Energy performance optimization of buildings using data mining techniques

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)
65 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.
Originele taal-2Engels
TitelCLIMA 2019 Congress
SubtitelInformation and Communication Technologies (ICT) for the Intelligent Building Management
UitgeverijEDP Sciences
Aantal pagina's8
StatusGepubliceerd - 13 aug 2019
Evenement13th REHVA World Congress, CLIMA 2019 - Bucharest, Roemenië
Duur: 26 mei 201929 mei 2019
Congresnummer: 13

Publicatie series

NaamE3S Web of Conferences
UitgeverijEDP Sciences
ISSN van elektronische versie2555-0403


Congres13th REHVA World Congress, CLIMA 2019
Verkorte titelCLIMA 2019
Internet adres


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