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基于动态自适应图神经网络的电动汽车充电负荷预测
作者:
作者单位:

1.河南大学人工智能学院,河南省郑州市 450046;2.河南省车联网协同技术国际联合实验室,河南省郑州市 450046

摘要:

电动汽车充电站负荷波动的不确定性与长时间预测任务给提升充电负荷预测精度带来巨大的挑战。文中提出一种基于动态自适应图神经网络的电动汽车充电负荷预测算法。首先,构建了一个充电负荷信息时空关联特征提取层,将多头注意力机制与自适应相关图结合生成具有时空关联性的综合特征表达式,以捕获充电站负荷的波动性;然后,将提取的特征输入到时空卷积层,捕获时间和空间之间的耦合关系;最后,通过切比雪夫多项式图卷积与多尺度时间卷积提升模型耦合长时间序列之间的能力。以Palo Alto数据集为例,与现有方法相比,所提算法在4种波动情况下的平均预测误差大幅降低。

关键词:

基金项目:

国家自然科学基金资助项目(62176088);河南省科技攻关项目(232102211034);全国博士后交流计划引进项目(YJ20220262)。

通信作者:

作者简介:

张延宇(1980—),男,博士,副教授,硕士生导师,主要研究方向:能源互联网、电动汽车充电优化调度、负荷预测等。E-mail:zyy@henu.edu.cn
张智铭(1998—),男,通信作者,硕士研究生,主要研究方向:电动汽车充电负荷预测、电动汽车充电调度等。E-mail:104754222770@henu.edu.cn
刘春阳(2000—),女,硕士研究生,主要研究方向:电动汽车充电负荷预测、电动汽车充电调度等。E-mail:104754211275@henu.edu.cn


Electric Vehicle Charging Load Prediction Based on Dynamic Adaptive Graph Neural Network
Author:
Affiliation:

1.School of Artificial Intelligence, Henan University, Zhengzhou 450046, China;2.International Joint Laboratory of Henan Province Vehicle Networking Collaborative Technology, Zhengzhou 450046, China

Abstract:

The uncertainty and long-term prediction of the load fluctuation of electric vehicle (EV) charging stations pose significant challenges to accurately predict the charging load. An EV charging load prediction based on dynamic adaptive graph neural network is proposed. Firstly, a spatiotemporal correlation feature extraction layer for charging load information is constructed. By combining multi-head attention mechanism with adaptive relevance graph, a comprehensive feature representation with spatiotemporal correlation is generated to capture the load fluctuation of EV charging station. Then, the extracted features are input into a spatiotemporal convolutional layer to capture the coupling relationship between time and space. The ability of the model to couple long time series is enhanced by Chebyshev polynomial graph convolution and multi-scale temporal convolution. The effectiveness of the algorithm has been verified using two real datasets. Taking the Palo Alto dataset as an example, compared with existing methods, the average prediction error of this algorithm under 4 volatile conditions is reduced sharply.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 62176088), Program for Science & Technology Development of Henan Province (No. 232102211034), and Introduction Project of National Postdoc Exchange Plan (No. YJ20220262).
引用本文
[1]张延宇,张智铭,刘春阳,等.基于动态自适应图神经网络的电动汽车充电负荷预测[J].电力系统自动化,2024,48(7):86-93. DOI:10.7500/AEPS20230611001.
ZHANG Yanyu, ZHANG Zhiming, LIU Chunyang, et al. Electric Vehicle Charging Load Prediction Based on Dynamic Adaptive Graph Neural Network[J]. Automation of Electric Power Systems, 2024, 48(7):86-93. DOI:10.7500/AEPS20230611001.
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  • 收稿日期:2023-06-11
  • 最后修改日期:2023-12-04
  • 录用日期:2023-12-05
  • 在线发布日期: 2024-04-01
  • 出版日期: