山东电力技术2024，Vol.51Issue(7)：1-9,9.DOI:10.20097/j.cnki.issn1007-9904.2024.07.001
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
Electric Vehicle Charging Load Short-term Prediction Based on Generalized Regression Neural Network Optimized by Pelican Optimization Algorithm
摘要
Abstract
To solve the problem of insufficient prediction accuracy of electric vehicle charging load,a combined prediction method combining complementary ensemble empirical mode decomposition and pelican optimization algorithm to optimize generalized regression neural network was proposed.Firstly,the time series of electric vehicle charging load was decomposed into multiple intrinsic mode function components and a residual component by using complementary ensemble empirical mode decomposition.Then,considering that the decomposed intrinsic mode components are prone to redundant information,sample entropy was used to add and reconstruct the decomposed intrinsic mode components with similar values to reduce the degree of redundancy.Finally,considering that the prediction effect of the generalized regression neural network is closely related to the value of the smoothing factor,the pelican optimization algorithm was used to optimize the smoothing factor of the generalized regression neural network,and then the short-term prediction of the electric vehicle charging load was carried out.The simulation results show that the proposed prediction method can effectively improve the prediction accuracy of electric vehicle charging load with high practicability.关键词
广义回归神经网络/鹈鹕优化算法/电动汽车充电负荷/短期预测/互补集合经验模态分解Key words
generalized regression neural network/pelican optimization algorithm/electric vehicle charging load/short-term forecast/complementary ensemble empirical mode decomposition分类
信息技术与安全科学引用本文复制引用
陈晓华,吴杰康,张勋祥,龙泳丞,王志平..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):1-9,9.基金项目
国家自然科学基金项目(50767001). National Natural Science Foundation of China(50767001). （50767001）
