Energy-saving Diagnosis of Ground Water-source Heat Pump System Based on Artificial Neural Network

Abstract

Ground water-source heat pump(GWHP)system is currently widely used in China, because of high system energy-efficiency. However, due to the lack of effectively energy-saving diagnostic system, often leads to system operating efficiency be greatly reduced. According to the existing energy efficiency standards used in China, this paper proposes energy-saving state index for GWHP unit and its distribution system, establishes the relationship database between the system monitoring characteristic parameters and the system energy efficiency state, further develops the basic model of standard input about characteristic parameters samples and standard output about system energy-saving diagnosis. Based on the features of artificial neural network’s training & learning, memory & simulation, and non-linear approximation etc., the energy-saving diagnostic function of the GWHP system operation can be obtained, and the factors of system energy-efficient performance degradation can be monitored in real-time and the system energy-saving operation state can be controlled on line.