Mining Gradual Patterns in Big Building Operational Data for Building Energy Efficiency Enhancement

Cheng Fan, Fu Xiao

Research output: Journal article publicationConference articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Published by Elsevier Ltd. 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.
Original languageEnglish
Pages (from-to)119-124
Number of pages6
JournalEnergy Procedia
Volume143
DOIs
Publication statusPublished - 1 Jan 2017
Event1st Joint Conference on World Engineers Summit - Applied Energy Symposium and Forum: Low Carbon Cities and Urban Energy, WES-CUE 2017 - Singapore, Singapore
Duration: 19 Jul 201721 Jul 2017

Keywords

  • Building energy efficiency
  • Building operational performance
  • Data mining
  • Gradual pattern mining
  • Knowledge discover

ASJC Scopus subject areas

  • Energy(all)

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