The HVAC (heating, ventilation and air-conditioning) system accounts for almost half of the total electricity consumption in buildings, and the chillers in HVAC system are the major energy consumers. Hence, it is significant to reasonably control the chillers for satisfying the thermal comfort demand of occupancy according to indoor environment change, and avoiding excessive cooling output and saving energy consumption. Equilibrium optimization (EO) algorithm is a novel physics-based meta-heuristics algorithm which originates from the dynamic mass balance equation. This paper attempts to optimize chiller load dispatch of HVAC system for energy conversation with EO algorithm. To validate the effectiveness of EO algorithm in solving optimal chiller loading (OCL) problem, three well-known cases are utilized to implement simulation experiments. Moreover, the effects of different particle number and parameter setting of EO algorithm on the optimization results are discussed. The experiment results demonstrate that EO algorithm can find out the optimal solution in solving OCL problem with less particles (the minimum equals 10). The optimal solutions are obtained in less than 200 iterations in case 1, 150 iterations in case 2, 50 iterations in case 3, respectively. Comparing with traditional optimization algorithms, the optimum power consumption using EO algorithm can obtain the best solutions, and simultaneously is better than AVL (Average Loading) and SA (Simulated Annealing) algorithm in case 1, AVL and GA (Genetic Algorithm) algorithm in case 2, AVL and GA algorithm in case 3, respectively. Moreover, Friedman test and Wilcoxon signed ranks test are also performed to prove efficiency of EO algorithm in solving OCL problem. In conclusion, EO algorithm is suitable for solving OCL problem and has the good performance.
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