Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43506
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Building Services Engineeringen_US
dc.contributorDepartment of Computingen_US
dc.creatorFan, Cen_US
dc.creatorXiao, Fen_US
dc.creatorMadsen, Hen_US
dc.creatorWang, Den_US
dc.date.accessioned2016-06-07T06:16:31Z-
dc.date.available2016-06-07T06:16:31Z-
dc.identifier.issn0378-7788en_US
dc.identifier.urihttp://hdl.handle.net/10397/43506-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2015 Elsevier B.V. All rights reserved.en_US
dc.rights© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Fan, C., Xiao, F., Madsen, H., & Wang, D. (2015). Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings, 109, 75-89 is available at https://doi.org/10.1016/j.enbuild.2015.09.060en_US
dc.subjectBig dataen_US
dc.subjectBuilding automation systemen_US
dc.subjectBuilding energy managementen_US
dc.subjectTemporal knowledge discoveryen_US
dc.subjectTime series data miningen_US
dc.titleTemporal knowledge discovery in big BAS data for building energy managementen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage75en_US
dc.identifier.epage89en_US
dc.identifier.volume109en_US
dc.identifier.doi10.1016/j.enbuild.2015.09.060en_US
dcterms.abstractWith the advances of information technologies, today's building automation systems (BASs) are capable of managing building operational performance in an efficient and convenient way. Meanwhile, the amount of real-time monitoring and control data in BASs grows continually in the building lifecycle, which stimulates an intense demand for powerful big data analysis tools in BASs. Existing big data analytics adopted in the building automation industry focus on mining cross-sectional relationships, whereas the temporal relationships, i.e., the relationships over time, are usually overlooked. However, building operations are typically dynamic and BAS data are essentially multivariate time series data. This paper presents a time series data mining methodology for temporal knowledge discovery in big BAS data. A number of time series data mining techniques are explored and carefully assembled, including the Symbolic Aggregate approXimation (SAX), motif discovery, and temporal association rule mining. This study also develops two methods for the efficient post-processing of knowledge discovered. The methodology has been applied to analyze the BAS data retrieved from a real building. The temporal knowledge discovered is valuable to identify dynamics, patterns and anomalies in building operations, derive temporal association rules within and between subsystems, assess building system performance and spot opportunities in energy conservation.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEnergy and buildings, 15 Dec. 2015, v. 109, p. 75-89en_US
dcterms.isPartOfEnergy and buildingsen_US
dcterms.issued2015-12-15-
dc.identifier.scopus2-s2.0-84944755202-
dc.identifier.eissn1872-6178en_US
dc.identifier.rosgroupid2015001329-
dc.description.ros2015-2016 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberRGC-B3-0505-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextDanish CITIES project (DSF-1305-00027B)en_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Temporal_Knowledge_Discovery.pdfPre-Published version1.24 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

120
Last Week
1
Last month
Citations as of Mar 24, 2024

Downloads

104
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

116
Last Week
0
Last month
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

98
Last Week
1
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.