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...
VMDFEPSOLSTMdeep learningThe motion response of offshore floating wind turbines significantly influences their structural integrity, power generation efficiency, operational complexity, safety, and stability. Therefore, predicting the motion response of offshore floating wind turbines ...
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 ...
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...
LSTM桥梁挠度预测为进一步提高桥梁挠度预测的准确性,文章提出一种结合CEEMDAN,VMD,PSO及LSTM的混合模型.试验结果表明,该混合模型在桥梁挠度预测上表现出色,与其他单一模型相比,具有更高的稳定性与精度,可为桥梁健康监测提供新的思路与方法,并为桥梁结构安全领域提供借鉴与指导.覃东...
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...