Research to Inform Design of Residential Energy Use ... · Research to Inform Design of Residential Energy Use Behavior Change PilotChange Pilot Conservation Improvement ProgramConservation

Research to Inform Design of Residential Energy Use Behavior

Change PilotChange Pilot

Conservation Improvement ProgramConservation Improvement Program Discussion Hosted by the Minnesota Office

of Energy Security

July 21st, 2009

Ed CarrollMark Brown

Flow of DiscussionFlow of DiscussionWhy Consider Behavior Change?Project Overview and Research ApproachBehavioral Change Interventions OverviewLit t R iLiterature ReviewUtility Experiences with Behavior Change Pilot ProgramsCost Effectiveness and Applicability to MNCost Effectiveness and Applicability to MNKey Lessons Learned – Utility Manager PerspectivesPilot Models to ConsiderQ&A

Why Consider Behavior Change?Valuable tool to help you meet your CIP program goals

Utility experiences show that interventions can lead to significant energy savings:energy savings:

2% to 7% savings for program participants in the first year

Traditional product-based prescriptive and custom incentive programsTraditional product based prescriptive and custom incentive programs may be inadequate and provide diminishing returns:

“We realized we would be unable to meet our energy savings targets for the residential sector with purely a product basedtargets for the residential sector with purely a product-based approach.” – Program Manager, Seattle City Light

“The lighting program we have here is so successful that we are ll i t t f t ti l i th t f It i b freally going to run out of potential in the next few years. It is by far

the program that results in the biggest savings. It is sort of like the linchpin will be gone.” – Program Manager, SMUD

Cost effectiveness as low as 3¢ per kWh in first-year savings

Project OverviewOBJECTIVE: to provide the Minnesota Office of Energy Security (OES) and utilities a solid plan for piloting residential energy use behavior change programs as part of their CIP efforts

GOAL: to help Minnesota utilities better understand how to accelerate energy savings resulting from changes in residential energy-use behavior

ACTIVITIES: (completed Nov. 2008 to Apr. 2009):

Collected data and analysis from available published research

Interviewed experienced program managers, consultants, and researchers

Id tifi d b t ti d l l d f t di dIdentified best practices and lessons learned from studies and pilots aimed at addressing consumer behavior

Developed a report providing recommendations for utility pilot p p p g y pprograms applicable to Minnesota’s residential market

Information ResourcesInterview Respondents Published Research ResourcesInterview Respondents

Utilities/Administrators:• Austin Utilities (K. Lady)• Baltimore Gas & Electric (R. Kiselewich)

Published Research Resources

ACEEEEPRI

Baltimore Gas & Electric (R. Kiselewich)• BC Hydro (A. Korteland)• Connexus Energy, MN (B. Sayler)• City Utilities, MO (C. Shaefer)• Energy Trust of Oregon (K. Youngblood)• Pacific Gas & Electric (J Medvitz)

Precourt Institute for Energy Efficiency (Stanford Univ.)Environmental Change Institute (UK)BEHAVE Program (Europe)• Pacific Gas & Electric (J. Medvitz)

• Silicon Valley Power (L. Brown)• SMUD, Sacramento (A. Crawford)

Consultants/Vendors:

BEHAVE Program (Europe)CIEE (California Institute for Energy and Environment)

J l P bli ti• Comverge/ComEd (K. Papadimitriu)• The Brattle Group (A. Faruqui)• Paragon Consulting (B. Jackson)• Positive Energy (A. Laskey)• Van Denburgh Consulting (E. Van Denburgh)

Journal Publications:• Energy Efficiency• Energy Policy• Journal of Environmental Psychology• Journal of Environmental Systemsg g ( g )

Researchers:• Energy Center of Wisconsin (I. Bensch)• FSEC - Florida (D. Parker)

y

• Org. for Energy and the Environment – the Netherlands (H. van Elburg)

Interventions Overview

Behavior Change TheoryBehavior Change Impact of Feedback

Realize that there is a problem

Behavior ChangeDecision Making

Impact of Feedback

• Identifies cost of behavior or deviation from peers

Habitual Behavior Realize relevance of behavior to problem

R li ibiliti t

from peers

• Indicates the impact of specific behavior changes

• Turning on/off lights• Use of appliances

S tti th th t t

Realize possibilities to influence problem

Weigh motives:• Personal norms

changes

• Setting the thermostat Personal norms• Social norms• Other motives (e.g., comfort)

Evaluate conflicting motiveElectricity:

E bli d t

• Can frame behavior in terms of cost ($) or impact on the environment

Challenges

Evaluate conflicting motive

Take action

• Enabling product• Low-involvement• Intangible• Dissatisfier

• Repetitive prompts help to form new persistent habits

• Low cost priorityhabits

Categories of InterventionsWe see three distinct behavioral change program categories:

MonitorsReportsRatesWe see three distinct behavioral change program categories:

In-home devices and displays providing feedback• Real-time feedback on energy use and costs• Devices interface with utility electric meter or through CT

clips installed at electric panel• Examples: PowerCost Monitor, Kill-A-Watt, TED

Customized, regular feedback delivered to consumers• Processed feedback via mailed reports or online interface• Opportunity to incorporate comparative data/feedback

D i i i / t d i ( t t i )

pp y p p• Examples: Positive Energy, BC Hydro’s Team Power

Smart

Dynamic pricing / rate designs (e.g., smart metering)• Protocols that allow for different rates to be charged

based on time of use• Enabled by advanced metering infrastructure and two• Enabled by advanced metering infrastructure and two-

way communication between the utility and customer

Monitors (Direct Feedback)Pros:

PowerCost Monitorfrom Blue Line Innovations

• Users able to receive real-time feedback from their meter via a mobile monitor.

• Real-time feedback allows users to experiment and see the impact of their behaviorof their behavior

• Multiple utilities have demonstrated the savings achieved by customers using these devices

• NSTAR: 3% annual energy savings in ongoing pilot

Click to Launch Video• Hydro One: 6.5% annual savings in a 500-home pilot• Dominion: 6% saving in non-electric water heat homes

Cons:Opt in nature of programs (e g soliciting customers to install

The Energy Detectivefrom Energy, Inc.

• Opt-in nature of programs (e.g., soliciting customers to install devices) leads to low adoption rates and limited scale

• Low willingness to pay relative to device cost• Significant drop-out rates among participants as the novelty of the

device wears off, monitors are put away, or batteries die• Questions about persistence of savings and cost effectiveness of

the $130+ devices• Data capture reliability and resolution raises concerns

Click to Launch Video #2

• Data capture reliability and resolution raises concerns• Compatibility issues with meter/panel designs and interface

Reports (Indirect Feedback)Pros:Pros:• Opt-out (vs. opt-in) nature allows utilities to design and conduct

rigorous large-scale pilots and implementation for entire populations in desired segmentsP id ti f db k h i t ’ f• Provide comparative feedback, showing a customer’s performance relative to their neighbors; power of social norms

• Customized reports based on housing, demographic, and psychographic factors to maximize appeal

• Can operate with or without in-home devices and AMI• Cost effectiveness of savings achieved:

• 3¢ per kilowatt hour in first year (Positive Energy at SMUD)

Cons:• Will not match the real-time and (unless coupled with AMI-enabled

technology) use-specific feedback that in-home devices providegy) p p• Utilities must be careful in targeting and crafting their messaging in

order to minimize potential negative effects:• Small minority of customers offended by comparative feedback

C t d id t i th i ti• Customer may decide to increase their energy consumption

Rates (Dynamic Pricing)Pros:Pros:• Dynamic pricing provides direct monetary incentives for consumers• Utilities are better able to match prices to energy production and/or

purchase costs• Flexibility in rate design (e.g., time-of-use, real-time, critical-peak).• Solutions typically require in-home displays that provide feedback:

• Real-time and cumulative cost/energy consumption info and associated energy savings impactsassociated energy savings impacts

• Advantage of permanent installation/use

Cons:• Costly infrastructure investment requiring substantial resources to

install meters and develop integrated IT platforms• Programs costs are typically justified by returns from operational

efficiency and capacity (i e peak load) management and savingsefficiency and capacity (i.e., peak load) management and savings• Energy efficiency/conservation savings are typically secondary

benefits and not primary drivers

Literature Review

Literature Review

Major review studies: Key FindingsFeedback leads to energy gysavings:

• Direct: 5 to 15%• Indirect: 0 to 10%

Characteristics of effective feedback:

• Given frequently• Involves interaction• Involves interaction• Involves appliance-specific

information• Given over longer period• Presented in a user-

friendly formatConcern with experimental rigor of studiesrigor of studies

Findings from Literature Review

Covers 5 review studies and

(C. Fischer, 2008)

21 original studies across 10 countriesConcludes that feedback

i l istimulates energy savings –‘usual savings of 5% to 12%’Characteristics of effective feedback:feedback:

• Given frequently• Involves interaction• Involves appliance-specific pp p

information• Given over longer period• Presented in an

understandable and appealingunderstandable and appealing way


Savings from direct feedback – average from 5-15%

(S. Darby, 2006)

Savings from indirect feedback (e.g., billing) - range from 0-10%.

High energy users may respond more than low users to direct feedbackfeedback

Persistence of energy savings created from feedback when individuals develop new habits or invest in efficiency measures

Useful display features include instantaneous usage, expenditure, and historic feedback

Indirect feedback can be most helpful for evaluating heating load andIndirect feedback can be most helpful for evaluating heating load and the impact of investments in insulation/new major appliances

Direct feedback is better for understanding the impact of smaller end-uses and the significance of moment to moment behavior


Reviews thirty eight field studies from 1977 to 2004 aimed at

(Abrahamse et al, 2005)

Reviews thirty-eight field studies from 1977 to 2004 aimed at encouraging households to reduce energy consumption

Identifies that much of the research on energy conservation i t ti h l k d th i t i t l diti (interventions has lacked the appropriate experimental conditions (e.g., significant sample size, appropriate control groups) to validate findings and draw definitive conclusions

The large majority of studies addressing feedback find it to be an effective means to generate energy savings, with more frequent feedback leading to greater effectiveness

Rewards for energy conservation may influence behavior, but the effects are found to be short-lived

Using inter entions in combination is fo nd to ha e an impro ed effectUsing interventions in combination is found to have an improved effect

Utility Experiences with Behavior Change ProgramsChange Programs

Illustrative Case StudiesIllustrative Case StudiesDirect feedback via display devices:• Hydro One (Ontario, Canada)• NSTAR (Massachusetts)• Recent Findings Update:

• Dominion (Virginia)• Seattle City Lighty g• Energy Trust of Oregon

Indirect feedback• Positive Energy/SMUD

• Update on savings validation (Summit Blue)

BC Hydro• BC Hydro

Case Study: Hydro One - PowerCost Monitor Pilot

Study Findings

6.5% aggregate reduction in electricity (kWh) consumptiony ( ) p8% reduction in non-electrically heated homes

5% reduction in non-electric

Pilot Program Methodology

Study period >1 year

heat/hot water homes16% reduction in non-electric heat homes w/ electric hot water

400+ participantsSample across wide variation of climate and geography

1% reduction in electrically heated homes; load “completely overwhelms”

11% of homes have electric heat in area

“income and demographic factorsImpact measured based on historical comparisonPowerCost Monitor (Blue Line Innovations) used by participants

income and demographic factors had no impact on the responsiveness to the monitor”60% of participants felt the monitor Innovations) used by participants made a difference in their homes

Source: Summary: The Impact of Real-Time Feedback on Residential Electricity Consumption: The Hydro One Pilot, March 2006

Case Study: NSTAR - PowerCost Monitor PilotStudy Findings

2.9% savings for customers who used the monitor (~$64/year)


Pilot began May 2008

66%-75% installation rate33% of initial users stopped usingthe monitor during the study period

3,100+ units soldMedia coverage (TV, print) coincided with significant rise in sales

63% of participants indicate behavior change60% noticed savings in their bill

Offering Unit Price Adoption RateDirect install during energy audit Free 95%

Offering previous audit customers free PCM Free 14%PCMRetailOffering previous audit customers free PCM Free 14%

Direct Mail Solicitation/Media Promotion

$9.99 6%$29.99 5%$49 99 0 3%

RetailPrice:~$140

$49.99 0.3%

Source: 2008 BECC Conference Presentation: Power Cost Monitor Pilot, David MacLellan, NSTAR, November 2008

Recent In-Home Display Pilots: Dominion Virginia Power


Study Findings

6% kWh energy savings in homes without electric hot waterg gy

Free PowerCost Monitor, pre-programmed with rateEnrolled 1 000 users from 4 600

19% kWh savings in homes with electric hot waterSavings estimates based on weather-Enrolled 1,000 users from 4,600

solicitations13-month study; began Nov. 2007GoodCents used as vendor to

gnormalized billing analysis comparing historical consumption53% of respondents reported technical difficultiesGoodCents used as vendor to

execute pilot30% response to mailed survey soliciation, with pre-paid return and

i ti f f lti k

technical difficultiesBattery lifeSensor water damage

coupon incentive for free multipack of CFLsIn process of completing post-study survey

Plan to structure full rollout with $25 user payment for the meter; to achieve “skin in the game”Using Blue Line PCM monitors thatsurvey Using Blue Line PCM monitors that have AMI compatibility

Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009

Recent In-Home Display Pilots: Seattle City Light Study Findings

2-3% average electricity savings in comparison with control group


Goal of 33 home energy monitors installed

No significant variation in savings achieved across different monitors evaluatedS h t di i t d ithinstalled

Randomly chosen participantsSingle family homes only

3 types of meters installed

Somewhat disappointed with results to date;

Expected greater savings based on manufacturer claims and3 types of meters installed

PowerCost MonitorThe Energy Detective

on manufacturer claims and previously published studies

Difficulty in convincing customers to participate in the study

Cent-a-Meter8-month test period

Survey company found themselves having to sell hard

Logistics issues for electrical permits, installation scheduling for panelinstallation scheduling for panel devices


Recent In-Home Display Pilots: Energy Trust of Oregon Study Findings

Pil t P M th d l

Study Findings

Preliminary findings indicate “monitors did not have a significant impact on energy usePilot Program Methodology

Over 350 monitors deployed:164 sold via Web site @ $29 99

significant impact on energy use for either cohort”Six month response rates:

57% of Early Adopters164 sold via Web site @ $29.99 to “Early Adopters” (EA)201 installed as part of a Home Energy Review (HER)

57% of Early Adopters55% of HER cohort

66% of EA and 64% of HER using device after 6 months

Savings evaluation involved control groups with random stratified sample with adequate regional and home vintage representation

Two thirds of non-users report monitor no longer functionalLighting, space heating, and clothes dryers most often attributed as

g pSurveys conducted within 3 weeks of installation and at 6 months afterFirst installations in Jan of 2008

dryers most often attributed as savings sourceWhile never significant, point estimates of energy savings were gy ghighest at 3 months, and declined at 6-9 months


Indirect Feedback Programs

Case Study: Positive Energy @ SMUDStudy Findings (Ongoing)


Study Findings (Ongoing)

2% savings achieved on average for treatment group (~250kWh p.a.)g gy

Program launched April 200835,000 customer treatment group(non-targeted)

3¢ per kWh savings cost averageSignificantly higher savings among:

• Higher energy consumers(non-targeted)• 25,000 homes receiving monthly • 10,000 homes receiving quarterly

55 000 t t l

• Greenergy (renewable energy) customers

• Monthly vs. quarterly recipients55,000 customer control groupRandom sampling to create representative populationR t id ‘h ’ h

Indication of correlation of higher savings for lower income population800 of 35,000 decided to opt out

Reports provide a ‘here’s how you compare to your neighbors’ message customized to the home (type, size, location)

<1% of 35,000 opted to set personal goalPositive customer feedback

Customized energy savings tips provided along with report

Few very negative reactions

Source: Interviews with President of Positive Energy and Program Manager at SMUD

Verification Analysis for Impact of Positive Energy at SMUDSummit Blue Consulting - May 2009

“The estimate of annual savings from each of the three methods ranged from 2.1% to 2.2% showing strong robustness of results. The range around each of these estimates is tight, providing good reliability and precision…The strength of these estimates rests on the clean design of the experimentand the very large sample sizes that were used. It is often difficult to accurately assess a program savings of 2% from billing analysis because of the wide range of variability in

t bill b t th l l f thi i t ll d

Source: Impact Evaluation of Positive Energy SMUD Pilot Study, May 26, 2009

customer bills, but the large scale of this experiment allowed for accurate assessment of savings from this program.”

Behavioral Science Research – Normative FeedbackSeminal research published in 2007 by Nolan J M Schultz P WSeminal research published in 2007 by Nolan, J. M.. Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V.Used doorhanger messages to test response to four conservation messages among California residents:messages among California residents:

(1) they could save money by conserving energy(2) they could save the earth’s resources by conserving energy(3) they could be socially responsible citizens by conserving energy(3) they could be socially responsible citizens by conserving energy(4) the majority of their neighbors tried regularly to conserve energy

Only the social norming message produced significant savings

Source: "Normative Social Influence is Underdetected," J.M. Nolan, P.W. Schultz, R. B. Cialdini, N.J. Goldstein, and V. Griskevicius, Personality and Social Psychology Bulletin (July 2008).

Positive Energy – Home Electricity Report Example

Source: Positive Energy





Positive Energy – Home Electricity Report ExampleCustomized TipsCustomized Tips

Driven By:

HousingSi• Size

• Age• Fuel type• Pool etc• Pool, etc.

Consumption• Amount• PatternPattern

Demographics• Income

A• Age• Length of

residenceDIY• DIY

• Green




Case Study: BC Hydro Behavior Change Market Test


Study Findings

Reduction target had significant impact on recruitment success5% h d i ifiPilot Program Methodology

1-Year pilot launched early 2007Recruited employees of BC Hydro’s l t t

5% target had significant freeridership problem10% goal found to be optimalCash rewards more appealing thanlargest customer

Employees encouraged to participate:

• Commit to a given electricity

Cash rewards more appealing than prize draw rewardseNewsletter drove online visitsMore frequent visitors to online t l hi d hi h l t i it• Commit to a given electricity

reduction target• Use online tool to track/compare

consumption

tool achieved higher electricity savingsReported behavior changes

• Turning off lights• Participants received cash rebate for

achieving target (e.g. 5% electricity rebate for achieving

4 Different incentive rewards tested

u g o g ts• Changing laundry habits• Shorter showers• Unplugging chargers• Turning down the thermostat• Turning down the thermostat

Source: BC Hydro

BC Hydro – Team Power Smart

Online tools allow anyone in BC to enroll by committing to use 10% less energy over one year

Track consumptionTrack consumptionCompare consumption to similar householdsVisibility to community rivalry and promotion of “Pride of Province”

M b b fit i l d i l ff d t iti t iMembers benefits include special offers and opportunities to win prizes in drawings and contestsProgram supported by a roster of Team Power Smart Leaders including celebrity athletes and community leaderscelebrity athletes and community leadersExpected results among participants (~4% to 5% total savings):

17% become Achievers – average savings of 21%24% become Savers – average savings of 4%59% become Non-Achievers – no savings on average

Currently 74,000 members (4% of customers) enrolled toward goal of

Source: BC Hydro

Currently 74,000 members (4% of customers) enrolled toward goal of 210,000 by 2010

BC Hydro – Team Power Smart

Source: BC Hydro

Psychographic Segmentation

Targeting “StumblingTargeting Stumbling Proponents” with Team Power Smart

Cross references utility-focused categories (e.g., home heating, appliances, and lighting) with emotive categories:emotive categories:

• Health+Wellness• Food+Drink• Life+Leisure• Family+Friends

G• Home+Garden• Gadgets+Tech.

Uses survey data and demographic/housingdemographic/housing parameters to target customized messages most likely to be received positively to a given audience

Source: BC Hydro

given audience.

Cost Effectivenessand Applicability to MNand Applicability to MN

Examining Program Cost EffectivenessHome Energy Reports – Positive Energy @ SMUD (N=35,000)

250 kWh (~2%) first-year savings in non-targeted householdsFirst year cost of conserved energy (i e assumes no persistence):First-year cost of conserved energy (i.e., assumes no persistence):

3¢ per kWh (<$8 per household per year variable cost)In-Home Direct Feedback – PowerCost Monitor

Device Cost: ~$140 (without installation)Requires utility subsidy of ~$100+ to spur adoption (e.g. NSTAR)Likely savings potential of 3% (NSTAR) to 7% (Hydro One)Likely savings potential of 3% (NSTAR) to 7% (Hydro One)Cost of conserved energy @ $100/household program cost:

1 YearAssumed Savings Persistence Horizon

1 Year'first-year' 2 Years 5 Years 10 Years 20 Years

Savings scenario:3% - 330kWh $0.32 $0.16 $0.07 $0.04 $0.027% - 770 kWh $0.14 $0.07 $0.03 $0.02 $0.01

(cost of conserved energy: $ per kWh)

(Assumes: $100 program cost, 11,000 kWh average consumption, 5% discount rate)

Benchmarking First-Year Costs of Energy Savings

Source: Summit Blue Consulting, 2008

Importance of Opt-In vs. Opt-OutNSTAR’s PCM pilot sought to evaluate customer willingness to pay

Findings indicate the utility would have to subsidize nearly $100 of the device cost in order to reach a significant population (>1%)

Limited participation in opt-in programs has significant implications to achievable program savings:

Even if device programs could yield 10% savings (as per literature), if only 5% participate a utility would be limited to a 0 5% population impact5% participate, a utility would be limited to a 0.5% population impactConversely, a program like Positive Energy, saving only 2% among participants (possibly all customers), could have 4X the population impact

NSTAR Pil t Fi di C t Willi t P

95%FreeDirect install during energy auditAdoption RateUnit PriceOffering

95%FreeDirect install during energy auditAdoption RateUnit PriceOffering

NSTAR Pilot Findings – Customer Willingness to Pay

6%$9.99Direct Mail Solicitation/

Media Promotion5%$29.99

14%FreeOffering previous audit customers free PCM6%$9.99

Direct Mail Solicitation/Media Promotion

5%$29.99

14%FreeOffering previous audit customers free PCM

Media Promotion0.3%$49.99

Media Promotion0.3%$49.99

Source: NSTAR

Regional Energy IntensityIntensity in West North Central States matches national average

Factors that can influence regional differences:Climate – associated HVAC energy useAge distribution of the housing stock – associated appliance and weatherization efficiencyPopulation’s attitude toward conservationAmount of resources going toward EE and conservation programs

Source: Energy Information Administration

Residential Electricity End Use Consumption DriversAverage % of Household kWh by Region

30%

35%

Climate and fuel source

20%

25%

sum

ptio

n .

differences reflect relative share of electricity

consumption by region

15%

20%

Elec

tric

ity C

on

5%

10%

% o

f

0%

U.S. Avg. 16.0% 10.1% 5.0% 26.7% 9.1% 8.8% 7.2% 6.7% 2.5% 7.7%

West North Central 14.7% 8.2% 6.1% 29.2% 7.7% 8.6% 7.0% 8.1% 2.1% 8.3%

Air Conditioning

Space Heating

HVAC Appliances

Kitchen Appliances

Water Heating Lighting Home

ElectronicsLaundry

AppliancesOther

EquipmentOther End

Uses


New England 6.6% 6.6% 4.5% 32.6% 8.0% 13.2% 11.3% 8.6% 4.6% 4.1%

South Atlantic 21.4% 10.4% 4.2% 23.2% 12.4% 6.8% 5.7% 6.1% 2.4% 7.3%

Residential Electricity End Use Consumption DriversAverage Household kWh by Region

3,500

4,000

2,500

3,000

t Hou

rs

1,500

2,000

Ann

ual K

ilow

att

Uniformity exists in usesinvolving frequent behavioral interaction

500

1,000

A

-

U.S. Avg. 1,837 1,159 574 3,065 1,045 1,010 827 769 287 884

West North Central 1,689 942 701 3,356 885 988 805 931 241 954

Air Conditioning

Space Heating

HVAC Appliances

Kitchen Appliances

Water Heating Lighting Home

ElectronicsLaundry

AppliancesOther

EquipmentOther End

Uses


New England 491 491 334 2,423 595 981 840 639 342 305

South Atlantic 3,150 1,531 618 3,415 1,825 1,001 839 898 353 1,075

Evaluation of Miscellaneous Electric Loads (MELs)( )


Residential Electricity Consumptionby End Usey


Average Household Consumption by MEL


MELs in the Context of Total Energy Use


Water Heater Fuel Source by Region

F l Oil90%

100%

Gas

Gas

Gas

Fuel Oil

70%

80%

ds Gas

Gas

Gas

40%

50%

60%

of H

ouse

hold

Electric39%

Electric63%

Electric20%

30%

40%

% o

39% Electric26% Electric

20%

Electric29%

0%

10%

U S W t Mid t S th N th t


U.S. West Midwest South Northeast

Water Heater Fuel Sourceby Population Densityy p y

Fuel Oil

Other90%

100%

Gas Gas

Gas

Fuel Oil

70%

80%

ds Gas

40%

50%

60%

of H

ouse

hold

Electric Electric

Electric63%

20%

30%

40%

% o

35% 35% Electric26%

0%

10%

Citi T S b b R l


Cities Town Suburbs Rural

Average Household Energy Spendingby End Use and Region - (All Fuel Sources)

$2 388

$3,000

useh

old

All Other$739

$1,885$1,634

$1,823 $1,788

$2,388

$2,000

$2,500

ding

per

Hou

A/CWater Heat Water Heat W t H t

Water HeatRefrig.

RefrigRefrig. Refrig.

Refrig.All Other$647 All Other

$642

All Other$596

All Other$635

$1,634

$1,000

$1,500

Ene

rgy

Spe

n

Heating Heating Heating

HeatingA/C

A/C

A/CA/C

Water HeatWater Heat

Water Heat Water HeatRefrig.

Heating$500

,

ge A

nnua

l E

$0Total West Midwest South NortheastA

vera

10%: $190 $160 $180 $180 $2405% $95 $80 $90 $90 $120


5%: $95 $80 $90 $90 $1202%: $38 $33 $36 $36 $28

Key Lessons Learned –Utility Manger PerspectivesUtility Manger Perspectives

Motivation is the essential ingredient

Program Manager Perspectives: Key Lessons Learned

Motivation is the essential ingredient

Upfront customer input is invaluable• “Don’t design a project within your own four walls.”

Taking an iterative approach ensures consistency with goals and avoids technical issues• “Know your goals at the outset.”

A cross functional pilot team helps to ensure success

It is important to be sensitive to customer satisfactionIt is important to be sensitive to customer satisfaction impacts

Leveraging peer utility experience improves likelihoodLeveraging peer utility experience improves likelihood of success

Pre pilot surveys establish a baseline for analysis

Program Manager Perspectives: Key Lessons Learned

Pre-pilot surveys establish a baseline for analysis

Incorporate a control group

Novelty of the feedback will wear off

Meter interface can present barriersp

IHDs can be hampered by low installation rates

S l ti t b ll it d t th tSolution must be well suited to the customer population

“There probably isn’t going to be a silver bullet ”• There probably isn t going to be a silver bullet.

Tailoring messaging to specific segments can ensure messages resonate with your audiencemessages resonate with your audience

Program Models to Consider

Program Models to ConsiderProgram Models

Model 1:In-Home Energy Use

Monitor

Model 2:Indirect/Comparative Feedback on Home

Energy Use

Model 3:Hybrid Approach –

Comparative and Direct Feedback

Participants receive regular

Program Basics

Participants receive a monitor that provides real-

time feedback on home energy use in order to track and experiment with their

Participants receive regular reports in the mail that will compare their energy use with neighbors in similar homes. Targeted energy

i ti ill l b

p gcomparative feedback

reports and energy tips. Participants will be

encouraged to make use of real-time power monitors th t b h da d e pe e t t t e

energy use behavior saving tips will also be communicated.

that can be purchased or borrowed for several

months at a time.

Customer Engagement Method Opt-in Opt-out Opt-out (reports)

Opt-in (in-home device)

Targeted participant household savings(as % of total kWh)

5%(mid of 3% to 7% range)

Valid among self-selected participant population

2%Average in total customer

population; targeted segments would have

significantly higher savings

2%+Average in total customer

population; targeted segments would have

significantly higher savings participant population (e.g., in the 5% to 10% range)

(e.g., in the 5% to 10% range)

Big Advantage Real-time feedback for participants

Cost effective approach with broader reach

Hybrid approach maximizes savings potential

Significantl higher cost per Req ires integration ith Greater comple it /


Big Disadvantage Significantly higher cost per kWh saved

Requires integration with system data

Greater complexity/ resource requirements

Program Considerations: Model 3Points of Emphasis

Program Objective • Give customers the ability to compare energy-use with their neighbors• Provide opportunity for the utilization of in-home monitors, possibly on a temporary basis

Target Customer • Broad reach of the opt-out home energy report across geographic, housing, demographic strataTarget Customer Market • Use data from indirect feedback program to identify customer segments with the greatest

potential to benefit from direct feedback

Program Logistics

• Need internal IT system for report generation or contract third-party services• Detailed data on houses and homeowners may need to be obtained from third-party/proprietary

sources• Consider subsidized purchase for feedback devices or model to provide on a temporary basis

Customer Education

• Utilize energy use reports as a platform for education about conservation ideas and promotion of the direct feedback program

Enhancements • Raise awareness and promote associated devices to aid in customer behavior changes

Trade Ally Plan • Evaluate need for technical/installation assistance for feedback devices

Savings and Goals Assumptions

• Anticipated savings of 2% in indirect feedback population; additional savings from device group• Ongoing measurement is necessary to establish baselines for long-term savings persistence

Marketing and • If a temporary device lending program is ruled out, subsidies for customer device purchases Marketing and Incentive Strategy would be necessary, promoted through the indirect feedback reports

• Evaluate the incorporation of customer goal setting and commitments as a motivator

Quality Control Plan

• Having adequate pilot scale, duration, and measurement systems will ensure accurate cost effectiveness quantification

Program Budget


Program Budget Considerations • Evaluate available internal resources, third-party service costs, and need for device subsidies

Example: Behavior Change Pilot Program Plan - Model 3Process Step Inputs Actions Outputs

Critical Success Factors(Application of LessonsProcess Step Inputs Actions Outputs (Application of Lessons

Learned)

Identify Team/

Available internal resources

Potential

Identify required program pilot team with cross functional (operational, finance, technical, customer service) capabilities to address all aspects of program execution and business case assessment

Project team Project plan Define pilot

program outcome A diverse pilot team helps to Team/ Objectives

Potential implementation partners

business case assessment Define project timeline and specific pilot

learning objectives (e.g., quantify savings potential and $/kWh for program)

Quantify resource and budget requirements

program outcome measures

Pilot program budget

p pensure success

Review work of peer utilities; engage in di l

Determination of t Taking an iterative approach

Prepare for Customer Engagement

Identification of potential program partners (e.g., Positive Energy)

dialog Engage program partners (if

necessary/desired) Develop IT integration plan to enable

generation of home energy use reports Develop list of items on which to collect

customer input

program partner engagement

Identified challenges to report generation

Identified device preferences

Taking an iterative approach to piloting solutions ensures consistency with goals

Leveraging the experience of peer utilities improves chances of success

Validating the functionality of customer input Obtain real-time feedback devices and test

internally

preferences Customer input

objectives

new technology can avoid headaches down the road

Solicit customer engagement Collect feedback from a focus group (or

survey) Collect feedback on key aspects of program

Identified customer concerns with reports

Upfront customer input provides invaluable guidance for successful

Collect Customer Input

Small customer (e.g., focus group) population

Customer input objectives

Collect feedback on key aspects of program marketing and execution:

o Receptivity to comparative feedbacko Desired report information elements,

format/graphicso Attitudes toward conservationo Interest in real-time feedback devices

reports Key themes to

incorporate in customer targeting and messaging

Identified barriers to user acceptance

guidance for successful program design

Ensure the solution is well suited to customer population

Interfacing with meters for in-home devices can


o Interest in device distribution/rentalarrangements

pof device present barriers

Process Step Inputs Actions Outputs Applicable Lessons Learned

Establish desired customer segments on which to d t i i t

Define Parameters f C t

Available data on customer energy use and segmentation parameters:

o Level of

determine program impact Calculate required program sample size (in each

population) to allow for adequate precision/confidence in program outcomes measurement*

Establish a control group of (at least) similar size for comparison that is representative of the

Necessary program treatment and control group size

Identified customer segment representation

Incorporating a control group that representative of the underlying population and sufficiently large allows for the necessary precision andfor Customer

Comparison

o Level of energy use

o Ageo Incomeo Home

size/type/age

for comparison that is representative of the treatment group

Develop customer education plans to maximize awareness and satisfaction

Determine means/parameters to group customer homes for energy use comparisons (e.g., 100 homes of similar size in neighborhood)

representation desired in pilot group

Customer education plan

Program budget

the necessary precision and confidence to draw conclusions about specific sub-segments of the population

Determine program budget

*Note: See Appendix 1 for discussion of sample size determination. Control and treatment groups should be defined to observe impact of indirect feedback. The selection bias of device user population requires historical data comparison to evaluate savings.

Develop

Develop energy use reports to communicate customer energy use in comparison to neighbors and historical consumption

Template for home energy use report

Motivation is the essential ingredientDevelop

Energy Report Content

Customer segmentation data

and historical consumption Develop/obtain comprehensive lists of energy

savings measures to potentially recommend Establish means to select customized energy

savings tips for customers based on known segmentation parameters

gy p Means to determine

customized savings tips to include (may come from program partner)

g Look beyond traditional

customer segmentation models to find messages that resonate with particular groups

Id if l f d i l di / lDevelop Real-Time Feedback Device Distribution Model

Device preferences

Identified barriers to user acceptance of device

Identify plan for device lending/rental program (e.g. distribution through mail, library checkout, etc.)

Purchase adequate number of devices to support pilot

Develop necessary customer education materials to facilitate device lending program

Device lending program resources

Real-time feedback gives users the opportunity to experiment in finding energy saving behaviors


to facilitate device lending program

Process Step Inputs Actions Outputs Lessons Learned

Customer focus

Define survey to capture:o Home characteristics (e.g., appliances)o Demographics

E b h i / tt

Baseline profile of customer h t i ti d

Conduct Pre-Pilot Survey

Customer focus group feedback

Example surveys from past programs and other utilities

o Energy use behaviors/patternso Attitudes toward conservationo History of participation in utility energy

efficiency programs (e.g., rebates, etc.) Select pilot treatment and control groups (likely

random/stratified sample) Collect feedback from customers across treatment

characteristics and attitudes

Confirmation that treatment and control samples represent the underlying population

Pre-pilot surveys can establish baselines for analysis

Collect feedback from customers across treatment, control, and total customer populations

population

Selected treatment population

Resources to support report

Distribute customer education materials describing program/reports

Regularly generate and distribute home energy use reports to treatment group customers

o More frequent feedback has been shown to Pilot program

Execute Pilot Study

support report generation and distribution

Device distribution/ collection model

Resource to field

o More frequent feedback has been shown to lead to greater energy savings

Promote opportunities for participants to obtain real-time feedback devices to aid in their efforts to save energy

Facilitate distribution and collection of real-time feedback devices

gparticipation

Addressed customer concerns

Demand for real-time feedback devices

Motivated and educated participants

Ensure pilot execution allows for measurement of cost effectiveness

customer calls, questions, issues

Customer communications

Assist/respond to customer questions/issues with device installation/operation

Consider offerings customer the opportunity to establish an energy reduction goal

educated participants

Develop survey instruments to evaluate:oPerceptions of home energy use reports/devices Ability to adjust

Collect Participant Feedback

Pilot program participation

oPerceptions of home energy use reports/devicesoImpact on motivationoBehavior changes madeoInvestments madeoParticipation in other utility energy efficiency programs (e.g., rebates/incentives) – Important for savings adjustments/avoid double-counting

Ability to adjust savings for concurrent efficiency program participation

Survey data/feedback on participant experience and

Be sensitive to program’s impact on customer satisfaction


oConservation attitudes Collect feedback from pilot treatment/control groups

satisfaction

Process Step Inputs Actions Outputs Lessons Learned

Energy

Measurement of participant energy savings

Opt-out nature of program allows for

lt t bEvaluate Program Results/Savings Cost Effectiveness

Energy consumption data

Quantification of pilot program costs

Data from participant

Obtain measures of actual consumption over treatment period for treatment, control (if any), and population (sample)

Compare to normalized historical consumption and control group data to determine impact of the feedback intervention on energy conservation

savings Determination of

program cost effectiveness ($ per kWh of savings)

Determination of differences across

results to be more reasonably extended to potential for savings in entire population

Specific customer segments (e.g., higher energy users) are likelyfeedback survey segments (e.g.,

savings for high energy users)

energy users) are likely to see different levels of savings

Conduct ongoing monitoring

Pilot program participation

Execute customer surveys and data collection to determine persistence of energy savings and customer involvement

Data on device use pattern

Data on savings

[Limited data exists on persistence of savings from utility programs]monitoring involvement persistence from utility programs]


Note on Sample Size DeterminationOpt-in device program inherently prohibit simple control group determination due to the self-selected nature of the treatment groupOpt-out programs lend themselves to easier control group definition

Avoids problems that can come from using historical consumption data beyond the need for weather normalization

Economic conditionsMedia messagingIndividual household factors:

Tenant changesOccupancyRenovations

Alternative approaches to evaluation of savingsConfidence interval around the meanConfidence interval around the % change from prior periodLinear regression and differenced linear fixed effects modelsg

Source: Author’s calculations

Note on Sample Size DeterminationThe required sample size for a study aimed at verifying savings performance is a function of several parameters:

Hypothesized magnitude of energy saving to detect (μ0-μ1)Standard deviation of energy consumption across households (σ)Standard deviation of energy consumption across households (σ)Desired confidence (1-α) and power (1-β): tolerance for making a wrong conclusion

Sample size to test the difference in two population meansRule of thumb for 95% Confidence, 80% Power:

2

10

212/12

zz

n 210

16

n

1% 2% 5% 10%

100 kWh 200 kWh 500 kWh 1000 kWh

Hypothesized Annual Energy Savings (to Test)

100 kWh 200 kWh 500 kWh 1000 kWh1000 kWh 1,600 400 64 16

2000 kWh 6,400 1,600 256 64

3000 kWh 14,400 3,600 576 144

4000 kWh 25,600 6,400 1,024 256

Std. Dev. of Annual Energy

Cons mption

Source: Author’s calculations

4000 kWh 25,600 6,400 1,024 256

5000 kWh 40,000 10,000 1,600 400Consumption

Thank you!y

Questions?Ed Carroll: [email protected]

608-310-6910

Mark Brown: [email protected]

Behavior Change Through Rate Design

20

25

30

kWh

Average Customer

Rate D20

25

30

kWh

Average Customer

Rate D20

25

30

kWh

Average Customer

Rate D20

25

30

kWh

Average Customer

20

25

30

kWh

Average Customer

Rate D

Studies have shown that as much as a 6%

i

10

15

20

Cen

ts /

k

Rate C10

15

20

Cen

ts /

k

Rate C10

15

20

Cen

ts /

k

Rate C10

15

20

Cen

ts /

k10

15

20

Cen

ts /

k

Rate C

energy savings can be achieved from inclining block rates that take

0

5

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Rate A

Rate B Existing Flat Rate

0

5

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Rate A


0

5

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Rate A

Rate B

0

5

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 0000

5

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Rate A


advantage of price elasticity in consumer demand.

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month

Avg Percent Change in UsagePrice Elasticity Rate A Rate B Rate C Rate DPrice Elasticity Rate A Rate B Rate C Rate D

Short Run Mean -5.9% -2.2% -1.0% -0.5%Std Dev 2.0% 0.8% 0.3% 0.2%

Long Run Mean -18.4% -6.7% -3.1% -0.7%gStd Dev 6.5% 2.4% 1.1% 0.4%

Inclining Block Rate Bill ImpactsI li i bl k t ld b d i d th t l th hi h t fInclining block rate would be designed so that only the highest users of electricity would see billing increases.

Simulated Distribution of Bill Impacts

10%

20%

30%

l

Tier 1 Cutoff

Original Break-even Point

-20%

-10%

0%

10%

n M

onth

ly B

il

-50%

-40%

-30%

Cha

nge

in

No Price Elasticity

Break-even Point w/Price Elasticity

-70%

-60%

100

200

300

400

500

600

700

800

900

1,00

0

1,10

01,

200

1,30

01,

400

1,50

01,

600

1,70

0

1,80

01,

900

2,00

0

With Price Elasticity

Customer Size (kWh/month)

Documents

Research to Inform Design of Residential Energy Use ... · Research to Inform Design of Residential Energy Use Behavior Change PilotChange Pilot Conservation Improvement ProgramConservation