Research on EV charging load forecasting and orderly charging scheduling based on model fusion.

The randomness and uncertainty of charging behavior of EV users bring great challenges to the short-term prediction of charging load of EV charging stations. Aiming at the low precision of short-term load forecasting of EV charging station under the influence of many factors, this paper takes the optimization of EV charging loss as the objective of optimization, firstly, the time series attributes are combined with the nonlinear attributes, and PLSR and LightGBM are used to extract the attributes from EV charging load series, and the fusion prediction model is established. Secondly, the Bayesian optimization method is introduced to optimize hyper-parameters of the model, and the self-adaptive adjustment of the hyper-parameters are realized, which improves performance of the algorithm and the prediction precision. Finally, taking the actual load data of EV charging pile in a certain area as an example, the experimental analysis is carried out, the second-order cone optimal dispatching scheme is adopted to optimize the EV charging schedule. The results show that the EV prediction model constructed by this paper has higher prediction accuracy than other artificial intelligence prediction algorithms, and provides more accurate data for the second-order cone optimal scheduling scheme, the utility model effectively realizes the safe and stable operation of the power grid. • The accuracy of EV load forecasting is improved by using fusion algorithm. • The generalization ability of prediction model is improved by Bayesian optimization algorithm. • It provides an accurate data base for EV optimal scheduling. [ABSTRACT FROM AUTHOR]