Energy performance optimization of buildings using data mining techniques

C.J.J. Corten, Eric Willems, Shalika Walker, Wim Zeiler

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
75 Downloads (Pure)


The operational energy consumption of buildings often does not match with the predicted results from the design. One of the most dominant causes for these so-called energy performance gaps is the poor operational practice of the heating, ventilation and air conditioning (HVAC) systems. To improve underperforming HVAC systems, analysis of operational data collected by the building management system (BMS) can provide valuable information. In order to completely use and interpret operational data, the building sector is urging for methods and tools. Data mining (DM) is identified as an emerging powerful technique with great potential for discovering hidden knowledge in large data sets. In this study, the performance of HVAC systems was analysed using regression analysis as DM technique. This leads to valuable insights to control and improve the building energy performance. The results show that a reduction of 7-13% on the heating demand and 41-70% on the cooling demand can be obtained.
Original languageEnglish
Title of host publicationCLIMA 2019 Congress
Subtitle of host publicationInformation and Communication Technologies (ICT) for the Intelligent Building Management
PublisherEDP Sciences
Number of pages8
Publication statusPublished - 13 Aug 2019
Event13th REHVA World Congress, CLIMA 2019 - Bucharest, Romania
Duration: 26 May 201929 May 2019
Conference number: 13

Publication series

NameE3S Web of Conferences
PublisherEDP Sciences
ISSN (Electronic)2555-0403


Conference13th REHVA World Congress, CLIMA 2019
Abbreviated titleCLIMA 2019
Internet address


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