| Issue |
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
|
|
|---|---|---|
| Article Number | 05016 | |
| Number of page(s) | 8 | |
| Section | Information and Communication Technologies (ICT) for the Intelligent Building Management | |
| DOI | https://doi.org/10.1051/e3sconf/201911105016 | |
| Published online | 13 August 2019 | |
Energy performance optimization of buildings using data mining techniques
1 Department of the Built Environment, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
2 Huygen Engineers & Consultants, P.O. Box 27, 6160 MB, Geleen, The Netherlands
* Corresponding authors: k.corten@huygen.net, w.zeiler@tue.nl
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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