| Summary: | 博士 === 國立臺北科技大學 === 機電科技研究所 === 99 === The Optimal Chiller Loading (OCL) method includes Average Loading Method (AVL), Lagrangian Method (LGM), Genetic Algorithm(GA) and Particle Swarm Algorithm (PSO) at present. Although the Genetic Algorithm method can overcome disadvantages that LGM can not obtain the analytic solution in the low-load, it is very complicated and difficult to make the coding of program. GA method is unable to find the optimal solutions.
This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it not only outperforms the genetic algorithm in finding optimal solution, but also overcomes the non-solutions of analytic solution, which caused by the Lagrangian method occurring at low demands. Therefore, this study uses the Lagrangian method with the on-off strategy to obtain the analytic solution in the low-load. However, the complicated computation process will not be suitable for the multiple-chiller system.
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