IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i4p528-d94481.html
   My bibliography  Save this article

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

Author

Listed:
  • Akito Ozawa

    (Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan)

  • Ryota Furusato

    (Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan)

  • Yoshikuni Yoshida

    (Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan)

Abstract

Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption—as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

Suggested Citation

  • Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:528-:d:94481
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/4/528/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/4/528/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    2. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    3. Thompson, Samuel B., 2011. "Simple formulas for standard errors that cluster by both firm and time," Journal of Financial Economics, Elsevier, vol. 99(1), pages 1-10, January.
    4. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    5. Hutton, R Bruce, et al, 1986. "Effects of Cost-Related Feedback on Consumer Knowledge and Consumption Behavior: A Field Experimental Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(3), pages 327-336, December.
    6. Hendricks, Wallace & Koenker, Roger & Poirier, Dale J., 1979. "Residential demand for electricity : An econometric approach," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 33-57, January.
    7. Buchanan, Kathryn & Russo, Riccardo & Anderson, Ben, 2015. "The question of energy reduction: The problem(s) with feedback," Energy Policy, Elsevier, vol. 77(C), pages 89-96.
    8. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    9. Isamu Matsukawa, 2004. "The Effects of Information on Residential Demand for Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-18.
    10. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    11. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    12. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    13. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    14. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    15. van Houwelingen, Jeannet H & van Raaij, W Fred, 1989. "The Effect of Goal-Setting and Daily Electronic Feedback on In-home Energy Use," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(1), pages 98-105, June.
    16. Riddell, A. G. & Manson, K., 1996. "Parametrisation of domestic load profiles," Applied Energy, Elsevier, vol. 54(3), pages 199-210, July.
    17. Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
    18. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    19. Ian Ayres & Sophie Raseman & Alice Shih, 2009. "Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage," NBER Working Papers 15386, National Bureau of Economic Research, Inc.
    20. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
    21. Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
    22. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    23. Henryson, Jessica & Hakansson, Teresa & Pyrko, Jurek, 2000. "Energy efficiency in buildings through information - Swedish perspective," Energy Policy, Elsevier, vol. 28(3), pages 169-180, March.
    24. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    25. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    26. Shiraki, Hiroto & Nakamura, Shogo & Ashina, Shuichi & Honjo, Keita, 2016. "Estimating the hourly electricity profile of Japanese households – Coupling of engineering and statistical methods," Energy, Elsevier, vol. 114(C), pages 478-491.
    27. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song, Kwonsik & Anderson, Kyle & Lee, SangHyun, 2020. "An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups," Applied Energy, Elsevier, vol. 260(C).
    2. Nyberg, Roy A., 2018. "Using ‘smartness’ to reorganise sectors: Energy infrastructure and information engagement," International Journal of Information Management, Elsevier, vol. 39(C), pages 60-68.
    3. Zhang, Chaoqun & Zha, Donglan & Jiang, Pansong & Wang, Fu & Yang, Guanglei & Salman, Muhammad & Wu, Qing, 2023. "The effect of customized information feedback on individual electricity saving behavior: Evidence from a field experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    4. McAndrew, Ryan & Mulcahy, Rory & Gordon, Ross & Russell-Bennett, Rebekah, 2021. "Household energy efficiency interventions: A systematic literature review," Energy Policy, Elsevier, vol. 150(C).
    5. Anna Claudelin & Ville Uusitalo & Sanna Pekkola & Maija Leino & Suvi Konsti-Laakso, 2017. "The Role of Consumers in the Transition toward Low-Carbon Living," Sustainability, MDPI, vol. 9(6), pages 1-15, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paolo Zangheri & Tiago Serrenho & Paolo Bertoldi, 2019. "Energy Savings from Feedback Systems: A Meta-Studies’ Review," Energies, MDPI, vol. 12(19), pages 1-18, October.
    2. Buckley, Penelope, 2020. "Prices, information and nudges for residential electricity conservation: A meta-analysis," Ecological Economics, Elsevier, vol. 172(C).
    3. Weber, Sylvain & Puddu, Stefano & Pacheco, Diana, 2017. "Move it! How an electric contest motivates households to shift their load profile," Energy Economics, Elsevier, vol. 68(C), pages 255-270.
    4. Sandro Casal & Nives DellaValle & Luigi Mittone & Ivan Soraperra, 2017. "Feedback and efficient behavior," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-21, April.
    5. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    6. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    7. Schleich, Joachim & Faure, Corinne & Klobasa, Marian, 2017. "Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand," Energy Policy, Elsevier, vol. 107(C), pages 225-233.
    8. Gans, Will & Alberini, Anna & Longo, Alberto, 2013. "Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland," Energy Economics, Elsevier, vol. 36(C), pages 729-743.
    9. Delmas, Magali A. & Lessem, Neil, 2014. "Saving power to conserve your reputation? The effectiveness of private versus public information," Journal of Environmental Economics and Management, Elsevier, vol. 67(3), pages 353-370.
    10. Sandro Casal & Nives Della Valle & Luigi Mittone & Ivan Soraperra, 2016. "Feedback and consumption behavior," CEEL Working Papers 1608, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
    11. Saunders, Harry D. & Roy, Joyashree & Azevedo, Inês M.L. & Chakravarty, Debalina & Dasgupta, Shyamasree & De La Rue Du Can, Stephane & Druckman, Angela & Fouquet, Roger & Grubb, Michael & Lin, Boqiang, 2021. "Energy efficiency: what has research delivered in the last 40 years?," LSE Research Online Documents on Economics 114344, London School of Economics and Political Science, LSE Library.
    12. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    13. Matteo Fontana & Massimo Tavoni & Simone Vantini, 2019. "Functional Data Analysis of high-frequency load curves reveals drivers of residential electricity consumption," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.
    14. Giraudet, Louis-Gaëtan, 2020. "Energy efficiency as a credence good: A review of informational barriers to energy savings in the building sector," Energy Economics, Elsevier, vol. 87(C).
    15. Asensio, Omar Isaac & Delmas, Magali A., 2016. "The dynamics of behavior change: Evidence from energy conservation," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 196-212.
    16. Du, Limin & Guo, Jin & Wei, Chu, 2017. "Impact of information feedback on residential electricity demand in China," Resources, Conservation & Recycling, Elsevier, vol. 125(C), pages 324-334.
    17. Camara, N’Famory & Xu, Deyi & Binyet, Emmanuel, 2018. "Enhancing household energy consumption: How should it be done?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 669-681.
    18. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
    19. Louis-Gaëtan Giraudet, 2018. "Energy efficiency as a credence good: A review of informational barriers to building energy savings," Working Papers 2018.07, FAERE - French Association of Environmental and Resource Economists.
    20. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:528-:d:94481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.