A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features

Abstract

Accurate power demand prediction of electrical vehicles (EVs) is crucial to power grid operation. To fully utilize the existing knowledge of EVs’ power demand and further improve the prediction accuracy, this paper proposes a hybrid method for power demand prediction of EVs based on Auto-Regressive Integrated Moving Average (SARIMA) and deep learning with the integration of periodic features. First, the general linear trend of power demand is extracted by SARIMA; then, the residual non-linear components are obtained by eliminating the linear trend from the original power demand. Meanwhile, the periodic features of the non-linear component are determined according to the periodic parameters of the SARIMA. Afterward, the non-linear components are approximated by Long-Short Term Memory (LSTM), and the periodic features of the non-linear components are taken as a part of the inputs of the LSTM. Finally, the extracted linear trend and the predicted non-linear components are combined to generate the final prediction results. To verify the effectiveness of the proposed method, three experiments are conducted on a real EV charging station. The experimental results indicate that the proposed method significantly improves the prediction accuracy compared with other popular data-driven models.