## 1 Introduction  
Air conditioning, lighting, and ventilation systems constitute approximately 50% of the total power consumption of a building. In particular, air conditioning systems have a considerable effect on energy consumption; hence, energy conservation control mechanisms for air conditioning systems are crucial [1]. Air conditioning systems in a mass building are typically centralized and comprise fan coil units (FCUs) in each area of the building and a chilled water system in the mechanical room [2]. The chilled water system provides chilled water to the cooling coils of the FCUs, and the water in the coils is subsequently converted to cold air that is distributed to each area of the building. In general, because of the change of seasons, chilled water systems are designed on the basis of the maximum load in summer. Therefore, such systems often operate under a partial load ratio, leading to energy waste due to poor operating efficiency. The coefficient of performance of a chilled water system is a critical parameter in evaluating the performance of chilled water systems. A higher coefficient signifies higher chilled water system performance. [PERSON] et al. [3] proposed usinga neural network and genetic algorithm optimization to enhance the operating efficiency of a chilled water system to achieve energy conservation. [PERSON] and [PERSON] [4] used a regression model to predict the performance of a chilled water system to optimize the control of the system. Numerous studies have focused on efficiency optimization control strategies for chilled water systems and cooling towers. For example, [PERSON] and [PERSON] [5] used load-based speed control to determine the optimal control frequency for cooling towers and chilled water pumps to optimize the operating efficiency of chilled water systems. In addition to achieving optimal efficiency for chilled water systems (by controlling their operation), effectively controlling chilled water systems (by using indoor heat demand responses) is another means of achieving cost reduction. [PERSON] et al. [6] proposed the use of a dynamic demand response controller to change the temperature settings of a chilled water system and control its loading during periods of peak power consumption. [PERSON] et al. [7] revealed that thermal comfort is a crucial factor influencing the control of heating, ventilation, and air conditioning (HVAC) systems because a user's thermal comfort demand directly affects HVAC system control. Therefore, effective control of indoor thermal comfort temperature contributes to the maintenance of environmental comfort and the reduction of FCU energy waste. Specifically, an optimization control strategy to balance between HVAC energy consumption and indoor thermal comfort is valuable. The most commonly cited experiments on the human perception of thermal comfort have been performed by [PERSON] (1982). His analysis indicated the sensation of human thermal comfort by six factors: Four physical variables, including air temperature, mean radiant temperature, air velocity, and air humidity, and two personal variables, including metabolic rate and clothing insulation. [PERSON] and [PERSON] [8] proposed the PMV model, which agrees well with high-quality field studies in buildings with HVAC systems, situated in cold, temperature, and warm climates, studied during both summer and winter. Model predictive control (MPC) is comprehensively used to determine indoor HVAC temperature settings and comfortable temperature values by employing optimal control methods to reduce energy consumption and maintain indoor thermal comfort. [PERSON] et al. [9] proposed a hierarchical control method that entails the use of a non-linear MPC strategy to predict control. The purpose of this method is to maintain indoor comfort while preventing an HVAC system from entering high-load operation and to optimize efficiency during low-load operation. [PERSON] et al.