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dc.contributor.authorDavies, M-
dc.contributor.authorCroxford, B-
dc.contributor.authorBhuiyan, S-
dc.contributor.authorMavrogianni, A-
dc.date.accessioned2014-07-14T09:53:53Z-
dc.date.available2014-07-14T09:53:53Z-
dc.date.issued2010-
dc.identifier.citationSolar Energy, 84(12), 2246-2255, 2010en_US
dc.identifier.issn0038-092X-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0038092X10002616en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/8610-
dc.descriptionThis is the post-print version of the final paper published in Solar Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.en_US
dc.description.abstractThis paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West–East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis.en_US
dc.description.sponsorshipEPSRCen_US
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectUrban Heat Islanden_US
dc.subjectPredictionen_US
dc.subjectHeatingen_US
dc.subjectCoolingen_US
dc.subjectBuildingsen_US
dc.subjectANNen_US
dc.titleA validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of Londonen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.solener.2010.08.002-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff TxP-
pubs.organisational-data/Brunel/Brunel Active Staff TxP/College of Engineering, Design and Physical Sciences-
pubs.organisational-data/Brunel/Brunel Active Staff TxP/College of Engineering, Design and Physical Sciences/Dept of Mechanical, Aerospace and Civil Engineering-
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pubs.organisational-data/Brunel/University Research Centres and Groups/School of Engineering and Design - URCs and Groups/Centre for Energy and Built Environment Research-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
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Appears in Collections:Mechanical and Aerospace Engineering
Dept of Mechanical Aerospace and Civil Engineering Research Papers

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