Electric Vehicle Charging Station Load Prediction Based on IWOA-LSSVM Combined Model with Improved Variational Mode Decomposition

Abstract

Accurately predicting the electric vehicle charging station load is the basis for the optimal design of power supply planning. Aiming at the problem that the load data of electric vehicle charging station has strong randomness and volatility due to the interaction of various factors. The electric vehicle charging load prediction model is constructed, which considers the highest temperature, day type and weather type factors to select similar days. Firstly, an algorithm for optimizing variational modal decomposition parameters with relative entropy is proposed to decompose the extracted similar daily charging load data into multiple sub-sequences. Then, the decomposed data are respectively input into the combined prediction model of the least squares support vector machine optimized by the whale algorithm improved by the adaptive probability threshold and the random difference mutation strategy, and finally the predicted value of each subsequence is superimposed and summed to obtain the final predicted value. Based on the measured charging load data and weather forecast data of an electric vehicle charging station in Shanghai, simulation experiments are carried out to compare and verify the accuracy and effectiveness of the model in this paper.