1.Matlab实现VMD-TCN-LSTM-MATT变分模态分解卷积长短期记忆神经网多头注意力多变量时间序列预测; 2.运行环境为Matlab2023及以上; 3.输入多个特征,输出单个变量,考虑历史特征的影响,多变量时间序列预测; 4.data为数据集,main1-VMD.m、main2-VMD-TCN-LSTM-MATT.m为主程序,运行即可,所有文件放在一个文件夹; 5.命...
1.Matlab实现VMD-TCN-LSTM-MATT变分模态分解卷积长短期记忆神经网络多头注意力多变量时间序列预测; 2.运行环境为Matlab2023及以上; 3.输入多个特征,输出单个变量,考虑历史特征的影响,多变量时间序列预测; 4.data为数据集,main1-VMD.m、main2-VMD-TCN-LSTM-MATT.m为主程序,运行即可,所有文件放在一个文件夹; 5....
VMD对光伏功率分解,TCN-LSTM模型对分量分别建模预测后相加 2.运行环境为Matlab2021a及以上; 3.数据集为excel(光伏功率数据),输入多个特征,输出单个变量,多变量光伏功率时间序列预测,main.m为主程序,运行即可,所有文件放在一个文件夹; 4.命令窗口输出R2、MSE、RMSE、MAE、ME等多指标评价; 参考文献(非复现) 程序设...
Matlab实现VMD-TCN-LSTM变分模态分解结合时间卷积长短期记忆神经网络多变量光伏功率时间序列预测; VMD对光伏功率分解,TCN-LSTM模型对分量分别建模预测后相加 2.运行环境为Matlab2021a及以上; 3.数据集为excel(光伏功率数据),输入多个特征,输出单个变量,多变量光伏功率时间序列预测,main.m为主程序,运行即可,所有文件放在...
基于VMD-TCN-LSTM模型的短期光伏功率预测 认领 Short-Term Photovoltaic Power Prediction Ba sed on VMD-TCN-LSTM Model 在线阅读 下载PDF 引用 收藏 分享 摘要 针对光伏发电易受气象因素影响导致发电功率不稳定问题,提出了一种基于变分模态分解(Variational Modal Decomposition, VMD)、时域卷积网络(Temporal ...
Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model ...
Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model ...
Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model ...
Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model ...
Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model ...