Personalized thermal comfort forecasting for smart buildings via locally weighted regression with adaptive bandwidth

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dc.contributor.author Manna, Carlo
dc.contributor.author Wilson, Nic
dc.contributor.author Brown, Kenneth N.
dc.date.accessioned 2018-07-09T11:31:07Z
dc.date.available 2018-07-09T11:31:07Z
dc.date.issued 2013-01
dc.identifier.citation Manna, C., Wilson, N. and Brown, K.N. (2013) 'Personalized thermal comfort forecasting for smart buildings via locally weighted regression with adaptive bandwidth', Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, Aachen, Germany, 9-10 May. DOI: 10.5220/0004375100320040 en
dc.identifier.startpage 32 en
dc.identifier.endpage 40 en
dc.identifier.isbn 978-989-8565-55-6
dc.identifier.uri http://hdl.handle.net/10468/6436
dc.identifier.doi 10.5220/0004375100320040
dc.description.abstract A personalized thermal comfort prediction method is proposed for use in combination with smart controls for building automation. Occupant thermal comfort is traditionally measured and predicted by the Predicted Mean Vote (PMV) metric, which is based on extensive field trials linking reported comfort levels with the various factors. However, PMV is a statistical measure applying to large populations, and the actual thermal comfort could be significantly different from the predicted value for small groups of people. Moreover it may be hard to use for a real-time controller due to the number of sensor readings needed. In the present paper, we propose Robust Locally Weighted Regression with Adaptive Bandwidth (LRAB), a kernel based method, to learn individual occupant thermal comfort based on historical reports. Using publicly available datasets, we demonstrate that this technique is significantly more accurate in predicting individual comfort than PMV and other kernel methods. Therefore, is a promising technique to be used as input to adpative HVAC control systems. en
dc.description.sponsorship Irish Research Council for Science, Engineering and Technology (Enterprise Partnership Scheme) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Science and Technology Publications en
dc.subject Machine learning en
dc.subject Smart buildings en
dc.subject Thermal comfort en
dc.title Personalized thermal comfort forecasting for smart buildings via locally weighted regression with adaptive bandwidth en
dc.type Conference item en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@4c.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2014-01-16T12:46:03Z
dc.description.version Accepted Version en
dc.internal.rssid 237168667
dc.contributor.funder Intel Corporation en
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Irish Research Council for Science, Engineering and Technology en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress n.wilson@4c.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Strategic Research Cluster/07/SRC/I1170/IE/SRC ITOBO: Information and Communication Technology for Sustainable and Optimised Building Operation/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/08/IN.1/I1912/IE/The Development of Artificial intelligence Approaches for Preferences in Combinational Problems/ en


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