| Summary: | 碩士 === 國立臺北科技大學 === 能源與冷凍空調工程系 === 107 === Manual method currently used is not efficient, resulting in wasted large amounts of energy. If we can use optimal chiller loading method to meet the system requirements , the total power consumption of the chillers will be minimized.
This study uses the cascade forward backpropagation network to establish chiller power consumption models, which considers the operating constrains of each chillers. Simulated annealing is also integrated while satisfying cooling load conditions to optimize chiller loading.The simulation result show that integrating cascade forward backpropagation network and simulated annealing between 95% and 55% cooling loads improved power saving compared to manual method load distribution, saving maximum total power consumption by approximately 19% in75% load. Compared with the application of cascade forward backpropagation network integrated with genetic algorithm, the maximum error is about 3.4%, The results of the two algorithms are not much different, but the calculation time difference is about more than 1 minute.
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