Electrochemical Energy Storage Plants Costing Study Based on GWO-SVM Algorithm

Abstract

Establishing an accurate and reliable cost measurement model for energy storage plants is an important element in the pre-evaluation of energy storage plants. To this end, a cost measurement method for energy storage plants based on the Grey Wolf algorithm (GWO) optimized Support Vector Machine (SVM) is proposed. Using the GWO algorithm to optimize the penalty factor and kernel function of the SVM, and to establish a cost measurement model for energy storage plants on the basis of the GWO-SVM algorithm. Taking the historical data of storage power plant as an example, the prediction results of the GWO-SVM model are compared with those of SVM, ABC-SVM, CS-SVM and PSO-SVM models. According to the results, GWO-SVM model has a significant effect on improving the measurement accuracy of the cost of energy storage power plants.