# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 5 Case Study
### Informer PV Prediction  
The experimental environment for the Informer model includes Python 3.9, Pytorch 2.0.0 framework, Windows 11 system, with hardware configuration of 16 GB RAM, 12 th Gen Intel i7-12700H processor, and NVIDIA GeForce RTX 3060 workstation GPU. In the prediction phase, for each power IMF, an experimental dataset is built using weather multivariate intrinsic modes of the same frequency. These are then input into the prediction model to train and forecast the predicted results of each mode, which are combined to obtain the overall PV power generation. In this study, a single day from the experimental dataset is randomly selected as the target prediction day. The data from the preceding 50 days are partitioned into 80% for training, 10% for validation, and 10% for testing to initialize the model parameters. Given that the sampling frequency of the training data is every 15 min, the prediction step length is set to 96. The Adam optimizer is employed, with the learning rate adjusted within the [0, 1] range. Model parameters such as batch size, dropout rate, number of encoders, number of decoders, and the number of training epochs are fine-tuned based on the training dataset. By iteratively selecting different target prediction days and repeating the aforementioned process, a highly generalized model was ultimately developed for subsequent experiments. Table 2 presents the key parameter settings for the prediction model on the target prediction day. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Setting** \\ \hline batch\_size & 32 \\ dropout & 0.05 \\ learning\_rate & 0.0001 \\ enc\_in & 1 \\ dec\_in & 1 \\ n\_heads & 16 \\ e\_layers & 1 \\ s\_layers & \([3,2,1]\) \\ c\_out & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key parameter settings for the prediction model on the target day. Figure 4: IMF frequency distribution chart after MVMD. Based on Figures 5 and 6, the prediction curve of the proposed MVMD-Informer prediction model is the closest to the actual values, yielding the best predictive performance. Comparing the prediction performance across different neural networks, the inclusion of MVMD improves prediction accuracy in all cases, showing that MVMD achieves time-frequency synchronized analysis of PV power and meteorological sequences and performs multivariate decomposition. Therefore, it aligns more closely with the real operating conditions of distributed PV power plants, improving prediction accuracy. At the same time, the Informer model demonstrates advantages over other models in prediction accuracy and model size. The evaluation metrics for the prediction results are shown in Table 3.  
According to the evaluation metrics data in Table 3, the proposed MVMD-Informer model performed best in predicting PV power, with the lowest RMSE (0.939 MW), MAE (0.758 MW), and MAPE (4.31%). Notably, compared to the Informer model, the MVMD-Informer reduced RMSE by 62.48%, MAE by 52.18%, and MAPE by 20.92%. Compared to the CNN-LSTM model, the performance improvement was even more significant, with RMSE reduced by 81.67%, MAE by 79.92%, and MAPE by 43.40%. This demonstrates that the MVMD-Informer model combines the temporal decomposition capabilities of MVMD with the self-attention mechanism of the Informer, effectively capturing the dynamic characteristics and long-term dependencies of PV power. Due to the addition of the MVMD process, the training time of the MVMD-Informer model is longer than that of the Informer model alone. However, this additional complexity is reasonably compensated by a significant improvement in prediction accuracy. Compared to other models incorporating MVMD, the training time of the MVMD-Informer model is also shorter, reduced by approximately 13%. Furthermore, the training efficiency of the Informer model, compared to other models without MVMD, has increased by at least 15%. This indicates that the self-attention mechanism of the Informer model not only enhances prediction accuracy but also improves computational efficiency.