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

Benjamin, Kit, Luo, Zhiwen ORCID: and Wang, Xiaoxue 2021. Crowdsourcing urban air temperature data for estimating urban heat island and building heating/cooling load in London. Energies 14 (16) , 5208. 10.3390/en14165208

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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
Date Type: Publication
Status: Published
Schools: Architecture
Additional Information: This is an open access article distributed under the Creative Commons Attribution License
Publisher: MDPI
Date of First Compliant Deposit: 10 August 2022
Date of Acceptance: 13 August 2021
Last Modified: 16 May 2023 17:13

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