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Discriminant effects of consumer electronics use-phase attributes on household energy prediction

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  • Raihanian Mashhadi, Ardeshir
  • Behdad, Sara

Abstract

The aim of this study is to provide a better understanding of the heterogeneities in user-product relationships and their consequences regarding the household energy predictions. Several supervised and unsupervised machine learning algorithms have been applied to a comprehensive data set of residential energy consumptions collected by the US Energy Information Association. The results of the analyses reveal that, while the heterogeneities in the use-phase of consumer electronics could skew their environmental assessment results, they do not possess the same discriminant influences on the household electricity consumption compared to certain socio-demographics or usage of home appliances. Various cross-comparisons among product features and use-phase behaviors have been made and the most important predictors of the residential electricity consumption based on the data have been introduced. Product-level and user-level discussions on the findings have also been provided.

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  • Raihanian Mashhadi, Ardeshir & Behdad, Sara, 2018. "Discriminant effects of consumer electronics use-phase attributes on household energy prediction," Energy Policy, Elsevier, vol. 118(C), pages 346-355.
  • Handle: RePEc:eee:enepol:v:118:y:2018:i:c:p:346-355
    DOI: 10.1016/j.enpol.2018.03.059
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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Marlyne D. Sahakian & Julia K. Steinberger, 2011. "Energy Reduction Through a Deeper Understanding of Household Consumption," Journal of Industrial Ecology, Yale University, vol. 15(1), pages 31-48, February.
    3. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    4. Arnold Tukker & Maurie J. Cohen & Klaus Hubacek & Oksana Mont, 2010. "The Impacts of Household Consumption and Options for Change," Journal of Industrial Ecology, Yale University, vol. 14(1), pages 13-30, January.
    5. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    6. Jihoon Min & Zeke Hausfather & Qi Feng Lin, 2010. "A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 791-807, October.
    7. Kevin Maréchal, 2008. "An evolutionary perspective on the economics of energy consumption: the crucial role of habits," Working Papers CEB 08-012.RS, ULB -- Universite Libre de Bruxelles.
    8. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    9. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    10. Ekholm, Tommi & Krey, Volker & Pachauri, Shonali & Riahi, Keywan, 2010. "Determinants of household energy consumption in India," Energy Policy, Elsevier, vol. 38(10), pages 5696-5707, October.
    11. Hori, Shiro & Kondo, Kayoko & Nogata, Daisuke & Ben, Han, 2013. "The determinants of household energy-saving behavior: Survey and comparison in five major Asian cities," Energy Policy, Elsevier, vol. 52(C), pages 354-362.
    12. Ek, Kristina & Söderholm, Patrik, 2010. "The devil is in the details: Household electricity saving behavior and the role of information," Energy Policy, Elsevier, vol. 38(3), pages 1578-1587, March.
    13. Paul Teehan & Milind Kandlikar, 2012. "Sources of Variation in Life Cycle Assessments of Desktop Computers," Journal of Industrial Ecology, Yale University, vol. 16(s1), pages 182-194, April.
    14. 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.
    15. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    16. Burgess, Jacquelin & Nye, Michael, 2008. "Re-materialising energy use through transparent monitoring systems," Energy Policy, Elsevier, vol. 36(12), pages 4454-4459, December.
    17. Sekar, Ashok & Williams, Eric & Chen, Roger, 2016. "Heterogeneity in time and energy use of watching television," Energy Policy, Elsevier, vol. 93(C), pages 50-58.
    18. Shelie A. Miller & Stephen Moysey & Benjamin Sharp & Jose Alfaro, 2013. "A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment," Journal of Industrial Ecology, Yale University, vol. 17(3), pages 352-362, June.
    19. Saidur, R. & Masjuki, H.H. & Jamaluddin, M.Y., 2007. "An application of energy and exergy analysis in residential sector of Malaysia," Energy Policy, Elsevier, vol. 35(2), pages 1050-1063, February.
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