Experimental study of a model predictive control system for active chilled beam (ACB) air-conditioning system

Abstract

Active chilled beams (ACB) are gaining popularity worldwide as a potentially energy-efficient air-conditioning technology for buildings. However, the control of ACB system is challenging, as it needs to handle multiple cooling coils and the relatively slow response to cooling load dynamics. This paper reports the implementation of a model predictive control (MPC) system for an ACB system, which employs a linear white-box building model for building energy and indoor condition predictions. A multiple-objectives function is employed in the MPC controller to optimize the energy efficiency in air-conditioning system and indoor thermal comfort while fulfilling the constraints of indoor comfort range (−0.5 < PMV < 0.5). The MPC controller was implemented in the BCA SkyLab test facility in Singapore. The control characteristics and performance of the MPC controller in controlling a conventional fan coil unit (FCU) and an ACB system were investigated experimentally. A conventional feedback control based building management system (BMS) originally installed in the SkyLab test facility was employed as a reference for comparison with the MPC controller. Compared to the original BMS, the MPC controller achieved 14.7% and 20% electricity savings for the conventional FCU and ACB systems, respectively. The MPC controller also kept the indoor PMV within acceptable thermal comfort range at all time. This study demonstrated the real-time multi-objectives optimization capability of the MPC controller in enhancing the performance of ACMV systems.