4.5 Article

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

Journal

ENERGIES
Volume 14, Issue 16, Pages -

Publisher

MDPI
DOI: 10.3390/en14165208

Keywords

crowdsourcing; building energy; urban heat island; local climate zone; degree hours; London

Categories

Funding

  1. UROP UG research at the University of Reading
  2. GuangDong Basic and Applied Basic Research Foundation [2020A1515110763]
  3. Innovation of Science and Technology Commission of Shenzhen Municipality Ministry [JCYJ 20180305180905450]

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This study estimated the building cooling/heating load in London using crowdsourced data, revealing that the temperature differences between different urban areas are consistent with the Local Climate Zone definitions. It also highlighted both the advantages and challenges of using crowdsourced data for urban climate and building energy research.
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.

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