Applying Cat Swarm Optimization Algorithm to Optimal Chiller Loading

• Main Authors: Wu Cheng Xuan, 吳承軒
• Other Authors: 張永宗
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/949y63

碩士 === 國立臺北科技大學 === 能源與冷凍空調工程系 === 106 === The Optimal Chiller Loading (OCL) method include Average Loading Method (AVL), Lagrangian Multiplier Method (LGM), Genetic Algorithm (GA), Simulated Annealing Method (SA), Evolution Strategy method (ES) and Fruit Fly Optimization Algorithm (FOA) at present. Each of these method has its own advantages and shortcomings. For example, Average Loading method (AVL) may be the most popular used method but not the optimal one, the Lagrangian Multiplier (LGM) can not convergence. This study used Cat Swarm Optimization Algorithm (CSO) to solve the optimal chiller loading problem for reducing energy consumption. This paper compare three case use different methods for optimal chiller loading. The result showed the energy consumption can lowest in all case in Cat Swarm Optimization Algorithm. And the highest saving was 2.1% in the case1, 9.4% in the case2, 5.1% in the case3. The results indicate Cat Swarm Optimization Algorithm (CSO) used for optimal chiller loading was very well.