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

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dc.contributor.authorManna, Carlo
dc.contributor.authorWilson, Nic
dc.contributor.authorBrown, Kenneth N.
dc.contributor.funderIntel Corporationen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderIrish Research Council for Science, Engineering and Technologyen
dc.date.accessioned2018-07-09T11:31:07Z
dc.date.available2018-07-09T11:31:07Z
dc.date.issued2013-01
dc.date.updated2014-01-16T12:46:03Z
dc.description.abstractA 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.sponsorshipIrish Research Council for Science, Engineering and Technology (Enterprise Partnership Scheme)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationManna, 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: 10.5220/0004375100320040en
dc.identifier.doi10.5220/0004375100320040
dc.identifier.endpage40en
dc.identifier.isbn978-989-8565-55-6
dc.identifier.startpage32en
dc.identifier.urihttps://hdl.handle.net/10468/6436
dc.language.isoenen
dc.publisherScience and Technology Publicationsen
dc.relation.projectinfo: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.projectinfo: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
dc.subjectMachine learningen
dc.subjectSmart buildingsen
dc.subjectThermal comforten
dc.titlePersonalized thermal comfort forecasting for smart buildings via locally weighted regression with adaptive bandwidthen
dc.typeConference itemen
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