Model Predictive Control-based Thermal Comfort and Energy Optimization

This paper deals with the implementation of a Model Predictive Control (MPC) system for a classroom in a container building ventilation system and the associated indoor climate through controlling the airflow rate to the zone. A dynamic thermal model for the building system is formulated using the three resistors and two capacitors (3R2C) lumped capacitance method, and linearized using the Taylor's series expansion. This model is used for the proposed MPC implementation for thermal comfort management with energy optimization. Simulation results demonstrate the significance of the MPC controller in handling the constraints, multi-objective control, and producing optimal control strategy. The energy optimization results of the MPC have shown 31% of energy consumption reduction compared to a conventional controller.

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