Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model

• Published in: IEEE transactions on cybernetics Vol. 51; no. 6; pp. 3157 - 3170
• Main Authors: Zhang, Xian, Chan, Ka Wing, Li, Hairong, Wang, Huaizhi, Qiu, Jing, Wang, Guibin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural network (CNN); Data models; Deep learning; driver behavior; electric vehicle (EV); Electric vehicle charging; Electric vehicles; Electrical loads; Forecasting; load forecast; Load forecasting; Load modeling; Predictive models; Probabilistic models; queuing model; Traffic flow; Uncertainty
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.2975134

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.