A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction

Short-term load forecasting is an important issue in energy management system and a key measure to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. Specifically, load forecasting at different time scales has various and corresponding roles in power systems. In recent years, plug-in electric vehicles have gradually become popular in major cities, and the number of electric vehicles is growing rapidly. However, the mass roll out of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviours of the EVs drivers. The accurate model of EVs charging load forecasting is therefore an emerging topic. In this paper, artificial neural networks and a long short term memory model based deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the long short term memory model has demonstrated better performance and provided a model of higher accuracy in short-term EVs load forecasting comparing with the traditional artificial neural networks.