Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/61190
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.creator | Liang, X | - |
dc.creator | Hong, T | - |
dc.creator | Shen, GQ | - |
dc.date.accessioned | 2016-12-19T08:55:06Z | - |
dc.date.available | 2016-12-19T08:55:06Z | - |
dc.identifier.issn | 0360-1323 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/61190 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2016 Elsevier Ltd. All rights reserved. | - |
dc.rights | NOTICE: this is the author’s version of a work that was accepted for publication in Building and Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version Liang X., Hong T., & Shen G.Q.P. (2016) Occupancy data analytics and prediction: a case study. Building and Environment. 102, 179-192 is available at https://doi.org/10.1016/j.buildenv.2016.03.027 | - |
dc.subject | Data mining | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Occupancy prediction | en_US |
dc.subject | Occupant presence | en_US |
dc.title | Occupancy data analytics and prediction : a case study | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 179 | en_US |
dc.identifier.epage | 192 | en_US |
dc.identifier.volume | 102 | en_US |
dc.identifier.doi | 10.1016/j.buildenv.2016.03.027 | en_US |
dcterms.abstract | Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Building and environment, June 2016, v. 102, p. 179-192 | - |
dcterms.isPartOf | Building and environment | - |
dcterms.issued | 2016-6 | - |
dc.identifier.isi | WOS:000375498300015 | - |
dc.identifier.scopus | 2-s2.0-84962318935 | - |
dc.identifier.eissn | 1873-684X | en_US |
dc.identifier.rosgroupid | 2015002308 | - |
dc.description.ros | 2015-2016 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0135-n07 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Liang_Xin_2016d.pdf | Pre-Published version | 2.39 MB | Adobe PDF | View/Open |
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