A method based on GA-LSSVM for COP prediction and load regulation in the water chiller system

Abstract

In this paper, a method based on least square support vector machine (LSSVM) and genetic algorithm (GA) is applied for the coefficient of performance (COP) prediction and the load regulation of each chiller in the water chiller system. In order to show the generalizability of this method, two twin-screw water chiller systems with different nominal cooling capacities applied in different situations are studied. The proposed model uses two compressors’ load rate, inlet temperature of cooling water, outlet temperature of cooling water, inlet temperature of condensing water and outlet temperature of condensing water as input parameters. COP is used as the output parameter. To increase the accuracy of the model, more than 10,000 on-site testing data points are randomly divided into the training set and the testing set for each case. The results show that this GA-LSSVM-based model is accurate enough for COP prediction. For the first case, 98.05% of total points locate within the ± 5% lines and determination coefficient is 0.9835. For the second case, 99.66% of total points locate within the ± 5% lines and determination coefficient is 0.9907. Based on the proposed model with high precision, two different typical working conditions are used for two cases to develop the control strategy of each chiller’s load regulation, which is significantly helpful to improve the performance of the water chiller system.