Heating, ventilation and air conditioning (HVAC) systems are responsible for maintaining occupants' thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system and inaccurate models

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... deteriorate the control performance whilst overly complicated models can lead to prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple setpoints (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this work we address the challenge of developing more reliable HVAC models for MPC controllers based on experimental data. Data is obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC testbed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC setpoints based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on data from the HVAC testbed.