Crowdsourcing urban air temperature data for estimating urban heat island and building heating/cooling load in London

Benjamin, K., Luo, Z. ORCID: https://orcid.org/0000-0002-2082-3958 and Wang, X. (2021) Crowdsourcing urban air temperature data for estimating urban heat island and building heating/cooling load in London. Energies, 14 (16). 5208. ISSN 1996-1073 doi: 10.3390/en14165208

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/99879
Identification Number/DOI	10.3390/en14165208
Refereed	Yes
Divisions	Science > School of the Built Environment > Urban Living group Science > School of the Built Environment > Energy and Environmental Engineering group
Publisher	MDPI Publishing, Basel
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