华南理工大学学报（自然科学版）2025，Vol.53Issue(2)：58-67,10.DOI:10.12141/j.issn.1000-565X.240219
基于LSTM-FC模型的充电站短期运行状态预测
Short-Term Operation State Prediction of Charging Station Based on LSTM-FC Model
摘要
Abstract
The prediction of the number of available charging piles in public charging stations is of great signifi-cance 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 pre-dictive 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.关键词
LSTM神经网络/全连接网络/电动汽车/充电站运行状态Key words
long short-term memory neural network/fully connected network/electric vehicles/charging sta-tion operating status分类
交通工程引用本文复制引用
毕军,王嘉宁,王永兴..基于LSTM-FC模型的充电站短期运行状态预测[J].华南理工大学学报（自然科学版）,2025,53(2):58-67,10.基金项目
国家自然科学基金项目(72171019,72301020) Supported by the National Natural Science Foundation of China(72171019,72301020) （72171019,72301020）
