Short-term load forecasting for the electric bus station based on GRA-DE-SVR

Abstract

With large-scale electric vehicles penetrating into power system, the grid will be faced with severe challenges. Accurate charging load forecasting is required to ensure the security and economy of the grid. Firstly, the factors that influence the daily load of electric bus stations are analyzed in this paper. Based on the grey relation theory, samples of similar days are selected to establish SVM prediction model. In order to improve prediction accuracy, differential evolution (DE) algorithm is applied to optimize parameters of SVR model. Through empirical study, the root mean square error (RMSE) of daily load forecasting is 10.85%. Compared with the standard SVM prediction model, the prediction precision of this paper is increased by 1.52%. What's more, the proposed method has better forecasting performance than the other methods.