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Exploring variance in residential electricity consumption: Household features and building properties

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  • Bartusch, Cajsa
  • Odlare, Monica
  • Wallin, Fredrik
  • Wester, Lars
Abstract
Improved means of controlling electricity consumption plays an important part in boosting energy efficiency in the Swedish power market. Developing policy instruments to that end requires more in-depth statistics on electricity use in the residential sector, among other things. The aim of the study has accordingly been to assess the extent of variance in annual electricity consumption in single-family homes as well as to estimate the impact of household features and building properties in this respect using independent samples t-tests and one-way as well as univariate independent samples analyses of variance. Statistically significant variances associated with geographic area, heating system, number of family members, family composition, year of construction, electric water heater and electric underfloor heating have been established. The overall result of the analyses is nevertheless that variance in residential electricity consumption cannot be fully explained by independent variables related to household and building characteristics alone. As for the methodological approach, the results further suggest that methods for statistical analysis of variance are of considerable value in indentifying key indicators for policy update and development.

Suggested Citation

  • Bartusch, Cajsa & Odlare, Monica & Wallin, Fredrik & Wester, Lars, 2012. "Exploring variance in residential electricity consumption: Household features and building properties," Applied Energy, Elsevier, vol. 92(C), pages 637-643.
  • Handle: RePEc:eee:appene:v:92:y:2012:i:c:p:637-643
    DOI: 10.1016/j.apenergy.2011.04.034
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    References listed on IDEAS

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    1. Baker, Keith J. & Rylatt, R. Mark, 2008. "Improving the prediction of UK domestic energy-demand using annual consumption-data," Applied Energy, Elsevier, vol. 85(6), pages 475-482, June.
    2. 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.
    3. Nilsson, Mats, 2005. "Electric power oligopoly and suspicious minds--a critique of a recently approved merger," Energy Policy, Elsevier, vol. 33(15), pages 2023-2036, October.
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