Applying Genetic Algorithm to Optimal Loading for RTU Systems
碩士 === 國立臺北科技大學 === 能源與冷凍空調工程系 === 106 === In the research site installed the rooftop unit variable refrigerant flow (RTU+VRF) system, which has been an alternative to chilled water air conditioning systems for hypermarkets. The primary advantage of the RTU+VRF system is its compactness. Chilled wat...
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ndltd-TW-106TIT057030292019-07-25T04:46:50Z http://ndltd.ncl.edu.tw/handle/9f6ra5 Applying Genetic Algorithm to Optimal Loading for RTU Systems 應用基因演算法於RTU空調系統之負載分配最佳化 Li-Kang Su 蘇立康 碩士 國立臺北科技大學 能源與冷凍空調工程系 106 In the research site installed the rooftop unit variable refrigerant flow (RTU+VRF) system, which has been an alternative to chilled water air conditioning systems for hypermarkets. The primary advantage of the RTU+VRF system is its compactness. Chilled water systems require an engine room for chiller units, as well as a space to install chilling towers. In addition, rooms for air handling units may become considerable space demands depending on the volume of the site. By contrast, an RTU+VRF system requires no more than a space for the installation, reducing the need for an engine room and rooms for air handling units. When the air conditioning demand of a site is substantial, the use of the RTU+VRF system conserves considerably large space, which can then be used as additional parking spaces or concession stores to increase the incomes of the hypermarket. iii A regression analysis method was used to establish a model. Equal load distribution (ELD) and genetic algorithms (GA) were adopted to optimize the load distribution. Under the purpose of minimizing the total power consumption, optimal dispatch of each of the RTUs were performed. The results revealed that the adoption of genetic algorithms was more energy-efficient in every loading level compared with equal load distribution. The experimental results indicated that the energy conservation rate was the highest when the load rate of the RTU+VRF system was approximately 70%. Yung-Chung Chang 張永宗 2018 學位論文 ; thesis 57 zh-TW |
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碩士 === 國立臺北科技大學 === 能源與冷凍空調工程系 === 106 === In the research site installed the rooftop unit variable refrigerant flow
(RTU+VRF) system, which has been an alternative to chilled water air conditioning
systems for hypermarkets. The primary advantage of the RTU+VRF system is its
compactness. Chilled water systems require an engine room for chiller units, as well
as a space to install chilling towers. In addition, rooms for air handling units may
become considerable space demands depending on the volume of the site. By contrast,
an RTU+VRF system requires no more than a space for the installation, reducing the
need for an engine room and rooms for air handling units. When the air conditioning
demand of a site is substantial, the use of the RTU+VRF system conserves
considerably large space, which can then be used as additional parking spaces or
concession stores to increase the incomes of the hypermarket.
iii
A regression analysis method was used to establish a model. Equal load
distribution (ELD) and genetic algorithms (GA) were adopted to optimize the load
distribution. Under the purpose of minimizing the total power consumption, optimal
dispatch of each of the RTUs were performed. The results revealed that the adoption
of genetic algorithms was more energy-efficient in every loading level compared with
equal load distribution. The experimental results indicated that the energy
conservation rate was the highest when the load rate of the RTU+VRF system was
approximately 70%.
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author2 |
Yung-Chung Chang |
author_facet |
Yung-Chung Chang Li-Kang Su 蘇立康 |
author |
Li-Kang Su 蘇立康 |
spellingShingle |
Li-Kang Su 蘇立康 Applying Genetic Algorithm to Optimal Loading for RTU Systems |
author_sort |
Li-Kang Su |
title |
Applying Genetic Algorithm to Optimal Loading for RTU Systems |
title_short |
Applying Genetic Algorithm to Optimal Loading for RTU Systems |
title_full |
Applying Genetic Algorithm to Optimal Loading for RTU Systems |
title_fullStr |
Applying Genetic Algorithm to Optimal Loading for RTU Systems |
title_full_unstemmed |
Applying Genetic Algorithm to Optimal Loading for RTU Systems |
title_sort |
applying genetic algorithm to optimal loading for rtu systems |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/9f6ra5 |
work_keys_str_mv |
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