MPC-Suitable Hygrothermal, Data-Based Modeling of an Aquaculture Hall for the (Energetic) Optimisation of Inner Climate

Model predictive control (MPC) for buildings finds an increasing interest in research with mostly thermal models being investigated. There are, however, buildings with internal vapor loads, where humidity is a dominating factor for control of heating, ventilation and air conditioning (HVAC). In this paper, data-based hygrothermal models of an aquaculture hall and its HVAC-unit are investigated using real-life data from over six months. In a forward selection needed model complexity is investigated. A model is selected that shows average model deviations of around $0.5^{\circ} \mathrm{C}$ and 1.4 % for temperature and relative humidity (RH) in the validation period of two weeks. Over longer periods, the selected model shows drift. Implementing an extended Kalman filter (EKF) for adaptive modeling enabled 24 hour predictions with average model deviations of around $0.5^{\circ} \mathrm{C}$ and 1.8 % over the whole time period. To support future research, the model and the underlying dataset is shared publicly.