Abstract: As the scale of electric vehicle（EV） charging facilities keeps expanding, EV charging data can be obtained more conveniently. Some non-human factors will lead to the problems of missing data and abnormal data in the data set, which hinders the improvement of EV load forecasting accuracy. Therefore, this paper uses the gated recurrent unit neural network cells for imputation（GRUI） in the generative adversarial network（GAN） to deal with the irregular time-delay relationship between the previous and later observation data in the incomplete load data set, and a data imputation model based on GRUI-GAN is proposed to restore the EV load data. Then, the long short-term memory（LSTM） network with the Mogrifier gating mechanism is used for EV load forecasting. Finally, the experimental results show that the proposed method can generate new data with high accuracy to interpolate missing values, and the data restored by the proposed method can effectively improve the accuracy of EV load forecasting.