Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103117
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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.
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