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...
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...
LSTM网络被引入，其能够处理时序数据的短期和长期关系，进一步增强模型的预测性能。在这些基础层面上，MATT的加入尤为关键，它通过多头注意力机制对序列不同部分进行动态聚焦，确保模型能准确识别并利用序列中的关键信息。运行环境要求Matlab2023及以上版本，主程序main1-VMD.m和main2-VMD-TCN-LSTM-MATT.m可...
(Long Short Term Memory Network, LSTM)的短期光伏功率预测模型,以准确预测其发电功率,给电力系统的调度规划提供参考.上述模型首先使用VMD对光伏发电功率数据进行分解,得到多个不同频率的分量,再将光伏发电功率数据,分量数据以及气象数据输入TCN-LSTM,以对数据特征进行深度挖掘,经多次迭代最终输出预测结果.通过实验验证,...
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 ...
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 ...