Abstract
Based on interviews of users’ experience with current smart-meter technologies the authors propose, implement and evaluate a user-centered design of an energy-use information system that assists private households in making efficient energy consumption decisions. Instead of providing disaggregated data, the envisioned system automatically calculates the monetary savings from replacing an appliance or by changing the operational behavior of an appliance. The information provided is personalized with respect to appliance use and also comprises information from external databases. A prototype is implemented and evaluated in a use case with white goods household appliances. The study concludes with directions for further interactivity improvements and research into the structures of an openly shared appliance database.





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Notes
Specific heat capacity of water (\(4186\, \mathrm{J/(kg~deg)}\) ) \(\cdot\) temperature difference (\(60\, \mathrm{deg} - 40\, \mathrm{deg} = 20 \,\mathrm{deg}\)) \(\cdot\) water mass (\(10 \,\mathrm{l} \approx 10\, \mathrm{kg }\)) \(\cdot\) Wh per joule (\(1/3600\, \mathrm{(Wh)/J}\)).
References
Abrahamse W, Steg L, Vlek C, Rothengatter T (2005) A review of intervention studies aimed at household energy conservation. J Environ Psychol 25(3):273–291
Abrahamse W, Steg L, Vlek C, Rothengatter T (2007) The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. J Environ Psychol 27(4):265–276
AG Energiebilanzen (2014) Auswertungstabellen zur Energiebilanz für die Bundesrepublik Deutschland 1990 bis 2011. http://www.ag-energiebilanzen.de/index.php?article_id=10. Accessed 21 July 2016
Allcott H, Mullainathan S (2010) Behavior and energy policy. Science 327(5970):1204–1205
Anderson W, White V (2009) Exploring consumer preferences for home energy display functionality. Tech. rep., Centre for Sustainable Energy, Report to the Energy Savings Trust
Baskerville R, Pries-Heje J (2010) Explanatory design theory. Bus Inf Syst Eng 2(5):271–282. doi:10.1007/s12599-010-0118-4
Benders RM, Kok R, Moll HC, Wiersma G, Noorman KJ (2006) New approaches for household energy conservation in search of personal household energy budgets and energy reduction options. Energy Policy 34(18):3612–3622
Bhattacherjee A, Sanford C (2006) Influence processes for information technology acceptance: an elaboration likelihood model. MIS Q 30(4):805–825
Brocke J, Loos P, Seidel S, Watson RT (2013) Green IS. Bus Inf Syst Eng 5(5):295–297. doi:10.1007/s12599-013-0288-y
Corbin J, Strauss A (2008) Basics of qualitative research: techniques and procedures for developing grounded theory. Sage, Newbury Park
Costanza E, Ramchurn SD, Jennings NR (2012) Understanding domestic energy consumption through interactive visualisation. In: Proceedings of the 2012 ACM conference on ubiquitous computing—ubicomp ’12. ACM Press, New York, p 216. doi:10.1145/2370216.2370251
Darby S (2008) Energy feedback in buildings: improving the infrastructure for demand reduction. Build Res Inf 36(5):499–508. doi:10.1080/09613210802028428
Darby S (2010) Smart metering: what potential for householder engagement? Build Res Inf 38(5):442–457
Darby S, Anderson W, White V (2011) Large-scale testing of new technology: some lessons from the UK smart metering and feedback trials. In: European council for an energy-efficient economy, Summer Study, pp 1–7
Dietz T, Gardner GT, Gilligan J, Stern PC, Vandenbergh MP (2009) Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc Natl Acad Sci 106(44):18,452–18,456
Eekels J, Roozenburg N (1991) A methodological comparison of the structures of scientific research and engineering design: their similarities and differences. Design Stud 12(4):197–203. doi:10.1016/0142-694X(91)90031-Q
Ehrhardt-Martinez K, Donnelly KA, Laitner S (2010) Advanced metering initiatives and residential feedback programs: a meta-review for household electricity-saving opportunities. In: American Council for an Energy-Efficient Economy, Washington DC
European Commission (2006) Action plan for energy efficiency: raising the potential. COM(2006)545 final
Fischer C (2008) Feedback on household electricity consumption: a tool for saving energy? Energy Effic 1(1):79–104
Fitzpatrick G, Smith G (2009) Technology-enabled feedback on domestic energy consumption: articulating a set of design concerns. IEEE Pervasive Comput 8(1):37–44. doi:10.1109/MPRV.2009.17
Frieden BJ, Baker K (1983) The market needs help: the disappointing record of home energy conservation. J Policy Anal Manag 2(3):432–448
Goes PB (2014) Editor’s comments: design science research in top information systems journals. MIS Q 38(1):iii–viii. http://dl.acm.org/citation.cfm?id=2600518.2600519. Accessed 27 May 2017
Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–356. http://dl.acm.org/citation.cfm?id=2535658.2535660
Gregor S, Jones D (2007) The anatomy of a design theory. J Assoc Inf Syst 8(5):312–335
Grønhøj A, Thøgersen J (2011) Feedback on household electricity consumption: learning and social influence processes. Int J Consum Stud 35(2):138–145
Gutberlet KL (2008) Energieeffizienz im Haushalt. GFK-Tagung, Nuremberg. http://www.gfk-verein.org/veranstaltungen/gfk-tagung-2008/vortrag-2. Accessed 4 July 2008
Han Q, Nieuwenhijsen I, de Vries B, Blokhuis E, Schaefer W (2013) Intervention strategy to stimulate energy-saving behavior of local residents. Energy Policy 52:706–715
Hargreaves T, Nye M, Burgess J (2013) Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term. Energy Policy 52:126–134. doi:10.1016/j.enpol.2012.03.027
Hart G (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891. doi:10.1109/5.192069
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105
Honebein PC, Cammarano RF, Donnelly KA (2009) Will smart meters ripen or rot? Five first principles for embracing customers as co-creators of value. Electric J 22(5):39–44
Hopf K, Sodenkamp M, Kozlovkiy I, Staake T (2016) Feature extraction and filtering for household classification based on smart electricity meter data. Comput Sci Res Dev 31(3):141–148
ISO (2010) ISO 9241–210:2010: Ergonomics of human-system interaction—Part 210: human-centred design for interactive systems. Tech. rep., International Organization for Standardization, Geneva
Kamb ML, Rhodes F, Hoxworth T, Rogers J, Lentz A, Kent C, MacGowen R, Peterman TA (1998) What about money? Effect of small monetary incentives on enrollment, retention, and motivation to change behaviour in an HIV/STD prevention counselling intervention. The Project RESPECT Study Group. Sex Transm Infect 74(4):253–255
Kuckart U, Rädiker S, Rheingans-Heintze A (2006) Umweltbewusstsein in Deutschland 2006—Ergebnisse einer repräsentativen Bevölkerungsumfrage. Tech. rep., Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (BMU)
Langgassner W (2001) Energieeffizienz elektrischer Antriebe in Haushaltsgeräten. E und M, Energie-und-Management-Verl.-Ges, Herrsching
Liang J, Ng SKK, Kendall G, Cheng JWM (2010) Load signature study part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569. doi:10.1109/TPWRD.2009.2033800
Loock CM, Staake T, Thiesse F (2013) Motivating energy-efficient behavior with green is: an investigation of goal setting and the role of defaults. MIS Q 37(4):1313–1332
Lossin F, Loder A, Staake T (2016) Energy informatics for behavioral change. Comput Sci Res Dev 31(3):149–155. doi:10.1007/s00450-014-0295-3
Mattle P, Aigner M, Schmautzer E, Fickert L (2011) Smart Plug—Konzept für ein intelligentes Lastmanagementsystem für Einzelverbraucher. In: Energieversorgung 2011: Märkte um des Marktes Willen?, TU Wien. Internationale Energiewirtschaftstagung. Institut für Energiesysteme und Elektrische Antriebe, Vienna, pp 96–97, 7
McMakin AH, Malone EL, Lundgren RE (2002) Motivating residents to conserve energy without financial incentives. Environ Behav 34(6):848–863
Nilsson A, Bergstad CJ, Thuvander L, Andersson D, Andersson K, Meiling P (2014) Effects of continuous feedback on households electricity consumption: potentials and barriers. Appl Energy 122:17–23
Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77
Petersen JE, Shunturov V, Janda K, Platt G, Weinberger K (2007) Dormitory residents reduce electricity consumption when exposed to real-time visual feedback and incentives. Int J Sustain Higher Educ 8(1):16–33. doi:10.1108/14676370710717562
Poortinga W, Steg L, Vlek C, Wiersma G (2003) Household preferences for energy-saving measures: a conjoint analysis. J Econ Psychol 24(1):49–64
Pullinger M, Lovell H, Webb J (2014) Influencing household energy practices: a critical review of UK smart metering standards and commercial feedback devices. Technol Anal Strateg Manag 26(10):1144–1162
Ritchie JRB, McDougall GHG (1985) Designing and marketing consumer energy conservation policies and programs: implications from a decade of research. J Public Policy Mark 4(1):14–32
Rüdenauer I, Eberle U, Grieß hammer R (2006) Ökobilanz und Lebenszykluskostenrechnung Wäschewaschen. Tech. rep., Öko-Institut e.V., Freiburg/Hamburg. http://www.oeko.de/oekodoc/289/2006-008-de.pdf. Accessed 27 May 2017
Schwartz T, Denef S, Stevens G, Ramirez L, Wulf V (2013) Cultivating energy literacy. In: Proceedings of the SIGCHI conference on human factors in computing systems—CHI ’13. ACM Press, New York, p 1193. doi:10.1145/2470654.2466154
Simmhan Y, Aman S, Cao B, Giakkoupis M, Kumbhare A, Zhou Q, Paul D, Fern C, Sharma A, Prasanna V (2011) An informatics approach to demand response optimization in smart grids. Nat Gas 31:60
Steg L (2008) Promoting household energy conservation. Energy Policy 36(12):4449–4453
Strengers Y (2011) Designing eco-feedback systems for everyday life. In: Proceedings of the 2011 annual conference on human factors in computing systems—CHI ’11. ACM Press, New York, pp 2135–2144. doi:10.1145/1978942.1979252
Thuvander L, Meiling P, Andersson K (2012) Energivisualisering via display: Förändras beteendet när hyresgästerna har möjlighet att följa sin elförbrukning? Tech. rep., Chalmers University of Technology, Göteborg
Umweltbundesamt (2012) Ausstattung privater Haushalte mit langlebigen Gebrauchsgütern. Tech. rep., Daten zur Umwelt. http://www.umweltbundesamt-daten-zur-umwelt.de/umweltdaten/public/theme.do?nodeIdent=3535. Accessed 15 Mar 2013
Urquhart C, Lehmann H, Myers MD (2009) Putting the theory back into grounded theory: guidelines for grounded theory studies in information systems. Inf Syst J 20(4):357–381. doi:10.1111/j.1365-2575.2009.00328.x
Van Dam S, Bakker C, Van Hal J (2010) Home energy monitors: impact over the medium-term. Build Res Inf 38(5):458–469
Van Dam S, Bakker C, Van Hal J (2012) Insights into the design, use and implementation of home energy management systems. J Design Res 10(1–2):86–101
Vassileva I, Odlare M, Wallin F, Dahlquist E (2012) The impact of consumers feedback preferences on domestic electricity consumption. Appl Energy 93:575–582
Vassileva I, Dahlquist E, Wallin F, Campillo J (2013) Energy consumption feedback devices impact evaluation on domestic energy use. Appl Energy 106:314–320
Wallenborn G, Orsini M, Vanhaverbeke J (2011) Household appropriation of electricity monitors. Int J Consum Stud 35(2):146–152. doi:10.1111/j.1470-6431.2010.00985.x
Walls JG, Widmeyer GR, El Sawy OA (1992) Building an information system design theory for vigilant EIS. Inf Syst Res 3(1):36–59. doi:10.1287/isre.3.1.36
Weiss M (2010) emeter: Stromverbrauchsfeedback auf basis eines pervasive energy monitoring systems, Doktoranden-Workshop Energieinformatik 2010. https://www.vs.inf.ethz.ch/publ/papers/weismark-emeter-2010.pdf. Accessed 16 July 2016
Weiss M, Staake T, Mattern F, Fleisch E (2012) PowerPedia: changing energy usage with the help of a community-based smartphone application. Pers Ubiquitous Comput 16(6):655–664
White AL (1993) Accounting for pollution prevention. EPA J 19(3):23–25
Zhou K, Yang S (2016) Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew Sustain Energy Rev 56:810–819
Zufferey D, Gisler C, Khaled OA, Hennebert J (2012) Machine learning approaches for electric appliance classification. 11th International conference on information science, signal processing and their applications (ISSPA). IEEE, Montreal, pp 740–745
Acknowledgements
The authors would like to express their gratitude to Michael Schilling and Henning Quellenberg for their research assistance during the development of this study.
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Accepted after three revisions by Prof. Dr. Jarke.
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Dalén, A., Krämer, J. Towards a User-Centered Feedback Design for Smart Meter Interfaces to Support Efficient Energy-Use Choices. Bus Inf Syst Eng 59, 361–373 (2017). https://doi.org/10.1007/s12599-017-0489-x
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DOI: https://doi.org/10.1007/s12599-017-0489-x