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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 CO 2  concentration. For CO 2  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 CO 2  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.

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