Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/77162
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building Services Engineering | en_US |
dc.creator | Fan, C | en_US |
dc.creator | Xiao, F | en_US |
dc.date.accessioned | 2018-07-30T08:26:38Z | - |
dc.date.available | 2018-07-30T08:26:38Z | - |
dc.identifier.issn | 0143-6244 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/77162 | - |
dc.language.iso | en | en_US |
dc.publisher | SAGE Publications | en_US |
dc.rights | This is the accepted version of the publication Fan, C., & Xiao, F. (2018). Mining big building operational data for improving building energy efficiency: A case study. Building Services Engineering Research and Technology, 39(1), 117-128. © Authors 2017. DOI: 10.1177/0143624417704977 | en_US |
dc.subject | Association rule mining | en_US |
dc.subject | Big building operational data | en_US |
dc.subject | Building energy efficiency | en_US |
dc.subject | Clustering analysis | en_US |
dc.subject | Decision tree | en_US |
dc.title | Mining big building operational data for improving building energy efficiency : a case study | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 117 | en_US |
dc.identifier.epage | 128 | en_US |
dc.identifier.volume | 39 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1177/0143624417704977 | en_US |
dcterms.abstract | Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application: The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Building services engineering research and technology, Jan. 2018, v. 39, no. 1, p. 117-128 | en_US |
dcterms.isPartOf | Building services engineering research and technology | en_US |
dcterms.issued | 2018-01 | - |
dc.identifier.scopus | 2-s2.0-85040009806 | - |
dc.identifier.eissn | 1477-0849 | en_US |
dc.identifier.rosgroupid | 2017006187 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201807 bcrc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | RGC-B3-0507, BEEE-0553 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University; Shenzhen University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6810366 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fan_Mining_Big_Building.pdf | Pre-Published version | 776 kB | Adobe PDF | View/Open |
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