Creating an in-home display: Experimental evidence and guidelines for design

Applied Energy

Volumn 108, Issue , 2013, Pages 448-458

  Krishnamurti, Tamar ^a   Davis, Alexander L ^a   Wong Parodi, Gabrielle ^a   Wang, Jack ^a   Canfield, Casey ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Consumer preferences; Electricity feedback; In home displays; Learning

Indexed keywords

DIGITAL DEVICES; ELECTRICITY;

CONSUMER PREFERENCES; CUSTOMER PREFERENCES; EXPERIMENTAL EVIDENCE; FEED BACK INFORMATION; INFORMATION PROVISION; LEARNING; PERSONALIZED FEEDBACK; RESIDENTIAL CONSUMERS;

DESIGN;

COMPUTER SIMULATION; DESIGN; ELECTRICITY SUPPLY; EXPERIMENTAL STUDY; GUIDELINE; HOUSEHOLD ENERGY; LEARNING; REAL TIME; RESIDENTIAL ENERGY;

EID: 84877884316     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.03.048     Document Type: Article

References (48)

1. Kempton W., Layne L.L. The consumer's energy analysis environment. Energy Policy 1994, 22(10):857-866.
2. Darby S. Smart metering: what potential for householder engagement?. Build Res Inform 2010, 38(5):442-457.
3. Keil F.C. Folkscience: coarse interpretations of a complex reality. Trends Cogn Sci 2003, 7(8):368-373.
4. Kempton W., Montgomery L. Folk quantification of energy. Energy 1982, 7(10):817-827.
5. Kempton W. Two theories of home heat control. Cogn Sci 1986, 10(1):75-90.
6. Rozenblit L., Keil F. The misunderstood limits of folk science: an illusion of explanatory depth. Cogn Sci 2002, 26(5):521-562.
7. Wood G., Newborough M. Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design. Energy Build 2003, 35(8):821-841.
8. Krishnamurti T., Schwartz D., Davis A., Fischhoff B., Bruine de Bruin W., Lave L., et al. Preparing for smart grid technologies: a behavioral decision research approach to understanding consumer expectations about smart meters. Energy Policy 2012, 41:790-797.
9. Seligman C., Darley J.M., Becker L.J. Behavioral approaches to residential energy conservation. Energy Build 1978, 1(3):325-337.
10. Seligman C., Becker L.S., Darley J.M. Encouraging residential energy conservation through feedback. Adv Environ Psychol 1981, 3:93-113.
11. Dobson JK, Griffin JDA. Conservation effect of immediate electricity cost. Feedback on residential consumption behavior. In: Proceedings of the 7th ACEEE summer study on energy efficiency in buildings; 1992. p. 10.33-35.
12. Davis A, Krishnamurti T, Fischhoff B, Bruine de Bruin W. Setting a standard for electricity field studies. Unpublished manuscript; 2012.
13. McClelland L., Cook S.W. Energy conservation effects of continuous in-home feedback in all-electric homes. J Environ Syst 1979, 9(2):169-173.
14. Yun T.J. Investigating the impact of a minimalist in-home energy consumption display. Proceedings of the 27th international conference extended abstracts on Human factors in computing systems 2009, 4417-4422. ACM.
15. Abrahamse W., Steg L., Vlek C., Rothengatter T. A review of intervention studies aimed at household energy conservation. J Environ Psychol 2005, 25(3):273-291.
16. Carroll E, Hatton E, Brown M. Residential energy use behavior change pilot. Technical report, Franklin energy; 2009.
17. Darby S. The effectiveness of feedback on energy consumption. A Rev DEFRA Lit Meter, Bill Direct Displays 2006, 486.
18. Ehrhardt-Martinez K, Donnelly KA, Laitner S. Advanced metering initiatives and residential feedback programs: a meta-review for household electricity-saving opportunities. Technical report, American Council for an Energy-Efficient Economy; 2010.
19. Faruqui A., Sergici S., Sharif A. The impact of informational feedback on energy consumption - a survey of the experimental evidence. Energy 2010, 35(4):1598-1608.
20. Anderson W, White V. Exploring consumer preferences for home energy display functionality. Technical report; 2009.
21. Wilson T.D., Gilbert D.T. Affective forecasting. Adv Exp Soc Psychol 2003, 35:345-411.
22. Loewenstein G., Lerner J.S. The role of affect in decision making 2003, Oxford University Press, USA, p. 619-642.
23. Steg L., Vlek C. Encouraging pro-environmental behaviour: an integrative review and research agenda. J Environ Psychol 2009, 29(3):309-317.
24. Wilhite H., Ling R. Measured energy savings from a more informative energy bill. Energy Build 1995, 22(2):145-155.
25. Lutzenhiser Loren Social and behavioral aspects of energy use. Ann Rev Energy Environ 1993, 18(1):247-289.
26. Vassileva I., Odlare M., Wallin F., Dahlquist E. The impact of consumers' feedback preferences on domestic electricity consumption. Appl Energy 2012, 575-582.
27. Hargreaves T., Nye M., Burgess J. Making energy visible: a qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy 2010, 38(10):6111-6119.
28. Langevin J., Gurian P.L., Wen J. Reducing energy consumption in low income public housing: interviewing residents about energy behaviors. Appl Energy 2012.
29. Egan C. Graphical displays and comparative energy information: what do people understand and prefer. In: Summer study of the European council for an energy efficient economy; 1999. p. 2-12.
30. Wilhite HA, Hoivik A, Olsen JG. Advances in the use of consumption feedback information in energy billing: the experiences of a Norwegian energy utility. In: Proceedings, European council for an energy-efficient economy; 1999.
31. Roberts S, Humphries H, Hyldon V. Consumer preferences for improving energy consumption feedback. Technical report, Ofgem, Centre for Sustainable Energy; 2004.
32. Wood G., Newborough M. Energy-use information transfer for intelligent homes: enabling energy conservation with central and local displays. Energy Build 2007, 39(4):495-503.
33. Fischer C. Feedback on household electricity consumption: a tool for saving energy?. Energy Efficiency 2008, 1(1):79-104.
34. Roberts S, Baker W. Towards effective energy information. improving consumer feedback on energy consumption. Technical report, Ofgem, Centre for Sustainable Energy; 2003.
35. Neenan B, Robinson J, Boisvert RN. Residential electricity use feedback: a research synthesis and economic framework. Technical report, Electric Power Research Institute; 2009.
36. Karjalainen S. Consumer preferences for feedback on household electricity consumption. Energy Build 2011, 43(2):458-467.
37. Ueno T., Sano F., Saeki O., Tsuji K. Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Appl Energy 2006, 83(2):166-183.
38. Allen D, Janda K. The effects of household characteristics and energy use consciousness on the effectiveness of real-time energy use feedback: a pilot study. In: Proceedings of the ACEEE summer study on energy efficiency in buildings; 2006. p. 7.1-12.
39. Paetz AG, Becker B, Fichtner W, Schmeck H. Shifting electricity demand with smart home technologies-an experimental study on user acceptance. In: 30th USAEE/IAEE North American conference online proceedings; 2011.
40. Paolacci G., Chandler J., Ipeirotis P.G. Running experiments on Amazon Mechanical Turk. Judgment Decis Mak 2010, 5(5):411-419.
41. Horton J.J., Rand D.G., Zeckhauser R.J. The online laboratory: conducting experiments in a real labor market. Exp Econ 2011, 14(3):399-425.
42. Mason W., Watts D.J. Financial incentives and the performance of crowds. ACM SIGKDD Explor Newslett 2010, 11(2):100-108.
43. Buhrmester M., Kwang T., Gosling S.D. Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality, data?. Perspect Psychol Sci 2011, 6(1):3-5.
44. DeWaters J.E., Powers S.E. Energy literacy of secondary students in new York state (USA): a measure of knowledge, affect, and behavior. Energy Policy 2011, 39(3):1699-1710.
45. Gelman A., Hill J. Data analysis using regression and multilevel/hierarchical models 2007, Cambridge University Press, New York.
46. Gelman A., Su Y.S., Yajima M., Hill J., Pittau M.G., Kerman J., et al. arm: Data analysis using regression and multilevel/hierarchical models. R package version 2010, 1-3.
47. Cowan N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci 2001, 24(1):87-114.
48. Miller G.A. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 1956, 63(2):81-97.