基于图时空神经网络的多充电站负荷协同预测方法

针对传统充电站负荷预测方法只能实现对单一站点预测的问题，提出一种基于图时空神经网络 （Graph Spatiotemporal Neural Network， GSTNN） 模型的多充电站负荷协同预测方法。定义时空信息图，描述充电站负荷之间的时空关系；构建时空特征提取网络，分别利用图卷积神经网络和门控序列卷积网络提取信息图的空间和时间维度信息，并使用长短期记忆网络 （Long Short Term Memory Networks，LSTM） 挖掘影响负荷预测的外部特征信息；融合提取的所有特征，进行负荷预测。算例结果表明，基于 GSTNN模型的方法能充分考虑时空特征和外部特征的影响，协同多个充电站的负荷数据进行预测，并同时输出各充电站的预测结果，有效提高预测准确度，有助于电网稳定运行。

Collaborative Load Prediction for Multiple Charging Stations Based on Graph Spatiotemporal Neural Network

Addressing the limitation of traditional charging station load prediction methods, which only forecast the load prediction of a single site, the paper proposes a collaborative forecasting method for multiple charging stations using the Graph Spatiotemporal Neural Network (GSTNN). Firstly, a spatiotemporal infographic is defined to describe the spatiotemporal relationship between charging station loads. Then, a spatiotemporal feature extraction network is constructed. It utilizes the graph convolutional neural network and the gated sequence convolutional network to extract spatial and temporal dimension information from the infographic. Furthermore, the Long Short-Term Memory Networks (LSTM) are used to mine external feature information that affects load prediction. Finally, all the extracted features are fused to predict the load.The results from the test cases show that the method based on the GSTNN model fully considers the influences of spatiotemporal characteristics and external features, cooperates with the load data of multiple charging stations for prediction, and produces results for each station concurrently, thereby effectively impoving prediction accuracy and supporting the stable operation of the power grid.