Abstract
In recent years, electric vehicles (EVs) have been widely used. A large number of EVs connected to the power grid will affect the economy and stable operation of the grid. Load prediction of EVs is the basis to solve the above problems. In practical application, the parameters of traditional model are difficult to obtain accurately and the calculation speed is slow. In order to solve this problem, a combination prediction method of electric vehicle charging load based on Monte Carlo method and neural network is proposed in this paper. Firstly, the Monte Carlo model is built to fit the electric vehicle charging load (EVCL) according to the user’s behavior characteristics. Then, the neural network is guided by the Monte Carlo model to learn the EVCL under different user behavior characteristics, and the mapping relationship from the basic data to the predicted load is established. Finally, the trained neural network model can realize the EVCL directly and quickly based on the basic data of EVs. The simulation results show that the proposed combined prediction method can realize the prediction of EVCL quickly. It is applicable to the daily total load prediction of large-scale electric vehicle cluster.
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