Since the difficulty of building mechanism model and the structure of COP model of chiller is complex

greatly affected by operating parameter

a COP prediction model of chiller is proposed based on Support Vector Regression

and the parameters are optimized by Particle Swarm Optimization algorithm. In this paper

396 sets of operating data of chiller of a shopping mall are randomly selected to train and test this model. The results shows that the prediction accuracy of SVR model based on PSO optimization algorithm is higher than that of BP neural network and the relative error is within 3%. At last

operating data of two days in summer and transition season are randomly selected to verify the model. The relative error is within 5%. So this model can provide theoretical basis for the chiller energy efficiency analysis

fault detection and diagnosis and optimizing control.