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Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building

  • In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations.

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Author details:Khem Raj GautamORCiD, Guoqiang Zhang, Niels LandwehrORCiDGND, Julian AdolphsORCiDGND
DOI:https://doi.org/10.1016/j.compag.2021.106259
ISSN:0168-1699
ISSN:1872-7107
Title of parent work (English):Computers and electronics in agriculture : COMPAG online ; an international journal
Publisher:Elsevier Science
Place of publishing:Amsterdam [u.a.]
Publication type:Article
Language:English
Date of first publication:2021/06/23
Publication year:2021
Release date:2024/03/21
Tag:Animal building; Automatically controlled windows; Machine learning; Natural ventilation; Optimization
Volume:187
Article number:106259
Number of pages:10
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
6 Technik, Medizin, angewandte Wissenschaften / 63 Landwirtschaft / 630 Landwirtschaft und verwandte Bereiche
6 Technik, Medizin, angewandte Wissenschaften / 64 Hauswirtschaft und Familie / 640 Hauswirtschaft und Familie
Peer review:Referiert
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