Hybrid model based predictive control for temperature-humidity regulation in direct expansion air conditioning systems

Indoor thermal comfort and energy efficiency in direct expansion (DX) air conditioning (A/C) systems are critical yet often conflicting objectives. Conventional controllers prioritize temperature and humidity regulation without optimizing energy use. This study addresses this gap by proposing a model predictive control (MPC) strategy to balance thermal comfort and energy efficiency for a single DX A/C system. A hybrid modeling approach is developed to facilitate the design of MPC through integrating a white-box model for the DX cooling coil to capture the cooling and dehumidification characteristics and a gray-box model for the air-conditioned room to predict its thermal dynamics. Using the developed hybrid system model, two MPC schemes are designed: one targeting temperature regulation alone and another incorporating both temperature and humidity in the objective function. Validation demonstrates that the temperature-only MPC can accurately regulate the temperature but resulting in an undesirable humidity level and 28.6 %–36.8 % higher energy consumption. The inclusion of both temperature and humidity significantly reduces energy use while maintaining thermal comfort within a tight predicted mean vote (PMV) range of −0.2 to +0.2. The results highlight that simultaneous temperature-humidity optimization enhances energy efficiency and comfort, resolving the trade-off inherent in traditional systems. The key contribution lies in the novel hybrid modeling framework in MPC design and the demonstration of the superiority of the MPC for temperature-humidity regulation over the temperature-only MPC. This research advances DX A/C system control by providing a scalable, energy-conscious solution for sustainable building operational management.