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

Uittreksel

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.
TaalEngels
TitelCLIMA 2019 Congress
SubtitelInformation and Communication Technologies (ICT) for the Intelligent Building Management
UitgeverijEDP Sciences
Aantal pagina's8
DOI's
StatusGepubliceerd - 13 aug 2019
Evenement13th REHVA World Congress, CLIMA 2019 - Bucharest, Roemenië
Duur: 26 mei 201929 mei 2019
Congresnummer: 13
https://www.clima2019.org/

Publicatie series

NaamE3S Web of Conferences
UitgeverijEDP Sciences
Volume111
ISSN van elektronische versie2555-0403

Congres

Congres13th REHVA World Congress, CLIMA 2019
Verkorte titelCLIMA 2019
LandRoemenië
StadBucharest
Periode26/05/1929/05/19
Internet adres

Vingerafdruk

Data mining
Air conditioning
Ventilation
Heating
Regression analysis
Energy utilization
Systems analysis
Cooling

Citeer dit

Corten, C. J. J., Willems, E., Walker, S., & Zeiler, W. (2019). Energy performance optimization of buildings using data mining techniques. In CLIMA 2019 Congress: Information and Communication Technologies (ICT) for the Intelligent Building Management [05016] (E3S Web of Conferences; Vol. 111). EDP Sciences. DOI: 10.1051/e3sconf/201911105016
Corten, C.J.J. ; Willems, Eric ; Walker, Shalika ; Zeiler, Wim. / Energy performance optimization of buildings using data mining techniques. CLIMA 2019 Congress: Information and Communication Technologies (ICT) for the Intelligent Building Management. EDP Sciences, 2019. (E3S Web of Conferences).
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title = "Energy performance optimization of buildings using data mining techniques",
abstract = "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.",
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Corten, CJJ, Willems, E, Walker, S & Zeiler, W 2019, Energy performance optimization of buildings using data mining techniques. in CLIMA 2019 Congress: Information and Communication Technologies (ICT) for the Intelligent Building Management., 05016, E3S Web of Conferences, vol. 111, EDP Sciences, Bucharest, Roemenië, 26/05/19. DOI: 10.1051/e3sconf/201911105016

Energy performance optimization of buildings using data mining techniques. / Corten, C.J.J.; Willems, Eric; Walker, Shalika; Zeiler, Wim.

CLIMA 2019 Congress: Information and Communication Technologies (ICT) for the Intelligent Building Management. EDP Sciences, 2019. 05016 (E3S Web of Conferences; Vol. 111).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AB - 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.

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Corten CJJ, Willems E, Walker S, Zeiler W. Energy performance optimization of buildings using data mining techniques. In CLIMA 2019 Congress: Information and Communication Technologies (ICT) for the Intelligent Building Management. EDP Sciences. 2019. 05016. (E3S Web of Conferences). Beschikbaar vanaf, DOI: 10.1051/e3sconf/201911105016