Predictive Control Modeling of Regional Cooling Systems Incorporating Ice Storage Technology

Abstract

Due to the hot climate, energy consumption for refrigeration is significantly higher in the subtropical monsoon climate region. Combined with renewable energy and ice-storage technology, a model predictive control model of the regional cooling system was proposed, which was conducive to improving the flexibility of the regional cooling system and the ability of peak shifting and valley filling. In this model, an artificial bee colony (ABC) optimized back propagation (BP) neural network was used to predict the cooling load of the regional cooling system, and the model parameter identification method was adopted, combining utilizing a river-water-source heat pump and ice-storage technology. The results showed that the load prediction algorithm of the ABC-BP neural network had a high accuracy, and the variance coefficient of load prediction root-mean-square error (RMSE) was 16.67%, which was lower than BP, support vector regression (SVR), and long short-term memory (LSTM). In addition, compared with the three control strategies of chiller priority, ice-storage priority, and fixed proportion, the operation strategy optimized by the comprehensive model can reduce the average daily cost by 19.20%, 4.45%, and 5.10%, respectively, and the maximum daily energy consumption by 30.02%, 18.08%, and 8.90%, respectively.