Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77463
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dc.contributorDepartment of Building Services Engineeringen_US
dc.creatorFan, Cen_US
dc.creatorXiao, Fen_US
dc.date.accessioned2018-08-28T01:32:31Z-
dc.date.available2018-08-28T01:32:31Z-
dc.identifier.urihttp://hdl.handle.net/10397/77463-
dc.descriptionWorld Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singaporeen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2017 The Authors. Published by Elsevier Ltd.en_US
dc.rightsThis 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). Mining gradual patterns in big building operational data for building energy efficiency enhancement. Energy Procedia, 143, 119-124 is available athttps://dx.doi.org/10.1016/j.egypro.2017.12.658en_US
dc.subjectBuilding energy efficiencyen_US
dc.subjectBuilding operational performanceen_US
dc.subjectData miningen_US
dc.subjectGradual pattern miningen_US
dc.subjectKnowledge discoveren_US
dc.titleMining gradual patterns in big building operational data for building energy efficiency enhancementen_US
dc.typeConference Paperen_US
dc.identifier.spage119en_US
dc.identifier.epage124en_US
dc.identifier.volume143en_US
dc.identifier.doi10.1016/j.egypro.2017.12.658en_US
dcterms.abstractThe advance in information technology has enabled the real-time monitoring and controls over building operations. Massive amounts of building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potential of big building operational data in enhancing building energy efficiency. Data mining (DM) technology, which is renowned for its excellence in discovering hidden knowledge from massive datasets, has attracted increasing attention from the building industry. The rapid development in DM has provided powerful mining methods for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for identifying interesting patterns in big data. The knowledge discovered is represented as gradual rules, i.e., 'the more/less A, the more/less B'. It can bring special interests to building energy management by highlighting co-variations among building variables. This paper investigates the usefulness of gradual pattern mining in analysing massive building operational data. Together with the use of decision trees, motif discovery and association rule mining, a comprehensive mining method is developed to ensure the quality and applicability of the knowledge discovered. The method is validated through a case study, using the real-world data retrieved from an educational building in Hong Kong. It shows that novel and valuable insights on building operation characteristics can be obtained, based on which fault detection and optimal control strategies can be developed to enhance building operational performance.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEnergy procedia, 2017, v. 143, no. , p. 119-124en_US
dcterms.isPartOfEnergy procediaen_US
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85040825674-
dc.relation.conferenceWorld Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference [WES-CUE]en_US
dc.identifier.eissn1876-6102en_US
dc.description.validate201808 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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