Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77463
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Title: Mining gradual patterns in big building operational data for building energy efficiency enhancement
Authors: Fan, C
Xiao, F 
Issue Date: 2017
Source: Energy procedia, 2017, v. 143, no. , p. 119-124
Abstract: The advance in information technology has enabled the real-time monitoring and controls over building operations. Massive amounts of building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potential of big building operational data in enhancing building energy efficiency. Data mining (DM) technology, which is renowned for its excellence in discovering hidden knowledge from massive datasets, has attracted increasing attention from the building industry. The rapid development in DM has provided powerful mining methods for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for identifying interesting patterns in big data. The knowledge discovered is represented as gradual rules, i.e., 'the more/less A, the more/less B'. It can bring special interests to building energy management by highlighting co-variations among building variables. This paper investigates the usefulness of gradual pattern mining in analysing massive building operational data. Together with the use of decision trees, motif discovery and association rule mining, a comprehensive mining method is developed to ensure the quality and applicability of the knowledge discovered. The method is validated through a case study, using the real-world data retrieved from an educational building in Hong Kong. It shows that novel and valuable insights on building operation characteristics can be obtained, based on which fault detection and optimal control strategies can be developed to enhance building operational performance.
Keywords: Building energy efficiency
Building operational performance
Data mining
Gradual pattern mining
Knowledge discover
Publisher: Elsevier
Journal: Energy procedia 
EISSN: 1876-6102
DOI: 10.1016/j.egypro.2017.12.658
Description: World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singapore
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). Mining gradual patterns in big building operational data for building energy efficiency enhancement. Energy Procedia, 143, 119-124 is available athttps://dx.doi.org/10.1016/j.egypro.2017.12.658
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