Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103117
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dc.contributorDepartment of Building Environment and Energy Engineeringen_US
dc.creatorFan, Cen_US
dc.creatorXiao, Fen_US
dc.date.accessioned2023-11-28T03:27:12Z-
dc.date.available2023-11-28T03:27:12Z-
dc.identifier.urihttp://hdl.handle.net/10397/103117-
dc.description8th International Conference on Sustainability in Energy and Buildings, SEB-16, 11-13 September 2016, Turin, ITALYen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Fan, C., & Xiao, F. (2017). Assessment of building operational performance using data mining techniques: a case study. Energy Procedia, 111, 1070-1078 is available at https://doi.org/10.1016/j.egypro.2017.03.270.en_US
dc.subjectBig dataen_US
dc.subjectBuilding automation systemen_US
dc.subjectBuilding energy conservationen_US
dc.subjectData miningen_US
dc.subjectIntelligent buildingen_US
dc.titleAssessment of building operational performance using data mining techniques : a case studyen_US
dc.typeConference Paperen_US
dc.identifier.spage1070en_US
dc.identifier.epage1078en_US
dc.identifier.volume111en_US
dc.identifier.doi10.1016/j.egypro.2017.03.270en_US
dcterms.abstractToday's buildings are not only energy intensive, but also information intensive. Massive amounts of operational data are available for knowledge discovery. Data mining (DM) has excellent ability in extracting insights from massive data. This paper performs a case study on the assessment of building operational performance using DM techniques. Typical DM techniques are compared and considerations for choosing specific DM techniques for the case study are presented. The methodology developed has been applied to analyze the data retrieved from a university building in Hong Kong. Useful insights have been obtained to identify typical operation patterns and energy conservation opportunities.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEnergy procedia, Mar. 2017, v. 111, p. 1070-1078en_US
dcterms.isPartOfEnergy procediaen_US
dcterms.issued2017-03-
dc.identifier.scopus2-s2.0-85017213480-
dc.relation.conferenceInternational Conference on Sustainability in Energy and Buildings [SEB]en_US
dc.identifier.eissn1876-6102en_US
dc.description.validate202311 bckwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberBEEE-0850-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6909281-
dc.description.oaCategoryCCen_US
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