Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141628
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dc.contributor.authorZhang, Weien_US
dc.contributor.authorLiu, Fangen_US
dc.contributor.authorFan, Ruien_US
dc.date.accessioned2020-06-09T09:01:49Z-
dc.date.available2020-06-09T09:01:49Z-
dc.date.issued2018-
dc.identifier.citationZhang, W., Liu, F., & Fan, R. (2018). Improved thermal comfort modeling for smart buildings : a data analytics study. International Journal of Electrical Power and Energy Systems, 103, 634-643. doi:10.1016/j.ijepes.2018.06.026en_US
dc.identifier.issn0142-0615en_US
dc.identifier.urihttps://hdl.handle.net/10356/141628-
dc.description.abstractThermal comfort is a key consideration in the design and modeling of buildings and is one of the main steps to achieving smart building control and operation. Existing solutions model thermal comfort based on factors such as indoor temperature. However, these factors are not directly controllable by building operations, and instead are a by-product of complex interactions between controllable parameters such as air conditioning setpoint and other environmental conditions. In this paper, we use machine learning (ML) to bridge the gap between controllable building parameters and thermal comfort, by conducting an extensive study on the efficacy of different ML techniques for modeling comfort levels. We show that neural networks are especially effective, and achieve 98.7% accuracy on average. We also show these networks can lead to linear models where thermal comfort score scales linearly with the HVAC setpoint, and that the linear models can be used to quickly and accurately find the optimal setpoint for the desired comfort level.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Electrical Power and Energy Systemsen_US
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleImproved thermal comfort modeling for smart buildings : a data analytics studyen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.ijepes.2018.06.026-
dc.identifier.scopus2-s2.0-85048740235-
dc.identifier.volume103en_US
dc.identifier.spage634en_US
dc.identifier.epage643en_US
dc.subject.keywordsThermal Comforten_US
dc.subject.keywordsMachine Learningen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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