Applying Genetic Algorithm to Optimal Loading for RTU Systems

• Main Authors: Li-Kang Su, 蘇立康
• Other Authors: Yung-Chung Chang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9f6ra5

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%.