Discovering gradual patterns in building operations for improving building energy efficiency

Cheng Fan, Yongjun Sun, Kui Shan, Fu Xiao, Jiayuan Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

The development of information technologies has enabled 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 potentials of big building operational data in enhancing building energy efficiency. The rapid development of data mining has provided powerful tools for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for discovering useful patterns from building operational data. The knowledge discovered is represented as gradual relationships, i.e., “the more/less A, the more/less B”. It can bring special interests to building energy management by highlighting co-variations among numerical building variables. This study investigated the usefulness of gradual pattern mining for building energy management. A generic methodology was proposed to ensure the quality and applicability of the knowledge discovered. The methodology was validated through a case study. The results showed that the methodology could successfully extract valuable insights on building operation characteristics and provide opportunities for building energy efficiency enhancement.

Original languageEnglish
Pages (from-to)116-123
Number of pages8
JournalApplied Energy
Volume224
DOIs
Publication statusPublished - 15 Aug 2018

Keywords

  • Building energy efficiency
  • Building operational performance
  • Data mining
  • Gradual pattern mining
  • Motif discovery

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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