Prediction of Vapor-Compressed Chiller Performance Using ANFIS Model

냉동기 성능 진단을 위한 적응형 뉴로퍼지(ANFIS) 모델 개발

  • 신영기 (세종대학교 기계공학과) ;
  • 장영수 (한국과학기술연구원 열유동제어 연구센터) ;
  • 김영일 (한국과학기술연구원 열유동제어 연구센터)
  • Published : 2001.11.01

Abstract

On-site diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for the purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

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