TY  - JOUR
A1  - Gautam, Khem Raj
A1  - Zhang, Guoqiang
A1  - Landwehr, Niels
A1  - Adolphs, Julian
T1  - Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building
JF  - Computers and electronics in agriculture : COMPAG online ; an international journal
N2  - 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.
KW  - Animal building
KW  - Natural ventilation
KW  - Automatically controlled windows
KW  - Machine learning
KW  - Optimization
Y1  - 2021
U6  - https://doi.org/10.1016/j.compag.2021.106259
SN  - 0168-1699
SN  - 1872-7107
VL  - 187
PB  - Elsevier Science
CY  - Amsterdam [u.a.]
ER  -