A data-driven energy management strategy based on performance prediction for cascade refrigeration systems. (April 2022) - Li, Yanpeng; Pan, Xi; Liao, Xinzhong; Xing, Ziwen
Highlights: A model library of refrigeration components for developing and testing control strategies is established. The first method based on data-driven model and knowledge-driven model is accurate enough for power prediction. The second method based on empirical formula, data-driven model and knowledge-driven model is used for COP prediction. GA-LSSVM is suitable to predict power, temperature difference and discharge pressure. The average optimized total power is 10.52% lower than the average tested total power. Abstract: The cascade refrigeration systems become the preferred choice in the field of cold storage due to its excellent performance under the condition of low evaporation temperature. Hence, a set of accurate performance prediction and energy management framework is essential for energy conservation and emission reduction. In this paper, a library of refrigeration components is established, based on which, two methods taking the data-driven model and the knowledge-driven model into account are presented to obtain optimal intermediate pressure in the cascade-condenser from the perspective of power and COP prediction respectively. Then, the effectiveness of the proposed optimal energy management strategy is demonstrated taking an actual CO2 /NH3 cascade refrigeration system as a case study. Ten parameters are used to train GA-LSSVM model and 13014 on-site testing data points are randomly divided into the training set and the testing set. Two empirical formulas are used to calculate the isentropic efficiency of the NH3 and the CO2 compressors. A thermodynamic model is used as the knowledge-driven model to connect the calculation of the high-temperature stage with that of the low-temperature stage. The results show that these two methods are accurate enough for power and COP prediction. Based on the proposed methods, a set of data (1440 data points) from a typical workday is used to show the improvement by applying the optimal energy management strategy and the results show that the power consumption can be reduced by 10.52%.