电测与仪表2025，Vol.62Issue(3)：20-29,10.DOI:10.19753/j.issn1001-1390.2025.03.003
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
摘要
Abstract
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Key words
electric vehicle/load forecasting/VMD/fuzzy entropy/NSGAⅡ/NAHL neural network分类
信息技术与安全科学引用本文复制引用
郭鑫喆,王业琴,王超,吴明江,杨艳,张楚..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[J].电测与仪表,2025,62(3):20-29,10.基金项目
国家自然科学基金资助项目(62303191,62306123) （62303191,62306123）
