Zhang Xian, Chan Ka Wing, Li Hairong, Wang Huaizhi, Qiu Jing, Wang Guibin
IEEE Trans Cybern. 2021 Jun;51(6):3157-3170. doi: 10.1109/TCYB.2020.2975134. Epub 2021 May 18.
With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications.
随着电动汽车（EV）和快速充电技术的兴起，电动汽车负荷预测已成为电动汽车充电站（CS）规划者和运营者关注的问题。由于交通流（TF）的非平稳特性以及充电过程的不确定性，电动汽车充电负荷难以准确预测。在本文中，首先使用基于深度学习的卷积神经网络（CNN）预测交通流，然后评估不同的预测不确定性以制定交通流预测区间（PI）。接着，根据历史数据和所提出的混合模型计算电动汽车到达率。基于交通流预测和到达率结果，利用一种考虑充电服务限制和驾驶员行为的新型概率排队模型研究电动汽车充电过程，将交通流转换为充电负荷。使用实际交通流数据对所提出的模型进行评估，结果表明可以全面了解电动汽车充电负荷的不确定性，显示出其在实际应用中的巨大潜力。
