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Routines and Energy Intensity of Activities in the Smart Home

  • L. Stankovic
  • V. Stankovic
  • J. Liao
  • Charlie Wilson
Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

The instrumental view of smart homes and their users is premised on active management of energy demand contributing to energy system objectives. In this chapter we explore a novel way of using data from smart home technologies (SHTs) to link energy consumption in homes to daily activities. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households’ own lived experience. We set out a novel method for disaggregating a household’s electricity consumption down to the appliance level allowing us to make inferences about the activities occurring in the home in any given time period. We apply this method to analyse the pattern of activities over the course of one month in 10 of the homes participating in the SHT field trial described in Chap.  1. We show how both the energy intensity and temporal routines of different activities vary both within and between households. Our method also clearly reveals the complexities of everyday life at home which shapes the domestication of SHTs.

References

  1. Bertoldi P, Atanasiu B (2006) Residential lighting consumption and saving potential in the enlarged EU. In: Proceedings of 4th international conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL’06), 2006, pp 21–23Google Scholar
  2. De Lauretis S, Ghersi F, Cayla J-M (2016) Time use, lifestyle and energy consumption: lessons from time use and budget data for French households. Paper presented at the energy: expectations and uncertainty, Bergen, Norway, 19–22 June 2016Google Scholar
  3. DECC (2014) Smart metering implementation programme: smart metering equipment technical specifications version 1.58. Department of Energy and Climate Change (DECC), London, UKGoogle Scholar
  4. Gram-Hanssen K (2014) New needs for better understanding of household’s energy consumption—behaviour, lifestyle or practices? Architectural Eng Des Manage 10(1–2):91–107CrossRefGoogle Scholar
  5. Katzeff C, Wangel J (2015) Social practices, households, and design in the smart grid. In: ICT innovations for sustainability. Springer, pp 351–365Google Scholar
  6. Liao J, Elafoudi G, Stankovic L, Stankovic V (2014a) Non-intrusive appliance load monitoring using low-resolution smart meter data. In: IEEE international conference on Smart grid communications (SmartGridComm), IEEE, 2014, pp 535–540Google Scholar
  7. Liao J, Stankovic L, Stankovic V (2014b) Detecting household activity patterns from smart meter data. In: International conference on Intelligent Environments (IE), IEEE, 2014, pp 71–78Google Scholar
  8. Murray D, Liao J, Stankovic L, Stankovic V, Hauxwell-Baldwin R, Wilson C, Coleman M, Kane T, Firth S (2015) A data management platform for personalised real-time energy feedback. Paper presented at the 8th international conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL’15), Luzern, Switzerland, 26–18 August 2015Google Scholar
  9. ONS (2000a) National survey of time use. Office of National Statistics (ONS), London, UKGoogle Scholar
  10. ONS (2000b) Survey on time use: activity coding list. Office of National Statistics (ONS) & Eurostat, London, UKGoogle Scholar
  11. Schwartz T, Stevens G, Jakobi T, Denef S, Ramirez L, Wulf V, Randall D (2014) What people do with consumption feedback: a long-term living lab study of a home energy management system. Interact Comput 27(6):551–576CrossRefGoogle Scholar
  12. Stankovic L, Wilson C, Liao J, Stankovic V, Hauxwell-Baldwin R, Murray D, Coleman M (2015) Understanding domestic appliance use through their linkages to common activities. In: 8th international conference on energy efficiency in domestic appliances and lighting, 2015Google Scholar
  13. Wilson C, Stankovic L, Stankovic V, Liao J, Coleman M, Hauxwell-Baldwin R, Kane T, Hassan T, Firth S (2015) Identifying the time profile of everyday activities in the home using smart meter data. Paper presented at the European Council for an Energy Efficient Economy (ECEEE) summer study, Hyeres, France, 1–6 June 2015Google Scholar
  14. Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans on Consum Electron 57(1)Google Scholar
  15. Zoha A, Gluhak A, Imran MA, Rajasegarar S (2012) Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12(12):16838–16866CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • L. Stankovic
    • 1
  • V. Stankovic
    • 1
  • J. Liao
    • 1
  • Charlie Wilson
    • 1
  1. 1.Tyndall Centre for Climate Change Research, School of Environmental SciencesUniversity of East AngliaNorwichUK

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