Assessment of Building Operational Performance Using Data Mining Techniques: A Case Study

Cheng Fan, Fu Xiao

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1070-1078
Number of pages9
JournalEnergy Procedia
Volume111
DOIs
Publication statusPublished - 1 Mar 2017
Event8th International Conference on Sustainability in Energy and Buildings, SEB 2016 - Turin, Italy
Duration: 11 Sep 201613 Sep 2016

Keywords

  • Big Data
  • Building Automation System
  • Building Energy Conservation
  • Data Mining
  • Intelligent Building

ASJC Scopus subject areas

  • Energy(all)

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