Genetic algorithm based optimal chiller loading for energy conservation

This study employs genetic algorithm (GA) to solve optimal chiller loading (OCL) problem. GA overcomes the flaw that Lagrangian method is not suitable as there is non-convex kW-PLR function in a system. This study uses the part load ratios (PLR) of chiller units to binary code chromosomes, and execute reproduction, crossover and mutation operation. Since the semiconductor plant is the largest a/c load for power consumption, it is used as an example in this paper. After analysis and comparison of the case study, we are confident to say that this method not only solves the problem of Lagrangian method, but also produces results with high accuracy within a rapid timeframe. It can be perfectly applied to the operation of air conditioning systems.