电动汽车的规模化发展及其充电设施的持续性建设严重威胁电力系统的稳定性，但是目前尚缺简便有效的电动车保有量和负荷预测方法。因此，建立基于综合预测的电动汽车保有量预测模型，应用灰色预测、反向传播（BP）神经网络以及长短时记忆（LSTM）网络3种预测模型对电动汽车保有量进行预测，获得单预测模型的预测结果，并利用熵权法对单预测模型预测结果分配权重，计算得到综合预测结果。建立基于蒙特卡洛算法的电动汽车负荷预测模型，在保有量预测的基础上，模拟电动汽车电池特征参数和用户出行习惯，对电动汽车无序充电行为进行预测，形成电动汽车日负荷曲线。最后，以某市电动汽车保有量及充电负荷数据验证所提模型的有效性。算例分析表明，所提综合预测模型比单预测模型具有更高的预测精度，负荷预测结果表明规模化电动汽车并网将给电力系统带来新的挑战。

The large-scale development of electric vehicle and the continuous construction of charging facilities seriously threaten the stability of the power system. However, there is still a lack of simple and effective methods for electric vehicle ownership and load prediction. Therefore, the prediction model of electric vehicle ownership based on the comprehensive prediction is established. Three prediction models of grey prediction, back propagation (BP) neural network and long-short term memory (LSTM) network are used to predict the electric vehicle ownership, and the prediction results of the single prediction model are obtained. The entropy weight method is used to assign weight to the prediction results of the single prediction model, and the comprehensive prediction results are calculated. The electric vehicle load prediction model based on Monte Carlo algorithm is established. On the basis of ownership prediction, the characteristic parameters of electric vehicle batteries and user travel habits are simulated to predict the disordered charging behavior of electric vehicle and to form the daily load curve of electric vehicle. Finally, the effectiveness of the proposed model is verified by the data of electric vehicle ownership and charging load in a city. The example analysis shows that the proposed comprehensive prediction model has higher prediction accuracy than the single prediction model, and the load prediction results show that the grid connection of large-scale electric vehicle will bring new challenges to the power system.