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- For almost a decade, intelligent system have shown great potentials in solving non-linear control problems. This paper exhibits a possibility of using a neuro-fuzzy to advise a chiller plant. This plant, located at the Building No.11 of Rangsit University, is consisted of three water chillers. Nonlinear dynamic characteristics, resulting from outdoor conditions, equipment within the plant such as pumps, modulation valve, are discussed herein. The objective of this research is to minimize energy cost while maintaining high performance of the plant. In this paper, identification and prediction of non-linear discrete time system using neuro-fuzzy system are investigated. To develop an algorithm for achieving such an objective, first step is to learn relationship of outdoor temperature, humidity, and cooling load of building that are the plant dynamics from historical data. After being trained completely, the neuro-fuzzy system is used to predict the cooling load at the Building No. 11Rangsit University and operating optimal points of the water chiller plant. We use the visual studio to develop an advised chiller plant management program. This program learns the information from both historical and present data of the chiller plant. After that, we compare energy cost between before (May-July 2014) and after (August-October 2014) using advisory management of chiller plant program. The system can be reduce electrical cost within 5 %. Furthermore, it is continuously trained by plant operators
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