Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

¹ KU Leuven , Department of Civil Engineering, Construction Technology Cluster, Technology Campus Ghent, Gebroeders De Smetstraat 1, 9000, Gent, Belgium
² KU Leuven, Department of Civil Engineering, Building Physics, Kasteelpark Arenberg 40, 3001, Leuven, Belgium

^* Corresponding author: bart.merema@kuleuven.be

Abstract

In school and office buildings the ventilation system has a large contribution to the total energy use. A control strategy that adjusts the operation to the actual demand can significantly reduce the energy use. This is important in rooms with a highly fluctuating occupancy profile. However, a standard rule-based control is reactive, making the installation ‘lag behind’ in relation to the demand. A model predictive control (MPC) might be a solution. To implement an MPC control first a suitable model must be identified for reliable predictions of room temperature and CO₂ concentration. For CO₂ predictions three scenarios are proposed respectively counting camera, lecture schedule and motion sensor. Two model identification techniques are evaluated: ARX and RC models. For identifying the heating dynamics of the case study building the 3 state RC model showed a good performance, a 5 step ahead prediction on a 15 minute time interval indicated a RMSE of approximately 0.60 °C. The 3^rd order ARX model indicated similar results, however the cross validation demonstrated that the RC model outperforms the ARX model. For CO₂ predictions the counting scenario resulted in the most accurate n-step ahead predictions. The RMSE found for the RC model is at maximum 90 ppm while 140 ppm for the ARX model. RC models are recommended for modelling all-air HVAC systems attributed by the higher prediction accuracy over ARX models. In addition, these models still contain physical parameters compared to ARX models.