基于多目标模态分解与NAHL神经网络的电动汽车充电负荷预测方法  

Electric vehicle charging load prediction method based on multi-objective modal decomposition and NAHL neural network

作　　者：郭鑫喆 王业琴 王超[3] 吴明江 杨艳 张楚 GUO Xinzhe;WANG Yeqin;WANG Chao;WU Mingjiang;YANG Yan;ZHANG Chu(Faculty of Automation,Huaiyin Institute of Technology,Huai'an 223003,Jiangsu,China;Jiangsu Permanent Magnet Motor Engineering Research Center,Huai'an 223003,Jiangsu,China;Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 100038,China)

机构地区：[1]淮阴工学院自动化学院,江苏淮安223003 [2]江苏省永磁电机工程研究中心,江苏淮安223003 [3]中国水利水电科学研究院水资源研究所,北京100038

出　　处：《电测与仪表》2025年第3期20-29,共10页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(62303191,62306123)。

摘　　要：为提高电动汽车充电负荷预测精度,提出了一种基于多目标变分模态分解(variational mode decompo-sition,VMD)和具有增强隐藏层的自动人工神经网络(network with an augmented hidden layer,NAHL)的预测方法。文章采用模拟单点二进制交叉算子(simulated binary crossover,SBX)和线性递减的自适应变异策略(linear decreasing mutation,LDM)对NSGAII(non-dominated sorting genetic algorithm II)算法进行改进,称为NSGAII-LDSBX算法,利用改进NSGAII-LDSBX算法优化VMD的参数,将信号分解为若干个子序列,并通过模糊熵(fuzzy entropy,FE)对子序列进行重构;进一步使用NSGAII-LDSBX对NAHL模型进行优化,对各分量进行预测;以上海市嘉定区电动汽车充电站的负荷为例进行实验。分析表明:与其他模型相比,所提模型具有更好的预测精度,可有效预测电动汽车充电负荷。To improve the accuracy of electric vehicle charging load prediction,a prediction method based on multi-objective variational mode decomposition(VMD)and automatic artificial neural network with an augmented hidden layer(NAHL)is proposed.The non-dominated sorting genetic algorithm II(NSGAII)is improved by using the simulated binary crossover(SBX)and linear decreasing mutation(LDM),known as the NSGAII-LDSBX algo-rithm.The improved NSGAII-LDSBX algorithm is used to optimize the parameters of VMD,decompose the signal into several subsequences,and reconstruct the subsequences through fuzzy entropy(FE).Furthermore,the NS-GAII-LDSBX is used to optimize the NAHL model and predict each component.An experiment is conducted using the load of the electric vehicle charging station in Jiading District,Shanghai as an example.Analysis shows that compared with other models,the proposed model has better prediction accuracy and can effectively predict the char-ging load of electric vehicles.

关 键 词：电动汽车 负荷预测 变分模态分解 模糊熵 NSGAII NAHL神经网络 

分 类 号：TM561[电气工程—电器]