An improved DFVMD-LSTM hybrid time series model for predicting total solar irradiance

The prediction of total solar irradiance (TSI) time series holds significant importance in the study of solar activity and the assessment of solar energy resources. The variational mode decomposition-based long short-term memory (VMD-LSTM) model is a hybrid deep learning model that demonstrates high prediction accuracy on long-term time series. To address the information leakage issue faced by hybrid models based on VMD and other data preprocessing methods, this study proposes a prediction method for hybrid deep learning models called dualfusion variational mode decomposition (DFVMD), which modifies the VMD decomposition approach. The DFVMD-LSTM model utilizes multiple TSI datasets as model features, and multisource datasets and multiple model comparison experiments are employed to verify the applicability and robustness of the model. The experimental results show that the DFVDM-LSTM model significantly reduces the periodic TSI prediction deviation introduced by the LSTM model. Furthermore, regardless of the training period or prediction horizon, the DFVMD-LSTM model exhibits an average root mean square error (RMSE) reduction of 14.79% and an average mean absolute error (MAE) decrease of 21.50%, demonstrating the superior predictive performance and improved reliability of the DFVDM-LSTM method. Keywords: Total Solar Irradiance Deep Learning Time Series Prediction VMD LSTM