Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 2: 58 - 67

DOI: https://doi.org/10.12141/j.issn.1000-565X.240219

Short-Term Operation State Prediction of Charging Station Based on LSTM-FC Model

  • BI Jun ,
  • WANG Jianing ,
  • WANG Yongxing
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  • 1.School of Traffic and Transportation，Beijing Jiaotong University，Beijing 100044，China
    2.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport，Beijing Jiaotong University，Beijing 100044，China
毕军（1973—），男，博士，教授，主要从事大数据智能交通决策等研究。E-mail： jbi@bjtu.edu.cn

Received date: 2024-05-06

  Online published: 2024-08-23

Supported by

the National Natural Science Foundation of China(72171019)

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Abstract

The prediction of the number of available charging piles in public charging stations is of great significance for the formulation of intelligent charging recommendation strategy and the reduction of users’ charging queue time. At present, the research on the operating state of charging stations typically focuses on charging load forecasting, with relatively little attention given to the utilization of charging piles within the stations. Additionally, there is a lack of support from real-world data. Therefore, based on the actual operation data of charging stations, this paper proposed a prediction model of available charging piles in charging stations based on the combination of long short-term memory network (LSTM) and fully connected network (FC), which effectively combines the historical charging state sequence and related features. Firstly, the order data from a specific charging station in Lanzhou was transformed into the number of available charging piles, followed by data preprocessing. Secondly, an LSTM-FC-based predictive model for the operational status of the charging station was proposed. Finally, three parameters—input step size, number of hidden layer neurons, and output step size—were individually tested. To validate the predictive performance of the LSTM-FC model, it was compared with the original LSTM network, BP neural network model, and support vector regression (SVR) model. The results show that the mean absolute percentage error of LSTM-FC model is reduced by 0.247%, 1.161% and 2.204% respectively, which shows high prediction accuracy.

Key words： long short-term memory neural network; fully connected network; electric vehicles; charging station operating status

Cite this article

BI Jun , WANG Jianing , WANG Yongxing . Short-Term Operation State Prediction of Charging Station Based on LSTM-FC Model[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(2) : 58 -67 . DOI: 10.12141/j.issn.1000-565X.240219

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