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A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption

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  • Tso, Geoffrey K.F.
  • Guan, Jingjing

Abstract

Modeling residential energy consumption survey (RECS) data is a complex socio-technical problem that involves macroeconomics, climate, physical characteristics of housing, household demographics and usage of appliances. A multilevel regression (MR) model is introduced to calculate the magnitude and significance of effects of environment indicators and household features on residential energy consumption (REC). MR helps construct a conceptual framework and organize explanatory variables. The benefit of this approach is that based on stratified sampling schemes, MR extracts area effects from total variations of REC and explains the remaining variations with manifest variables and their interactions. Using the US 2009 RECS micro data consisting of 10,838 unique cases, 26 primary determinants of REC are found to be division groups, housing type, house size, usage of space heating equipment, household size and use of air-conditioning, etc. MR helps to quantify 82% of area effects and 47% of household effects. Proportion of the overall explained variance proportion is 53% compared to <40% using OLS regression models.

Suggested Citation

  • Tso, Geoffrey K.F. & Guan, Jingjing, 2014. "A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption," Energy, Elsevier, vol. 66(C), pages 722-731.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:722-731
    DOI: 10.1016/j.energy.2014.01.056
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    1. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    2. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    3. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    4. 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.
    5. Sailor, David J, 2001. "Relating residential and commercial sector electricity loads to climate—evaluating state level sensitivities and vulnerabilities," Energy, Elsevier, vol. 26(7), pages 645-657.
    6. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    7. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    8. Reid Ewing & Fang Rong, 2008. "The impact of urban form on U.S. residential energy use," Housing Policy Debate, Taylor & Francis Journals, vol. 19(1), pages 1-30, January.
    9. Hekkenberg, M. & Moll, H.C. & Uiterkamp, A.J.M. Schoot, 2009. "Dynamic temperature dependence patterns in future energy demand models in the context of climate change," Energy, Elsevier, vol. 34(11), pages 1797-1806.
    10. Ranjan, Manish & Jain, V.K., 1999. "Modelling of electrical energy consumption in Delhi," Energy, Elsevier, vol. 24(4), pages 351-361.
    11. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    12. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-162, February.
    13. van Ruijven, Bas & de Vries, Bert & van Vuuren, Detlef P. & van der Sluijs, Jeroen P., 2010. "A global model for residential energy use: Uncertainty in calibration to regional data," Energy, Elsevier, vol. 35(1), pages 269-282.
    14. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    15. Sanquist, Thomas F. & Orr, Heather & Shui, Bin & Bittner, Alvah C., 2012. "Lifestyle factors in U.S. residential electricity consumption," Energy Policy, Elsevier, vol. 42(C), pages 354-364.
    16. Bengt Muthén & Tihomir Asparouhov, 2009. "Multilevel regression mixture analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 639-657, June.
    17. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    18. Dixon, Gene & Abdel-Salam, Tarek & Kauffmann, Paul, 2010. "Evaluation of the effectiveness of an energy efficiency program for new home construction in eastern North Carolina," Energy, Elsevier, vol. 35(3), pages 1491-1496.
    19. Wang, Sicong & Wang, Shifeng, 2011. "Spatial interaction models for biomass consumption in the United States," Energy, Elsevier, vol. 36(11), pages 6555-6558.
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