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A housing stock model of non-heating end-use energy in England verified by aggregate energy use data

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  • Lorimer, Stephen

Abstract

This paper proposes a housing stock model of non-heating end-use energy for England that can be verified using aggregate energy use data available for small areas. These end-uses, commonly referred to as appliances and lighting, are a rapidly increasing part of residential energy demand. This paper proposes a model that can be verified using aggregated data of electricity meters in small areas and census data on housing. Secondly, any differences that open up between major collections of housing could potentially be resolved by using data from frequently updated expenditure surveys. For the year 2008, the model overestimated domestic non-heating energy use at the national scale by 1.5%. This model was then used on the residential sector with various area classifications, which found that rural and suburban areas were generally underestimated by up to 3.3% and urban areas overestimated by up to 5.2% with the notable exception of “professional city life” classifications. The model proposed in this paper has the potential to be a verifiable and adaptable model for non-heating end-use energy in households in England for the future.

Suggested Citation

  • Lorimer, Stephen, 2012. "A housing stock model of non-heating end-use energy in England verified by aggregate energy use data," Energy Policy, Elsevier, vol. 50(C), pages 419-427.
    • Handle: RePEc:eee:enepol:v:50:y:2012:i:c:p:419-427
    DOI: 10.1016/j.enpol.2012.07.037
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    Cited by:

    1. Chris Matthew & Catalina Spataru, 2023. "Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands," Energies, MDPI, vol. 16(13), pages 1-25, June.
    2. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).

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    Keywords

    Energy; Domestic; Model;
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