Understanding how to effectively reduce personal computer electricity consumption in an office setting Joshua Gluck, Gabrielle Wong-Parodi, Tamar Krishnamurti, Yuvraj Agarwal CEDM 2016 Annual Meeting 1

Computers & monitors represent 40-60% of energy used by office equipment Photo: techinsider.io; Reference: Bray (2006) 2

Offices are occupied ~25% of the week and computers are actively used only a fraction of that time Photo: Gmblog.ragnarokeurope.com; Reference: Bray (2006) 11:00 PM If a computer is on and no one is using it, electricity is being wasted 3

Behavioral interventions are a way to reduce use Automated systems (very effective but…) -“Big brother” concerns -Not cost effective for all Behavioral interventions (nudges may be effective but what really works?) -Hawthorne Effect – the mere knowledge of being watched can affect behavior -Social norms – comparison to others -Individualized feedback information Figures: Opower.com; References: Krishnamurti et al. (2012); Kamilaris et al. (2014); Schwartz et al. (2013); Allcott (2011); Darby (2006) 4

What do we mean by “works?” What do we mean by works? Works for everyone, even for those not particularly interested in saving electricity Works for everyone, even in the absence of incentives (e.g., financial) Works for everyone, even after the invention stops 5

Research questions What do we mean by works? Works for everyone, even for those not particularly interested in saving electricity Works for everyone, even in the absence of incentives (e.g., financial) Works for everyone, even after the invention stops 1.Does seeing social comparison/feedback information lead to greater electricity savings than simple awareness of being monitoring? 2.Do savings continue after the intervention ends? 6

Study design 7

Recruitment and participants Recruitment June administrative staff (mostly Gates) Deception “power quality & network connectivity” -Baseline measurement -Selection bias -Control condition (Hawthorne Effect) Belkin Wemo Insight installed (electricity consumption at 5 second intervals) Lottery to win 1 of 2 $200 Amazon gift cards Participants 67.4% female 42.1 (SD=13.1) average age 8.9 (SD=8.7) average employment tenure 58.7% use laptop PC at work 7.2 average hours working on PC per day (SD=1.7) 8

n=22 n=24 9

n=22 n=24 10

Social NormsHawthorne 11

n=22 n=24 12

n=22 n=24 Energy use calculated as percent change from baseline 13

Surveys and interviews Surveyed all participants -Level of feedback desired - comprehension -Perceived weekly energy use -Feelings towards the s -Number of notices received/opened -Visibility of power meter -Normal energy savings inclination/behavior -Amount of computer use -Knowledge of computer energy use -Demographics Debrief Optional interview (n=10; $20 Amazon gift card; 2 coders with Kappa of.80) -Ways to reduce energy use (most effective ways, potential barriers) -Computer energy use -Explaining responses to notices 14

1. Seeing social comparison/feedback information leads to greater electricity savings than simple awareness of being monitored Social Norm vs. Hawthorne F(1,44)=9.0, p<.01, η 2 p =0.2 15

2. Savings continue after the intervention ends Social Norm phase 2 vs. Social Norm phase 3 F(1,21)=0.0, p=0.9, η 2 p =0.0 16

Exploring what might explain the difference Some survey results -Number of notices opened and times opened had no effect (p>.05) -Social norm condition saw the notifications as more interesting and relevant than the Hawthorne condition (p<.05). (Interesting Hawthorne 3.18, 1.181, Interesting SN 4.22,.943)(Relevance Hawthorne 3.17,.865, Relevance SN 3.85,.895) -Older participants more likely save more electricity in Phase 3 than younger participants (p<.05) Some interview results -Only 1 out of 10 interviewees reported changing their behavior in response to notifications -6 out of 10 interviewees reported being more likely to change behavior with a financial incentive -All Hawthorne interviewees wanted more information “I guess give ideas about what a person could specifically do. Some of this is pretty big picture” – Interview 4 “I mean I don't know anything about energy. I don't know what a watt, what a this is, what a, you know. Sometimes you can make people sort of more excited about stuff and that's to me personally that's what I would want to know. “ – Interview 9 “…but if there were a way to peg specific behaviors or areas or something more specific than total energy use, that would certainly make it more interesting.” – Interview 6 17

Discussion Social Norm (social comparison/information feedback) associated with greater electricity savings than simply being aware of monitoring (Hawthorne) among those: -With no particular interest in saving electricity -Receiving no financial incentives -Receiving no stimuli (the intervention has ended) Social comparison/information feedback, more engaging and memorable Older people may have better strategies for saving electricity Financial incentives may be a way to promote savings 18

Future work Spill-over to other appliances Demand response notifications (not effective) 19

Thank you! Center for Climate and Energy Decision Making (DMUU Solicitation NSF ) Scott Institute Seed Grant Contact information Carnegie Mellon University, 129 BH, Pittsburgh, PA CMU research website 20