Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices

Developing effective operational strategies for commercial buildings is a high priority as the global community seeks to reduce energy usage and greenhouse gas emissions. This study aims to validate the feasibility of a model predictive control (MPC) strategy for commercial building in response to occupancy variations and time-variant electricity prices in comparison to a conventional rule-based control (RBC) strategy. The building energy system included an air-cooled chiller, stratified chilled water thermal energy storage, two fan coil units, three heat exchangers, and five pumps. The optimal operations of the chiller and storage system were determined with the goal of minimizing the operating cost while maintaining the zone temperature at a cooling set point temperature during cooling operation hours. Artificial neural network was utilized as prediction models and metaheuristics algorithm was employed as optimization solver to construct a reliable and computationally manageable MPC controller. The simulation was performed for four days during the cooling season with a confirmed optimal prediction time horizon of 24 h and a control timestep of 1 h intervals. In conclusion, MPC reduced the total operating cost by 3.4% compared to the RBC, which prioritized the storage system operation to manage the thermal load.