公共充电站可用充电桩数量预测对于制定智能充电推荐策略和减少用户的充电排队时间具有重要意义。现阶段充电站运行状态研究通常集中于充电负荷预测，对于站内充电桩占用情况的研究较少，同时缺乏实际数据支撑。为此，基于充电站实际运行数据，提出一种基于长短时记忆（LSTM）网络与全连接（FC）网络结合的充电站内可用充电桩预测模型，有效结合了历史充电状态序列和相关特征。首先，将兰州市某充电站的订单数据转化为可用充电桩数量，并进行数据预处理；其次，提出了基于LSTM-FC的充电站运行状态预测模型；最后，将输入步长、隐藏层神经元数量和输出步长3种参数进行单独测试。为验证LSTM-FC模型的预测效果，将该模型与原始LSTM网络、BP神经网络模型和支持向量回归（SVR）模型进行对比。结果表明：LSTM-FC模型的平均绝对百分比误差分别降低了0.247、1.161和2.204个百分点，具有较高的预测精度。

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.