黄健,陈建红,何剑杰,吴燕,万修,陈凡
HUANG Jian,CHEN Jianhong,HE Jianjie,WU Yan,WAN Xiu,CHEN Fan

• 1:国网浙江省电力有限公司兰溪市供电公司
• 2:浙江捷安工程有限公司
• 3:南京工程学院电力工程学院

摘要(Abstract)：

针对传统的电动汽车负荷预测方法未能充分利用电动汽车负荷之间的空间相关性，负荷预测精度较低的问题，提出一种基于GCN-LSTM（图卷积神经网络与长短期记忆网络）的电动汽车负荷预测方法。首先，构建图数据来描述充电站在地域上的分布，并使用GCN提取所研究充电站与相邻充电站之间的空间依赖信息；其次，将不同时刻GCN提取到的信息构成时间序列，输入LSTM网络，从而对电动汽车充电负荷进行预测。最后，以中国某市城区内的充电站负荷数据为例进行算例分析，结果表明所提出的方法能有效提高预测精度。
Traditional electric vehicle(EV) load forecasting methods often fail to fully utilize the spatial correlation among EV loads, resulting in low forecasting accuracy. To address this issue, a load forecasting method using graph convolution network-long short-term memory(GCN-LSTM) is proposed. Firstly, graph data is constructed to describe the distribution of charging stations in the region, and the spatial dependency information between the charging station under study and neighboring charging stations is extracted using a GCN. Secondly, the information extracted by the GCN at different time periods is formed into a time series and input into the LSTM to forecast the EV charging loads. Finally, the proposed algorithm is validated by using load data from charging stations in an urban area in China as an example. The results show that the proposed method can effectively improve forecasting accuracy.

关键词(KeyWords)： 电动汽车;负荷预测;时空相关性;图卷积神经网络;长短期记忆网络
EV;load forecasting;spatiotemporal correlation;GCN;LSTM

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（51577086）

作者(Author): 黄健,陈建红,何剑杰,吴燕,万修,陈凡
HUANG Jian,CHEN Jianhong,HE Jianjie,WU Yan,WAN Xiu,CHEN Fan

DOI: 10.19585/j.zjdl.202412006

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