Model predictive control for energy-efficient optimization of radiant ceiling cooling systems

Abstract

State-of-the-art model predictive control (MPC) applications have been performed in various heating and cooling systems of buildings, such as fan coil unit systems and radiant floor heating systems. However, there is also a significant potential for improved zone air temperature control for better thermal comfort and efficient energy savings in increasingly prosperous radiant ceiling cooling systems. In this research, the physics-based model was developed by applying the building envelope configuration and material property information. The reduced-order model of the original radiant ceiling cooling system model was then simplified using the balanced truncation method for reducing computational cost and maintaining a comparative model accuracy. The model predictive control of radiant ceiling cooling systems was proposed for zone air temperature tracking, allowing the system to be more robust and adaptive to external thermal disturbances such as solar radiation and ambient temperature. The superior performance of model predictive control in terms of accurate zone air temperature tracking and energy efficiency was evaluated by a simulation compared to PID control and conventional bang-bang control in both continuous and intermittent operation. The proposed model predictive control can achieve 21%–27% and 6% energy saving efficiency, compared with PID control and conventional bang-bang control, respectively. And there is rarely any overshoot or steady-state error presence for the zone air temperature, which demonstrated the significant potential of the robust model predictive control for better thermal comfort and efficient energy savings in growing prosperous radiant ceiling cooling systems.