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Modelling household spatial energy intensity consumption patterns for building envelopes, heating systems and temperature controls in cities

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  • Urquizo, Javier
  • Calderón, Carlos
  • James, Philip

Abstract

This paper explore the benefits of a bottom-up spatially enabled engineering building-based energy framework in identifying neighbourhoods, and community’s building aggregated areas with spatial patterns. We argue that an area-based approach allows more houses to be targeted in places where local area characteristics show inefficient elements, and may therefore potentially capture a greater number of households per unit of cost, compared to the existing self-referral methods. We propose a spatial method to show the extent of building envelopes, heating systems and temperature controls. Heating controls, which are not recorded in the United Kingdom Homes Energy Efficiency Database (HEED), but we believe would be considered good practice to maintain balanced temperatures around the house, and also potentially reduce the complexity in modelling the thermal zones. Additionally, heating controls are seen as compulsory in new building regulations, an eligible measure in Green Deal and Energy Company Obligations, and in the United Kingdom Department of Energy and Climate Change (DECC) heat strategy. This paper has taught us that the emerging picture surrounding local energy modelling and that, for example, singularities such as group heating and district heating (decentralised energy supply) have a great impact on final energy consumption calculations.

Suggested Citation

  • Urquizo, Javier & Calderón, Carlos & James, Philip, 2018. "Modelling household spatial energy intensity consumption patterns for building envelopes, heating systems and temperature controls in cities," Applied Energy, Elsevier, vol. 226(C), pages 670-681.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:670-681
    DOI: 10.1016/j.apenergy.2018.05.125
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    References listed on IDEAS

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    1. Javier Urquizo & Carlos Calderón & Philip James, 2017. "Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas," Energies, MDPI, vol. 10(12), pages 1-22, December.
    2. Hargreaves, Anthony & Cheng, Vicky & Deshmukh, Sandip & Leach, Matthew & Steemers, Koen, 2017. "Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply," Applied Energy, Elsevier, vol. 186(P3), pages 549-561.
    3. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    4. Parshall, Lily & Gurney, Kevin & Hammer, Stephen A. & Mendoza, Daniel & Zhou, Yuyu & Geethakumar, Sarath, 2010. "Modeling energy consumption and CO2 emissions at the urban scale: Methodological challenges and insights from the United States," Energy Policy, Elsevier, vol. 38(9), pages 4765-4782, September.
    5. Kelly, Scott & Crawford-Brown, Doug & Pollitt, Michael G., 2012. "Building performance evaluation and certification in the UK: Is SAP fit for purpose?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6861-6878.
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