TY - JOUR
T1 - Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load with a Novel Queuing Model
AU - Zhang, Xian
AU - Chan, Ka Wing
AU - Li, Hairong
AU - Wang, Huaizhi
AU - Qiu, Jing
AU - Wang, Guibin
N1 - Funding Information:
Manuscript received January 31, 2019; revised November 26, 2019; accepted February 4, 2020. Date of publication April 2, 2020; date of current version May 18, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 51507103 and Grant 51707123, and in part by the Foundations of Shenzhen Science and Technology Committee under Grant JCYJ20170817100412438 and Grant JCYJ20190808141019317. This article was recommended by Associate Editor A. Katriniok. (Corresponding author: Huaizhi Wang.) Xian Zhang is with the School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.
Publisher Copyright:
© 2013 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Convolutional neural network (CNN)
KW - deep learning
KW - driver behavior
KW - electric vehicle (EV)
KW - load forecast
KW - queuing model
UR - http://www.scopus.com/inward/record.url?scp=85106497867&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2020.2975134
DO - 10.1109/TCYB.2020.2975134
M3 - Journal article
C2 - 32248136
AN - SCOPUS:85106497867
SN - 2168-2267
VL - 51
SP - 3157
EP - 3170
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 6
M1 - 9055130
ER -