To switch off the coffee-maker ornot: that is the question to beenergy-efficient at work

Diego Casado-MansillaMORElab - DeustoTechUniversity of DeustoAv. de las Universidades, 24,48007 Bilbao (SP)dcasado@deusto.es

Diego Lopez-de-IpinaMORElab - DeustoTechUniversity of DeustoAv. de las Universidades, 24,48007 Bilbao (SP)dipina@deusto.es

Juan Lopez-de-ArmentiaMORElab - DeustoTechUniversity of DeustoAv. de las Universidades, 24,48007 Bilbao (SP)juan.lopezdearmentia@deusto.es

Pablo GaraizarLearning - DeustoTechUniversity of DeustoAv. de las Universidades, 24,48007 Bilbao (SP)garaizar@deusto.es

Copyright is held by the author/owner(s)CHI’14 , April 26–May 1, 2014, Toronto, Canada.DOI string from ACM form confirmation

AbstractThere are some barriers to reduce energy consumption inshared spaces where many people use common electronicdevices (e.g. dilution of responsibility, the trade-offbetween comfort and necessity, absentmindedness, or thelack of support to foster energy-efficiency). The workplaceis a challenging scenario since the economic incentives arenot present. To tackle these issues we have created apersuasive prototype, in the shape of a sharedcoffee-maker, as an instance of what we call ’Eco-awareeveryday things’. Its design rationale is twofold: Firstly, tomake the coffee-maker able to learn its own usagepattern. Secondly, to communicate persuasively inreal-time to users whether it is more efficient to leave theappliance on or off during certain periods of time. Thegoal is that the Eco-aware coffee-maker allies with itsusers assisting them to reduce energy consumption.

Author KeywordsSmart devices; Eco-feedback; Energy-efficiency; Workplace

ACM Classification KeywordsH.5.m [Information interfaces and presentation (e.g.,HCI)]: User Interfaces.

General TermsDesign, Experimentation, Human Factors

IntroductionMethodologies for reducing the overall energyconsumption are a very popular research topic within thearea of sustainability. The HCI and UbiComp communitiesare actively contributing with further insights and studiesto face such a pivotal challenge. To date, most of thisresearch has focused on providing motivations andincentives at individual level to save energy [6]. Gradually,other studies focusing on communities and groups areflourishing (e.g. families [7], flatmates [11], students [5] orworkmates [12]). In these latter scenarios social andcollective awareness strategies play a paramount role forsaving energy resources.

The work-in-progress presented here is focused onworkplace’s environments. Some of the energy-challengesto face in these environments are that workers aregenerally unconcerned about their energy expenditure,usually they remain left out of the understanding ofdevices’ power consumption and the financial incentivesare not present to motivate them. Specifically, thisresearch contributes to reduce the energy wasted by sharedand common appliances (e.g. beamers, coffee-makers,printers, screens, portable fans, kettles, etc.). Theyrepresent more than 15% of the total energy consumed inoffices, and it is expected to rise above 20% in 2020 [1]).

To address this increasing percentage, we find twostrategies that have been applied in the literature: On oneside, a complete technological approach, like environmentautomation where machines are energy efficient and theytake the sustainability oriented decisions by moving peopleaside of their intervention (e.g. device’s auto-powerdown). On the other side, a human behavioural changeapproach, where people bear the full responsibility of theirdecisions (e.g. switch off the devices when they are not in

use). Our proposal is aligned with both trends. It devisesa mixed approach putting both technology (in our case, asmart coffee-maker which is an appliance of shared use)and workers on the same team to overcome the lack ofenergy consumption awareness in the workplace.

The presented work is an extension of our previousresearch [8]. There, it was demonstrated that anintelligent shifting between an appliance’s operatingmodes outperformed, in terms of energy efficiency, thetraditional methods, such as auto-power down or standby.In that work, we opted for applying automationtechniques. In this ongoing work, we explore ahuman-machine team, i.e. whether giving the initiative tousers to decide how to operate the common appliances,but being assisted by them, is a better choice. We expectnot only similar energy savings as found in [8], but also anincrease of workers’ energy awareness.

Design Principles of the Coffee-makerOver the set of shared devices that can be found in aworkplace, we have decided to start exploringcoffee-makers. Bush et al. [3] stated that some models ofcoffee machines can have higher electricity consumptionthan A-class ovens or A++ refrigerators. Indeed, in aprior three-months study which was carried out in theauthors’ laboratory, we discovered that the applianceunder analysis remained on much more time than itshould [8]. The stock of coffee machines in EU isestimated to be 100 million units (whereas a estimation ofcoffee machines in work-offices attains 30 million.),consuming 17000 million of kWh per year [3]. Hence,whatever minimal energy reduction is achieved with thesedevices will have a major impact in terms of energy.

The basis of our design rationale is that energy

conservation in common spaces is not only aboutswitching off the appliances after using them - anargument which is backed by the automationstakeholders [3], but it is also about empowering users tooperate the appliances in an intelligent manner, bearing inmind the real-time Eco-assistance provided by the devices.

Figure 1: The augmentedEco-aware coffee maker. On thebackground, the light arch thatinforms to intended users aboutits energy waste.

Figure 2: Different instrumentsand electronic tools used to makethe coffee machine to learn itsown usage pattern and toconnect itself to Internet.

In the presented design, the Eco-aware coffee-maker notonly raises the group attention about their energywastage, but it also supports end-users to decide whetheris more energy-efficient to leave the appliance on or offover a certain period of time. Table 1 provides a summaryof the design rationale and the background for each of thedevice’s features.

An Eco-aware Intelligent DeviceThe coffee machine that has been transformed into anEco-aware device can be appreciated in Figure 1. It is ableto collect, during a learning period, its usage-pattern (i.e.the fluctuations of hot drinks prepared in a workplace ineach of the one-hour time slots in which a work day isdivided). To meet this goal, a current sensor has beenplaced between the appliance and the mains to monitorthe energy flow (Top of Figure 2). This information isprocessed and stored by an Arduino micro-controller(Bottom of Figure 2). Such learning process is applied topredict the appliance’s next-week usage throughtime-series forecasting techniques [2], i.e. to infer themoment when the coffee-maker is likely to be usedthroughout the following week as a function of thenumber of people that previously used it.

Using such inference, the device operation should beadapted to the expected number of coffees to beprepared [8]. Thus, depending on the hour of the day andthe frequency of coffee intakes, the coffee maker must

remain on - high number of coffees -, or in contrast, to beswitched off - low activity. The coffee-maker has thenenough knowledge to aid people in their energy relateddecision-making.

The Coffee-maker Assists You!As earlier mentioned, in [8] we used the acquiredknowledge to make the coffee-maker to autonomouslycommute among any of their operating modes to saveenergy. Now, we use these predictions to interactpersuasively with concerned users in order to raise theirawareness about the necessity of being energy-efficientwhen operating the appliance. The theoretical strategiesthat support this approach are reviewed below.

Persuasive Interaction: Fogg [4] asserted that peoplewho perceive technology as an authority are moresusceptible to persuasion by that technology. TheEco-aware coffee-maker has information that people donot have access to (i.e. the invisible energy consumptionand the foreseeable energy-efficient operation mode ineach moment). Fogg also stated that ’reduction’ and’suggestion’ principles are pivotal to create persuasivetechnologies. Our coffee maker eases the user’sdecision-making (reduction) and it provides real-timeEco-feedback about how to operate the device(suggestion).

Ambient Eco-feedback: The Eco-aware coffee makerfeatures two types of ambient feedback: 1) An ambientlight arch that provides informative visual feedback; 2)machine built-in subtle suggestions that are prompted toinform users about how to operate the deviceappropriately. Firstly, the ambient light arch, placed closeto the coffee maker (Figure 1), gives information aboutthe energy that is being wasted when the device is left onwithout any apparent reason after its usage. At the

beginning of the day the arch is set completely green, butit progressively turns into red as the wasted energyincreases. Secondly, the subtle suggestions are prompted

Figure 3: On the first image anuser approaches its finger toswitch off the device. In thesecond and third picture, it isshown how the device informs tothe intended user to not do it byactivating a set of ultravioletlights LEDs that were embeddedunderneath the water tank.

when the Eco-aware coffee-maker infers that it is moreappropriate to leave the device on, instead of switching itoff in a specific slot of time. The coffee-maker can detectwhen somebody is about to switch the device off after acoffee preparation - we attached a proximity sensorpointing to the start-button to detect such action. Whensuch detection occurs, the Eco-aware appliance suggeststhe corresponding user to not do it depicting the message:’Please, leave me on’. This message was before disguisedand it can only be seen under ultraviolet light. Theprocess can be appreciated in the snapshots of Figure 3.

Social Networks: The Eco-aware coffee-maker isprovided with a public Twitter profile where it publisheseveryday the number of coffees prepared and its energyconsumption, both the effective energy, i.e. the energythat is properly employed to make coffees, and the energythat was wasted, i.e. the unused energy when anyone hasleft the device on without any apparent reasoning. AnEthernet shield attached to the micro-controller allows theInternet connection, so that the coffee-maker is able to’tweet’ by itself.

Figure 4: An informative tweet send by the Eco-awarecoffee-maker.

Teammates: Nass et al.[9] investigated about teamaffiliation between humans and computers. Theyconcluded that interdependence cues were pivotal to

create a team relationship. The Eco-aware coffee-maker ismore than a mere reporter of energy waste, it has beendevised as a collaborative Eco-activists device, i.e. itactively allies with people to take more sustainabledecisions in the common areas where is placed.

Pilot EvaluationWe performed three pilot case studies at a workplacerelated with the usage of coffee-makers. The threeappliances were placed in different labs. Only one of themfeatured the design rationale described before. Thecomparative study lasted one month and was devised toevaluate in a real context the effectiveness of theEco-aware coffee-maker with regard to other designsbased on informative Eco-feedback.The participants’ (N = 20) age range was 20-40 and theywere all males and engineers. The three groups presentedsimilar patterns of coffee intakes: around the noon andafter the lunch.

Phase 1: A pre-trial surveyThe first round of interviews was devoted to get thepersonal information from the participants and to have ageneral understanding about the knowledge that they hadon energy related concepts and their perception of energyconsumption at work. The objective of the experimentwas hidden to the subjects.

Phase 2: Field studyIn this phase, the energy data of the three appliances waslogged. Along the 4 weeks, the means by which each ofthe coffee-makers presented their energy consumption tousers, i.e. Eco-feedback, was changing weekly.

In the 1st week we did not apply any intervention beyondcollecting data. This week was considered as baseline.In the 2nd week, a public Twitter profile was created for

Eco-aware Coffee-maker DesignFeature Rationale Background

IntelligenceTo infer the moment when the coffee-maker The time series [2] assume that past patterns will similarly occur

is likely to be used. in the future, and therefore are predictable.

PersuasivenessIt interacts persuasively with users to raise

Fogg’s Behavior model [4]: Motivation, simplicity and triggers.their energy-awareness.

Ambient awareness Ambient light arch + Hidden messages.Not relying in behaviour change elicited by direct requests,but to be unobtrusive and to present the suggestions subtlety [10].

Social presence Collective awareness with just a daily ’tweet’.A promising channel to link human beingsand intelligent objects for sustainability purposes [11].

Team affiliationAssist users in their decision-making In [9] the participants under interdependence conditions were found

when operating the coffee-maker. to be more cooperative and open to influence from the computer.

Table 1: The rationale followed to design the Eco-aware coffee maker associated with the background which supports each feature.

each coffee-maker. All the subjects implied in the casestudies were notified about this new feature.

Figure 5: The informativeEco-feedback technologiesdevised for the two remaininglaboratories.

In the 3rd and 4th weeks the three coffee machines wereaugmented with different technologies: lab1: The beforepresented Eco-aware coffee-maker, i.e. the device thatfeatures the design rationale of Table 1; lab2: A coffeemachine with a QR-code linking to a web site whereinformative Eco-feedback was given through historicalvisual charts of coffee’s intakes and energy wasted (top ofFigure 5). It allowed its users to easily track theirlaboratory performance; lab3: A coffee-maker with aphysical display attached to it (bottom of Figure 5). Thisinformative Eco-feedback showed to users their previousday’s wasted energy (left side), whilst the energy beingwasted on a given day was shown on the right side.

Phase 3: Post-trial QuestionnaireWhen the intervention finished, we wanted to get theparticipant’s insights about the proposed interactions,their comprehensibility, understanding and usefulness, therelevance that they gave to the intervention and themotivations that have been issued to save energy during

the case study. The questionnaire was divided in twoparts: 1) A semi-structured interview; 2) A self-report ofeighteen items in a 5-point Likert scale.

Results and First ConclusionsThe results obtained are in line with the effectiveness ofcollective Eco-feedback. Two out of the three groupsdecreased their energy consumption (lab1 and lab3). 73%of subjects attributed energy reductions to thecoffee-maker, 82% of them said that the informationreported increased their motivation and 76% asserted thatthey had reflected with work-colleagues about the energyupdates.The group that jointly collaborated with the ”Eco-awarecoffee maker” was found to be the most efficient inenergy terms - so, even when is left to users the initiativeof deciding how to operate the common appliances, theefficiency remains as was the case with automation [8].Even more promising for our ongoing work, it is to findthat the 80% of this group asserted to have changed theirway of using the coffee-maker in only one month - i.e.probable signs of a general increase of awareness.

Likewise, the 76% of this group attributedinterdependence cues to the coffee-maker and showed tobe more open to the influence from the suggestionscoming from such Eco-aware thing. These findingsevidence that automation techniques make impossible forusers to correlate their actions with the energy associatedto them. Also, it was observed that simple informativeEco-feedback is not always enough to know in real-timewhether the action that the people in common spaces areperforming is ”environmentally adequate” or not.

Future WorkWe are now conducting a more thorough experiment thanthe presented in the pilot: More research groups are beingincluded, and thus more participants. We have alsoextended the length of the experiment up to 6 months.We are performing personalised interaction to research itseffectiveness with respect to collective awareness as well.Finally, we are including more persuasive interactions thanthe presented in this work-in-progress - both throughsocial networks and physically embedded within thedevices.

AcknowledgementsThe authors are very grateful to the University of Deustoand also to the project Future Internet II (IE11-316)supported by the Basque Government.

References[1] CarbonTrust: Employee Awareness and Office Energy

Efficiency [Online] Accessed on 16/09/2013.http://tiny.cc/o5p5tw.

[2] Box, G. E. P., and Jenkins, G. Time Series Analysis,Forecasting and Control. Holden-Day, 1990.

[3] Bush, E., Nipkow, J., Josephy, B., Heutling, S., andGriesshammer, R. Strategies to enhance energy

efficiency of coffee machines. In Proc. of EEDAL’09.1300–1307.

[4] Fogg, B. A Behavior model for Persuasive Design. InProc. of PERSUASIVE’09. 40:1–40:7.

[5] Foster, D., Linehan, C., Schoonheyt, M., andLawson, S. W. Cool and the gang: design insightsfor engaging student energy interventions. In Proc.of CHI’13 Extended Abstracts. 1479–1484.

[6] Froehlich, J., Findlater, L., and Landay, J. TheDesign of Eco-feedback Technology. In Proc. ofCHI’10, 1999–2008.

[7] Horn, M. S., Davis, P., Hubbard, A. K., Keifert, D.,Leong, Z. A., and Olson, I. C. Learning sustainability:families, learning, and next-generation eco-feedbacktechnology. In Proc. of IDC’11, 161–164.

[8] Lopez-de Armentia, J., Casado-Mansilla, D., andLopez-de Ipina, D. Reducing Energy Waste throughEco-aware Everyday Things. Mobile InformationSystems Pre-press (2013). 10.3233/MIS-130172.

[9] Nass, C., Fogg, B.J., and Moon, Y. Can computersbe teammates? Int. J. Hum.-Comput. Stud. 45, 6(Dec. 1996), 669–678.

[10] Schmidt, A., Gellersen, H.-W., and Beigl, M.Matching information and ambient media. InCoBuild. Springer, 1999, 140–149.

[11] Thieme, A., Comber, R., Miebach, J. , Weeden, J. ,Kraemer, N. , Lawson, S., and Olivier, P. . We’ve binwatching you”: Designing for reflection and socialpersuasion to promote sustainable lifestyles. In Proc.of CHI’12. ACM, 2337–2346.

[12] Yun, R., Scupelli, P., Aziz, A., and Loftness, V.Sustainability in the workplace: Nine Interventiontechniques for behavior change. In Proc. ofPERSUASIVE’13. Springer-Verlag, 253–265.