie BergerDavid Carroll

ACI New Jersey Home Performance ConferenceMarch 5, 2010

Session Outline

1. Introduction

2. Measuring Energy Savings – Projections

3. Measuring Energy Savings – Billing Data

4. Average Savings by Type of Measure

5. Energy Education Savings Potential

6. Maximizing Measure Savings

7. Conclusions2

Introduction - Perspective

• Evaluator’s Perspective• Based on findings from:

– Program design research– Survey research– In-field research– Energy impacts

3

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

4

MEASURING ENERGY SAVINGSProjections

5

Projected Savings vs. Measured Savings

• Value of projections

• Projection methodology

• Issues with projections

• Comparison of projected savings to measured savings

6

Projections vs. ImpactsData Needs

ProjectionsData

Driven Projections

Impacts

Installed measures No Yes Yes

Pre treatment usage No Yes Yes

Post treatment usage No No Yes

Degree days No No Yes

Comparison group No No Yes7

Projections vs. Impacts

• Basic Projection Methodology– Assumptions

• Measure installation rates

• Measure retention rates

• Pre installation usage

• Measure effectiveness

8

Projections vs. Impacts

• Basic Projection Methodology– Calculation

• Average household saving= Measure Installation Rate *

Measure Retention Rate *

(Pre Installation Usage – Post Installation Usage)

9

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

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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

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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

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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

13

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

14

Projections vs. Impacts

Survey Results

Annual kWh Savings by Hours Used

2.5 hr/day 1.5 hr/day

Bulbs provided (database) 15.4 660 396

Bulbs provided (client reported) 12.0 515 309

Number installed by auditor or client 11.6 497 298

Number not burned out or removed 10.6 455 273

(clients reported that 46% are used at least 1 hour per day)

15

Projections vs. Impacts

Impact Analysis Results

High Use Moderate Use

Projected Actual Projected Actual

kWh Savings per Bulb 76 41 72 26

kWh Savings per Home 1,231 677 878 316

Survey Results – High and Moderate UseMonths After Service Delivery

4-6 7-9 10-12 13-15 16-19

% Burned Out 6% 8% 9% 13% 17%

Source: M. Blasnik and Associates.

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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.

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MEASURING ENERGY SAVINGSBilling Data

18

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

19

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

20

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

21

Measure Savings – Evaluation Findings

kWh Savings Per MeasureOhio EPP1 PPL WRAP1

NJ CP1 CO E$P1

PECO LIURP2High

UseMod Use

BaseloadFull Cost

CFL 41 26 40 47

Refrigerator 926 544 777 532 912 692 949

Freezer 760

Air conditioner 172

1M. Blasnik and Associates.2APPRISE. 22

Measure Savings – Evaluation Findings

$ Cost per kWh Savings By Measure

NJ CP

CFL $0.061

Refrigerator $0.069

Source: M. Blasnik and Associates.23

Measure Savings – Evaluation Findings

CCF Savings Per MeasureNJ CP CO E$P IL WAP OH WAP IA WAP

Heater Replacement

85 114 146 117

Attic insulation

85 110 92 148 65

Air sealing 24 70-108 53

Thermostat 41

Source: M. Blasnik and Associates.24

Measure Savings – Evaluation Findings

$ Cost Per CCF Saved By Measure

NJ CP1 CO E$P1 PECO2

Heater Replacement $1.30 $1.90

Attic insulation $0.60 $0.20 $1.28

Air sealing $1.23

Thermostat $0.19

1M. Blasnik and Associates2APPRISE 25

ENERGY EDUCATIONPotential Savings

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Potential for Education

• Major opportunities

• Potential vs. realization

• Successful models

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Potential Education Savings

Wattage Reduction Number Savings

Electric Measures kWh

Turn off lights 60 4 hrs/day 4 350

Turn off lights at night 60 8 hrs/day 2 350

Reduce central AC 3º All times 250

Reduce TV usage 100 4 hrs/day 2 292

Turn off computer 250 8 hrs/day 1 730

Gas Measures Therms

Turn down water temperature 10° All times 25

Turn down thermostat 2° All times 84

Use cold water for clothes washing Cold wash 4/week 52

Set back temperature at night 4º Night 58AC – 72 to 75 degrees, heating 72 to 70 degrees

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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

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ECW Plug Load StudyPotential Education Savings

Computers

Potential Savings from Full Power Management

% of homesEstimated

Annual Savings

Always on 20% 400

Long idle periods 40% 190

Off when not in use 25% 15

Not used much 15% 2

Average savings 160

30

ECW Plug Load Study Potential Education Savings

Wattage When

Not in UseAnnual kWh Savings if

Unplugged When Not in Use

Mini Stereos 23 200

Older TVs 7 58

Printers 6 43

DVD/VCR Player 7 59

31

ECW Plug Load StudyPotential Education Savings

• Saving Strategies– Power management

– Unplug

– Turn off

– Use timer

– Use power strip

• Assessment– Potential savings

– Motivation

32

Education Impacts

Ohio EPP Unprompted

Agreed to Taken

Turn off lights 54% 16%

Turn off appliances 14% 3%

Use CFLs 10% 5%

Conserve energy 10% 2%

Use double spin on clothes washer 9% 2%

Reduce heating temperature 5% 1%

Line dry clothes 4% 0%

Reduce water heater temperature 3% 1%

Wash clothes in cold water 1% 1%

None 19% 19% 33

Education Impacts

Niagara Mohawk Unprompted

Actions Taken As a Result of:

Workshop VideoIn-Home Education

Turn off lights 43% 40% 33%

Install CFLs 27% 20% 24%

Turn down thermostat 14% 15% 10%

Reduce TV usage 11% 3% 6%

Turn off appliances 11% 9% 9%

Turn down water temperature 10% 12% 10%

Reduce use of AC 9% 3% 6%

Use cold water for clothes washing 9% 5% 6%

Set back temperature at night/when out 5% 4% 2% 34

Behavioral Impacts

35

Reduced use of _____ as a result of participating in the program?

Obs.Mean Electric Savings (kWh)

All Customers 233 854

Electric space heater**yes 105 1150no 128 611

Air conditioneryes 136 947no 97 723

Electric dryeryes 71 995no 162 792

Dehumidifieryes 18 1058no 215 837

Number of lights left on all night*yes 43 1174

no 190 781

Recap

• Projected savings tend to overestimate

• Billing data are critical

• Potential for savings from education

36

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

37

Targeting

Usage (ccf) Spending Savings $ per ccf saved

<1,000 $653 26 ccf $25

1,000-1,400 $836 80 ccf $10

1,400+ $1,043 171 ccf $6

38

Targeting

kWh Savings Per MeasureOhio EPP1 PPL WRAP1

NJ CP1 CO E$P1

PECO LIURP2High

UseMod Use

BaseloadFull Cost

CFL 41 26 40 47

Refrigerator 926 544 777 532 912 692 949

Freezer 760

Air conditioner 172

1M. Blasnik and Associates.2APPRISE. 39

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

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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

41

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

42

Targeting

Usage (ccf) Spending Savings $ per ccf saved

<1,000 $653 26 ccf $25

1,000-1,400 $836 80 ccf $10

1,400+ $1,043 171 ccf $6

43

Maximizing Savings

• Programs that save the most per dollar spent:

– Conduct tests to focus resources and time

– Use models as a guide for action

44

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

45

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

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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

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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?

48

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

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Contact Information

• Jackie Berger, 609-252-8009, jackie-berger@appriseinc.org

• David Carroll, 609-252-8010, david-carroll@appriseinc.org

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