Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system

Abstract

Using separate cooling coils for sensible and latent loads provide extra control flexibility to optimise the energy efficiency and comfort in air-conditioning and mechanical ventilation (ACMV) systems. A popular implementation of such technology is dedicated outdoor air system (DOAS)-assisted separate sensible and latent cooling (SSLC) systems. However, a sophisticated control technique is needed to coordinate the control of multiple cooling coils in such systems. This paper presents a novel model predictive control (MPC) developed for a DOAS-assisted SSLC system. The MPC adopts a linear state-space model that captures building thermodynamics, thermal comfort and ACMV for building response prediction and optimization. Subsequently, a multi-objective cost function is employed to optimize energy use and thermal comfort while fulfilling constraints of predicted mean vote (PMV) (-0.5, 0.5) and relative humidity (0%, 65%) in buildings. The performance of the MPC for controlling a conventional single-coil air-handling unit (AHU) system and a DOAS-assisted SSLC system is experimentally investigated and compared to a conventional feedback-control-based building management system (BMS). The MPC system achieved 18% and 20% electricity savings for the single-coil AHU and DOAS-assisted SSLC, respectively, as compared to the BMS controlled single-coil AHU. Furthermore, indoor thermal comfort is significantly improved, compared to the BMS. DOAS-assisted SSLC is shown to be advantageous compared to single-coil AHU to achieve better indoor environment in terms of thermal comfort and humidity, when both systems are controlled by MPC.