Improved Multi-Objective Coati Optimization Algorithm for Optimizing Energy Efficiency and Thermal Comfort in Chilled Water Systems

Abstract As the main energy-consuming component of the central air conditioning system, energy saving of the chilled water system is particularly crucial. This system realizes heat exchange with indoor air by delivering chilled water to the air-conditioning end equipment, thus effectively regulating the indoor temperature and humidity and ensuring the comfort of the indoor environment. In this paper, an improved multi-objective coati optimization algorithm (IMOCOA) is used to optimize the operating parameters and thermal comfort environment parameters of chilled water systems to improve thermal comfort and reduce energy consumption. The algorithm introduces chaotic mapping to increase search diversity, balances global and local search capabilities through Levy flight and Gauss variation update strategies, and uses location greedy choice to help individual coatis jump out of the local optimal. To verify the optimization effect of IMOCOA, the energy consumption model of chilled water system and the simplified geothermal comfort model are established, and the multi-objective optimization model was established with these two models as the objective functions. IMOCOA is used to optimize the chilled water supply temperature, chilled water pump speed ratio, indoor temperature, and relative humidity. Simulation results from the experimental equipment of the central air conditioning system in a laboratory show that the average energy saving rate of the chilled water system using IMOCOA is 7.8%, and the thermal comfort is increased by 19.6%. Compared to other optimization algorithms, this method has better optimization effect.