结合注意力和多尺度特征的电动汽车负荷预测  

Electric vehicle load prediction combining attention and multi-scale features

作　　者：肖霞 马强[1] 杨震 Xiao Xia;Ma Qiang;Yang Zhen(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China)

机构地区：[1]西南科技大学信息工程学院,绵阳621010

出　　处：《电子测量技术》2025年第5期57-64,共8页Electronic Measurement Technology

基　　金：国家自然科学基金面上项目(62071170)资助。

摘　　要：针对电动汽车负荷随机性以及预测精度低的问题,在TCN基础上,提出一种结合变分模态分解、注意力机制和多尺度特征的电动汽车负荷预测模型(VMD-AM-MSF-TCNnet)。首先,采用鲸鱼优化算法结合变分模态分解将电动汽车负荷序列分解;其次,引入门控机制和双重注意力改进TCN残差块结构,把不同尺寸的改进TCN残差块与注意力相结合实现多尺度特征融合;最后,对负荷分量进行预测再重构得到最终结果。实验结果表明,所提模型相比原始TCN在RSE、RAE、R~2性能指标上均有所提升,该模型具有较好的预测效果。Aiming to address the problems of randomness and low prediction accuracy in electric vehicle charging load forecasting,the paper proposed a novel approach based on TCN.This approach integrated variational mode decomposition,attention mechanisms and multi-scale features,leading to the development of the electric vehicle load forecasting model,VMD-AM-MSF-TCNnet.Firstly,the proposed method utilizes variational mode decomposition,optimized using the whale optimization algorithm,to decompose the electric vehicle charging load sequence.Secondly,the method introduced a gating mechanism and dual attention mechanisms to enhance the residual blocks of the original TCN.The model achieved multi-scale feature fusion by combining the attention-enhanced outputs of improved TCN residual blocks of varying sizes.Finally,it finalized the prediction through the reconstruction of the load components.The experimental results indicate that the proposed model demonstrates improvements in the performance metrics of RSE、RAE and R 2 compared to the original TCN,showing that it has a good predictive performance.

关 键 词：TCN 变分模态分解 注意力机制 多尺度特征 鲸鱼优化算法 

分 类 号：TM715[电气工程—电力系统及自动化] TN-9[电子电信]