Load Forecasting of Electric Vehicle Charging Station Based on Edge Computing

The randomness of charging behavior in time and space increases the difficulty of load forecasting of EV charging stations. In this paper, to improve the accuracy of load forecasting, by building a load prediction mode based on stacked auto encoder neural network(SAE) at the edge platform, and in-depth study of the interaction mode of EV and power grid. The model of electric vehicle charging pile load prediction based on SAE is introduced and the key factors affecting the charging station load, such as historical load, temperature, weather type, etc., are also considered. Finally, the short-term load forecasting of a practical charging station is realized and compares with DBN and ELM algorithm. The result shows the proposed approach can provide more accurate forecasting result, which benefits the stable operation pf power grid.