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

Xian Zhang, Ka Wing Chan, Hairong Li, Huaizhi Wang, Jing Qiu, Guibin Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9055130
Pages (from-to)3157-3170
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume51
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Convolutional neural network (CNN)
  • deep learning
  • driver behavior
  • electric vehicle (EV)
  • load forecast
  • queuing model

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this