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An effective methodology to quantify cooling demand in the UK housing stock

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  • Corcoran, Lloyd
  • Saikia, Pranaynil
  • Ugalde-Loo, Carlos E.
  • Abeysekera, Muditha

Abstract

According to the 2020 UN emissions report an increase by 3 °C of the average global temperature compared to pre-industrial levels is to be expected if no corrective measures are implemented. Alongside this, the UK Meteorological Office predicts that the UK will see a surge in both the recurrence and severity of heatwaves during summers—leading to an increased demand for space cooling. Although commercial infrastructures are likely to incorporate cooling provisions, residential properties are generally at a nascent stage to facilitate indoor cooling. Upgrading the cooling capabilities of residential dwellings would require a clear understanding of cooling demand. To this end, this paper presents a methodology to quantify cooling demand for typical UK dwellings. Following an in-depth literature review of the current UK housing stock to retrieve physical building data, a physics-based model was created using commercial building envelope modelling software. This considered building construction methods, ages, and layouts. To provide confidence in the approach, the model was verified with real data taken from a semi-detached dwelling in Loughborough, UK, and subsequently, thermal models for the most common type of dwellings were developed. Results highlight how cooling demand varies for differing dwelling types, orientations, locations, and constructions. For instance, for a typical design year, corner flats on the top floor of a 3-storey building in Cardiff, UK, with an orientation of 0° (north-facing) have the highest monthly cooling demand of 27.21 kWh and the bottom floor mid-flats have the lowest demand of 17.36 kWh. The presented methodology provides an initial framework to generate residential cooling demand data, which could be used to inform building developers, utilities, and local authorities on cooling demand peaks, overheating risks, and energy efficiency of typical UK dwellings in a warming world.

Suggested Citation

  • Corcoran, Lloyd & Saikia, Pranaynil & Ugalde-Loo, Carlos E. & Abeysekera, Muditha, 2025. "An effective methodology to quantify cooling demand in the UK housing stock," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924023869
    DOI: 10.1016/j.apenergy.2024.125002
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    References listed on IDEAS

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    1. Reilly, Aidan & Kinnane, Oliver, 2017. "The impact of thermal mass on building energy consumption," Applied Energy, Elsevier, vol. 198(C), pages 108-121.
    2. Khosravi, Fatemeh & Lowes, Richard & Ugalde-Loo, Carlos E., 2023. "Cooling is hotting up in the UK," Energy Policy, Elsevier, vol. 174(C).
    3. Deb, C. & Gelder, L.V. & Spiekman, M. & Pandraud, Guillaume & Jack, R. & Fitton, R., 2021. "Measuring the heat transfer coefficient (HTC) in buildings: A stakeholder's survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    5. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Koo, Choongwan & Park, Sungki & Hong, Taehoon & Park, Hyo Seon, 2014. "An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method," Applied Energy, Elsevier, vol. 115(C), pages 205-215.
    7. Johra, Hicham & Heiselberg, Per, 2017. "Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 19-32.
    8. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
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