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Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms

Author

Listed:
  • Anthony Beck
  • Gavin Long
  • Doreen S Boyd
  • Julian F Rosser
  • Jeremy Morley
  • Richard Duffield
  • Mike Sanderson
  • Darren Robinson

Abstract

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc. and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this article, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain: MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment.

Suggested Citation

  • Anthony Beck & Gavin Long & Doreen S Boyd & Julian F Rosser & Jeremy Morley & Richard Duffield & Mike Sanderson & Darren Robinson, 2020. "Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms," Environment and Planning B, , vol. 47(1), pages 45-64, January.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:1:p:45-64
    DOI: 10.1177/2399808318762436
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    References listed on IDEAS

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    1. Bertrand Mareschal & Jean Pierre Brans, 1992. "PROMETHEE V: MCDM problems with segmentation constraints," ULB Institutional Repository 2013/9341, ULB -- Universite Libre de Bruxelles.
    2. 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.
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    Cited by:

    1. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    2. Arthur Acolin & Annette M Kim, 2022. "Algorithmic justice and groundtruthing the remote mapping of informal settlements: The example of Ho Chi Minh City’s periphery," Environment and Planning B, , vol. 49(1), pages 151-168, January.

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