LSTM风速预测原始的风速序列是一种非线性和不平稳的风速序列,直接针对风速序列进行建模和预测精度不高.论文提出一种基于改进变分模态分解(Improved Variational Mode Decomposition,IVMD)的粒子群优化长短期记忆模型(Particle Swarm Optimization-Long Short-Term Memory,PSO-LSTM)网络相结合的方法对短期风速序列进行预测.I...
本发明公开了一种基于VMDPSOLSTM的临近空间80100km大气风速预报的建模方法,包括如下步骤:步骤1,风速数据预处理:步骤2,变分模态分解:步骤3,数据集构建:步骤4,采用PSO算法优化LSTM神经网络模型的参数:步骤5,IMF预测重构.本发明所公开的建模方法,采用变分模态分解算法(VMD)分解原始风速序列,该方法可以降低序列非平稳特性...
To cope with the nonlinear and nonstationarity challenges faced by conventional runoff forecasting models and improve daily runoff prediction accuracy, a hybrid model-based "feature decomposition-component prediction-result reconstruction" named VMD-LSTM-PSO was proposed. First, variational mode decomposition...
Addressing nonlinearities and non-stationarity of load prediction in metro power systems, this study introduces a novel hybrid prediction model that combines PSO with Bayesian optimization, integrated into a VMD-LightGBM-LSTM framework. Firstly, to reduce VMD decomposition residuals and enhance the ...
Prediction of 6-DOF motion response of semi-submersible floating wind turbine in extreme sea conditions using OVMD-FE-PSO-LSTM methodologydoi:10.1177/14750902241239361Huang, JiaruiSong, LeiYang, ZhuoyiWu, QilongJiang, XiaochenWang, ChengProceedings of the Institution of Mechanic...
针对桥梁运行阶段的健康状态监测,构建了CEEMDAN-VMD-PSO-LSTM模型对桥梁挠度进行预测.该模型主要分为二次模态分解平稳化,粒子群优化(PSO)算法和长短期记忆(LSTM)网络预测三大模块,共有5个步骤:①利用自适应噪声完备集合经验模态分解(CEEMDAN)算法对桥梁原始挠度序列进行初次模态分解,分解为若干本征模态分解函数(IMF);②...
本发明公开一种基于IVMDACMPSOCSLSTM组合电力负荷预测方法.该模型使用IVMD算法将负荷信号分解为多个IMF,将负荷信号减去所有IMF得到残差序列,IVMD的K值选取通过隐式反馈机制决定;然后将各序列分别建立LSTM子模型,采用滚动策略预测指定长度的负荷点序列,各子模型对应索引值相加得到负荷预测值通过ACMPSO算法求得LSTM最优参数...