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http://hdl.handle.net/10397/103117
Title: | Assessment of building operational performance using data mining techniques : a case study | Authors: | Fan, C Xiao, F |
Issue Date: | Mar-2017 | Source: | Energy procedia, Mar. 2017, v. 111, p. 1070-1078 | Abstract: | Today'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. | Keywords: | Big data Building automation system Building energy conservation Data mining Intelligent building |
Publisher: | Elsevier | Journal: | Energy procedia | EISSN: | 1876-6102 | DOI: | 10.1016/j.egypro.2017.03.270 | Description: | 8th International Conference on Sustainability in Energy and Buildings, SEB-16, 11-13 September 2016, Turin, ITALY | 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/). The 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. |
Appears in Collections: | Journal/Magazine Article |
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