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Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London

Author

Listed:
  • Kit Benjamin

    (Department of Meteorology, University of Reading, Reading RG6 6UR, UK)

  • Zhiwen Luo

    (School of the Built Environment, University of Reading, Reading RG6 6UR, UK)

  • Xiaoxue Wang

    (College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518061, China)

Abstract

Urban heat island (UHI) effects significantly impact building energy. Traditional UHI investigation methods are often incapable of providing the high spatial density of observations required to distinguish small-scale temperature differences in the UHI. Crowdsourcing offers a solution. Building cooling/heating load in 2018 has been estimated in London, UK, using crowdsourced data from over 1300 Netatmo personal weather stations. The local climate zone (LCZ) scheme was used to classify the different urban environments of London (UK). Inter-LCZ temperature differences are found to be generally consistent with LCZ temperature definitions. Analysis of cooling degree hours in July shows LCZ 2 (the densest urban LCZ in London) had the highest cooling demand, with a total of 1550 cooling degree hours. The suburban related LCZs 5 and 6 and rural LCZs B and D all had about 80% of the demand of LCZ 2. In December, the rural LCZs A, B and D had the greatest heating demand, with all recording around 5750 heating degree hours. Urban LCZs 2, 5 and 6 had 91%, 86% and 95% of the heating demand of LCZ D, respectively. This study has highlighted both advantages and issues with using crowdsourced data for urban climate and building energy research.

Suggested Citation

  • Kit Benjamin & Zhiwen Luo & Xiaoxue Wang, 2021. "Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London," Energies, MDPI, vol. 14(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5208-:d:620048
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    References listed on IDEAS

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    Cited by:

    1. Tian, Xiaoyu & Zhang, Hanwen & Liu, Lin & Huang, Jiahao & Liu, Liru & Liu, Jing, 2024. "Establishment of LCZ-based urban building energy consumption dataset in hot and humid subtropical regions through a bottom-up method," Applied Energy, Elsevier, vol. 368(C).
    2. Jie Han & Nan Mo & Jingyi Cai & Leixin Ouyang & Zhengxuan Liu, 2024. "Advancing the local climate zones framework: a critical review of methodological progress, persisting challenges, and future research prospects," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    3. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    4. Xinyi Wang & Yuan Chen & Zhichao Wang & Bo Xu & Zhongke Feng, 2024. "Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City," Sustainability, MDPI, vol. 16(8), pages 1-22, April.

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