基于堆叠异构空间图网络的电动汽车充电负荷预测

李璐; 王文龙; 刘楠; 杨威; 张佳夫; 王宇蛟

李璐,王文龙,刘楠,杨威,张佳夫,王宇蛟.基于堆叠异构空间图网络的电动汽车充电负荷预测[J].电测与仪表,2026,63(9):77-86.
Li lu,Wang wenlong,Liu nan,Yang wei,Zhang jiafu,Wang yujiao.Electric vehicle charging load prediction based on stacked heterogeneous spatial graph network[J].Electrical Measurement & Instrumentation,2026,63(9):77-86.

基于堆叠异构空间图网络的电动汽车充电负荷预测

Electric vehicle charging load prediction based on stacked heterogeneous spatial graph network

DOI：10.19753/j.issn1001-1390.2026.09.008

中文关键词: 电动汽车充电负荷预测时空图神经网络人工智能

英文关键词:electric vehicle, charging load prediction, spatiotemporal graph neural network, artificial intelligence

基金项目:国家电网公司科技资助项目（2023YF-138）

作者中文名	作者英文名	单位
李璐	Li lu	国网辽宁省电力有限公司阜新供电公司
王文龙	Wang wenlong	国网辽宁省电力有限公司阜新供电公司
刘楠	Liu nan	国网辽宁省电力有限公司阜新供电公司
杨威	Yang wei	国网辽宁省电力有限公司阜新供电公司
张佳夫	Zhang jiafu	国网辽宁省电力有限公司阜新供电公司
王宇蛟	Wang yujiao	沈阳工程学院

摘要点击次数: 72

中文摘要:

随着电动汽车渗透率的不断提高，充电设施和充电负荷将成为城市电网发展的关键增长点。准确预测电动汽车集中充电负荷的空间分布和时间变化对于确保城市电网的安全稳定运行至关重要。为此，提出了基于堆叠异构时空图网络（stacked heterogeneous spatio-temporal graph network, SHSGN）模型的电动汽车充电负荷预测方法，通过建模城市充电负荷的空间分布模式和用户多样化的充电方式，提升电动汽车负荷的预测精度，建立了表征充电站层级和区域层级负荷节点的双层异构图神经网络模型，通过强化表征充电负荷特征的跨区域负荷转移模式，提高挖掘负荷空间特征的能力，建立了多GRU并行解码神经网络模型，通过独立的解码任务准确表达每个充电站节点的差异化充电模式。以中国某城市的实际充电负荷数据为算例验证的结果表明，所提出的方法将充电负荷预测误差降至6.77%，相比传统方法提高了2.12%。

英文摘要:

With the increasing penetration of electric vehicles, charging facilities and load will become key growth areas for urban power grid development. Accurately forecasting the spatial distribution and temporal variation of electric vehicle charging load is crucial for ensuring the safe and stable operation of urban power grids. To address this, a forecasting method based on the stacked heterogeneous spatio-temporal graph network (SHSGN) model is proposed. This method improves the accuracy of electric vehicle load predictions by modeling the spatial distribution patterns of urban charging loads and the diverse charging behaviors of users. A two-layer heterogeneous graph neural network model is established to represent the charging station-level and regional-level load nodes. By enhancing the cross-regional load transfer patterns, this model improves the ability to capture spatial load characteristics. A multi-GRU parallel decoding neural network model is developed to accurately express the differentiated charging patterns of each charging station node through independent decoding tasks. Validation using real charging load data from a Chinese city demonstrates that the proposed method reduces the charging load prediction error to 6.77%, achieving a 2.12% improvement over traditional methods.

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