| Summary: | 碩士 === 國立臺北科技大學 === 冷凍空調工程系所 === 94 === This indicates that overall functioning efficiency of air-conditioning system is closely related to the efficiency of Chiller unit. The purpose of the Optimal Chiller Loading (OCL) is to meet the system load and to decide the chillers’ optimal part load ratios (PLR) to reduce the system power consumption. The optimal chiller loading methods include Average Loading (AVL) method, Largrangian Multiplier (LGM) method and Genetic Algorithm (GA) at present. These methods have some shortcomings. Such as AVL method being not optimal. LGM method will diverge if the initial condition isn’t suitable.
Although the GA method overcomes the shortcoming of Lagrangian method and produces results with high accuracy, the process of evolution is very complicated and makes the coding of program be difficult. This paper presets a method by using Evolution Strategy （ES） to improve these defects. Since ES only use the mechanisms of mutation and competition, leaving out all other encoding, decoding and crossover operations in the GA approach, it thus can not only reduce error, but also enable easy implementation. Besides, because the number of chillers in an air-conditioning system is not large, the convergence speed is enough to meet the requirement and saves us from the computation of selection and competition in (μ +λ) −ES when using (1+1)-ES to conduct OCL. Thus, this research will use the (1+1)-ES method. This thesis uses a cubic equation to simulate the chiller’s kW-PLR curve and to find a set of chiller output which doesn’t violate the operating limits while minimizing the objective function. The ES is adopted to find the near optimal solution of the function.
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