Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

This paper aims to explore the potential application of advanced DM techniques for effective utilization of big building operational data. Case studies of mining the operational data of an institutional building for cooling load prediction and operation performance improvement is presented. Deep learning-based prediction techniques, decision tree and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for forecasting 24-hour ahead building cooling load profiles, identifying typical building operation patterns and spotting energy conservation opportunities.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
PublisherIEEE
ISBN (Electronic)9781509065172
DOIs
Publication statusPublished - 12 Jun 2017
Event2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 - Hong Kong, Hong Kong
Duration: 29 May 201731 May 2017

Conference

Conference2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
CountryHong Kong
CityHong Kong
Period29/05/1731/05/17

Keywords

  • Big building operational data
  • Building cooling load
  • Building energy efficiency
  • Data mining
  • Deep learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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