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A database for modeling energy use in the non-domestic building stock of England and Wales


  • Bruhns, Harry
  • Steadman, Philip
  • Herring, Horace


This paper gives details of this NDBS project, which is 'mapping' the entire non-domestic building stock of England and Wales -- some 2 million premises. The project draws on detailed surveys of thousands of buildings and data from numerous other sources. The result of this research is a database from which it is possible to draw inferences from the local survey data and apply them to the national stock. As its first major output, the database has been used to estimate the national floor area and the exposed wall, glazing and roof areas. The database enables analysts to follow trends in the composition of the non-domestic stock, and its fuel and energy use. It has been used to predict the probable outcomes of various energy-efficiency measures and to monitor the effectiveness of such measures once implemented.

Suggested Citation

  • Bruhns, Harry & Steadman, Philip & Herring, Horace, 2000. "A database for modeling energy use in the non-domestic building stock of England and Wales," Applied Energy, Elsevier, vol. 66(4), pages 277-297, August.
  • Handle: RePEc:eee:appene:v:66:y:2000:i:4:p:277-297

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    References listed on IDEAS

    1. Helena Titheridge & Godfrey Boyle & Paul Fleming, 1996. "Development and Validation of a Computer Model for Assessing Energy Demand and Supply Patterns in the Urban Environment," Energy & Environment, , vol. 7(1), pages 29-40, February.
    2. Mortimer, N. D. & Ashley, A. & Elsayed, M. & Kelly, M. D. & Rix, J. H. R., 1999. "Developing a database of energy use in the UK non-domestic building stock," Energy Policy, Elsevier, vol. 27(8), pages 451-468, August.
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    Cited by:

    1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Kranzl, Lukas & Hummel, Marcus & Müller, Andreas & Steinbach, Jan, 2013. "Renewable heating: Perspectives and the impact of policy instruments," Energy Policy, Elsevier, vol. 59(C), pages 44-58.
    3. Kiluk, Sebastian, 2012. "Algorithmic acquisition of diagnostic patterns in district heating billing system," Applied Energy, Elsevier, vol. 91(1), pages 146-155.
    4. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.


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