基于ASC-GCN-GRU的电动汽车充电站负荷预测

尚中雯; 张倩; 赵婵娟; 袁伟博; 丁津津

doi:10.19912/j.0254-0096.tynxb.2024-2361

PDF(2119 KB)

太阳能学报 ›› 2026, Vol. 47 ›› Issue (5) : 489-497. DOI: 10.19912/j.0254-0096.tynxb.2024-2361

基于ASC-GCN-GRU的电动汽车充电站负荷预测

尚中雯¹, 张倩¹, 赵婵娟², 袁伟博³, 丁津津³

作者信息 +

LOAD PREDICTION OF ELECTRIC VEHICLE CHARGING STATION BASED ON ASC-GCN-GRU

Shang Zhongwen¹, Zhang Qian¹, Zhao Chanjuan², Yuan Weibo³, Ding Jinjin³

Author information +

文章历史 +

摘要

针对新能源电力系统与电动汽车充电负荷的协同需求,提出一种属性特征增强-改进图卷积神经网络-门控循环神经网络（ASC-GCN-GRU）模型,用于新能源场景下充电站负荷的准确预测。首先针对传统方法预先构建固定邻接矩阵的局限性,引入自适应图生成（DAGG）模块动态挖掘充电站间的时空关联特征;结合节点自适应参数学习（NAPL）模块适配新能源场景下充电模型的多样性。其次,综合考虑温度、降水量等与新能源出力密切相关的环境因素以及日期和兴趣点（POI）等外部因素,结合充电站的时序负荷建立属性特征增强的节点特征。最后,引入交叉验证递归特征消除法（RFECV）筛选出ASC-GCN-GRU负荷预测模型性能最好的最优特征集。算例结果表明,所提出的ASC-GCN-GRU模型与GCN-GRU、门控循环神经网络（GRU）和麻雀算法优化极限学习机（SSA-ELM）相比,具有更好的预测性能。

Abstract

For the collaborative demand of new energy power system and electric vehicle charging load, this paper proposes an attribute-augmented spatiotemporal-graph convolutional network- gated recurrent unit is proposed neural network for charging station forecasting （ASC-GCN-GRU） model to accurately predict charging station load in new energy scenarios. Firstly, in view of the limitation of the traditional method of pre-constructing a fixed adjacency matrix, the data adaptive graph generation （DAGG） module is introduced to dynamically mine the spatio-temporal correlation features between charging stations. node adaptive parameter learning （NAPL） module is used to adapt to the diversity of charging models in new energy scenarios. Secondly, considering the environmental factors closely related to new energy output, such as temperature and precipitation, as well as external factors such as date and point of interest （POI）, the node features with enhanced attribute characteristics are established by combining the temporal load of the charging station. Finally, recursive feature elimination with cross-validation （RFECV） was introduced to select the optimal collection with the best performance of ASC-GCN-GRU load forecasting model. The numerical results show that the proposed ASC-GCN-GRU model has better prediction performance than the GCN-GRU, gated recurrent unit neural network （GRU） and sparrow search algorithm-extreme learning machine （SSA-ELM）.

导出引用

尚中雯, 张倩, 赵婵娟, 袁伟博, 丁津津. 基于ASC-GCN-GRU的电动汽车充电站负荷预测[J]. 太阳能学报. 2026, 47(5): 489-497 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2361

Shang Zhongwen, Zhang Qian, Zhao Chanjuan, Yuan Weibo, Ding Jinjin. LOAD PREDICTION OF ELECTRIC VEHICLE CHARGING STATION BASED ON ASC-GCN-GRU[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 489-497 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2361

中图分类号： TM715

参考文献

[1] 周喆, 黄婧杰, 周任军, 等. 考虑关键气象因素的时间卷积网络充电桩负荷预测[J]. 电力系统及其自动化学报, 2023, 35(5): 28-36.
ZHOU Z,HUANG J J, ZHOU R J,et al.Charging pile load prediction using temporal convolutional network with consideration of key meteorological factors[J]. Proceedings of the CSU-EPSA, 2023, 35(5): 28-36.
[2] 马苗苗, 任智伟, 刘立成, 等. 考虑新能源消纳的电动汽车有序充电控制策略[J]. 太阳能学报, 2024, 45(8): 94-103.
MA M M,REN Z W,LIU L C,et al.Orderly charging control strategy for electric vehicles considering new energy accommodation[J]. Acta energiae solaris sinica, 2024, 45(8): 94-103.
[3] 闫威, 李南, 沈月秀, 等. 基于CNN-GAN与半监督回归的电动汽车充电负荷预测[J]. 浙江电力, 2023, 42(2): 83-89.
YAN W,LI N,SHEN Y X,et al.Electric vehicle charging load forecasting based on CNN-GAN and semi-supervised regression[J]. Zhejiang electric power, 2023, 42(2): 83-89.
[4] 刘巍炜, 周羽生, 周文晴, 等. 考虑异方差性的城市电网电动汽车充电负荷预测[J]. 电力系统自动化, 2024, 48(15): 54-63.
LIU W W,ZHOU Y S,ZHOU W Q,et al.Charging load forecasting of electric vehicles in urban power considering heteroscedasticity[J]. Automation of electric power systems, 2024, 48(15): 54-63.
[5] 李波, 王宁, 吕叶林, 等. 考虑环境因素的电动汽车充电站实时负荷预测模型[J]. 同济大学学报(自然科学版), 2024, 52(6): 962-969.
LI B,WANG N,LYU Y L,et al.Real-time load prediction model of electric vehicle charging station considering environmental factors[J]. Journal of Tongji University (natural science edition), 2024, 52(6): 962-969.
[6] 陈晓华, 吴杰康, 张勋祥, 等. 基于鹈鹕优化算法优化广义回归神经网络的电动汽车充电负荷短期预测[J]. 山东电力技术, 2024, 51(7): 1-9.
CHEN X H,WU J K,ZHANG X X,et al.Electric vehicle charging load short-term prediction based on generalized regression neural network optimized by pelican optimization algorithm[J]. Shandong electric power technology, 2024, 51(7): 1-9.
[7] 胡博, 张鹏飞, 黄恩泽, 等. 基于图WaveNet的电动汽车充电负荷预测[J]. 电力系统自动化, 2022, 46(16): 207-213.
HU B,ZHANG P F,HUANG E Z,et al.Graph WaveNet based charging load forecasting of electric vehicle[J]. Automation of electric power systems, 2022, 46(16): 207-213.
[8] QIN Y, WANG J, REN S, et al.Prediction of EV random charging load based on monte carlo simulation method[C]//2023 3rd International Conference on New Energy and Power Engineering, Hangzhou, China, 2023: 295-298.
[9] 刘成. 基于ISSD-GRU模型的台区售电量预测方法[J]. 电工技术, 2024(11): 36-40.
LIU C.ISSD-GRU model-based electricity sale prediction[J]. Electric engineering, 2024(11): 36-40.
[10] 任东方, 马家庆,何志琴, 等. 基于AVMD-CNN-GRU-Attention的超短期风功率预测研究[J]. 太阳能学报, 2024, 45(6): 436-443.
REN D F,MA J Q,HE Z Q,et al.Research on ultra-short-term wind power porecast based on AVMD-CNN-GRU-Attention[J]. Acta energiae solaris sinica, 2024, 45(6): 436-443.
[11] LIU J, HUANG Y, YUAN H,et al.Residential electric vehicle charging load prediction method based on DBO-ELM[C]//2024 IEEE 4th New Energy and Energy Storage System Control Summit Forum, Hohhot, China, 2024:133-137.
[12] 朱子意, 孙晓燕, 柳先彪, 等. 基于相似用电单元及图卷积神经网络的电力负荷预测[J]. 电力科学与工程, 2023, 39(7): 9-23.
ZHU Z Y,SUN X Y,LIU X B,et al.Power load forecasting based on similar power units and graph convolutional neural network[J]. Electric power science and engineering, 2023, 39(7): 9-23.
[13] 王印松, 吕率豪. 基于改进时间卷积网络的微电网超短期负荷预测[J]. 太阳能学报, 2024, 45(6): 255-263.
WANG Y S,LYU S H.Ultra-short-term power load prediction of micro-grid based on improved temporal convolution network[J]. Acta energiae solaris sinica, 2024, 45(6): 255-263.
[14] 朱力, 李成, 郭龙, 等. 基于GCN-GRU的短期时空负荷预测方法[J]. 能源与环保, 2022, 44(4): 211-215,221.
ZHU L, LI C, GUO L, et al.Spatio-temporal load forecasting method based on GCN-GRU[J]. China energy and environmental protection, 2022, 44(4): 211-215,221.
[15] BAI L, YAO L, LI C,et al.Adaptive graph convolutional recurrent network for traffic forecasting[J]. Advances in neural information processing systems, 2020, 33: 17804-17815.
[16] ZHU J, YANG Z, MOURSHED M,et al.Electric vehicle charging load forecasting:a comparative study of deep learning approaches[J]. Energies, 2019, 12(14): 2692.
[17] 陈庆明, 廖鸿飞, 孙颖楷, 等. 基于GWO-GRU的光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 438-444.
CHEN Q M,LIAO H F,SUN Y K,et al.Photovoltaic power prediction model based on GWO-GRU[J]. Acta energiae solaris sinica, 2024, 45(7): 438-444.
[18] 吴军英, 路欣, 刘宏, 等. 基于Spearman-GCN-GRU模型的超短期多区域电力负荷预测[J]. 中国电力, 2024, 57(6): 131-140.
WU J Y, LU X, LIU H,et al.Ultra-short-term multi-region power load forecasting based on Spearman-GCN-GRU model[J]. Electric power, 2024, 57(6): 131-140.
[19] 刘栋, 魏霞, 王维庆, 等. 基于VMD-WPE和SSA-ELM的短期风电功率预测研究[J]. 太阳能学报, 2022, 43(12): 360-367.
LIU D,WEI X,WANG W Q,et al.Short term wind power forecasting based on VMD-WPE and SSA-ELM[J]. Acta energiae solaris sinica, 2022, 43(12): 360-367.