# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 6 Conclusions  
Traditional prediction methods often decompose the power itself to study its characteristics, neglecting the impact of the multidimensional weather environment on the decomposition. This paper proposes a short-term PV power forecasting method based on MVMD feature extraction and the Informer model, which has shown significant results through experimental validation. The main conclusions are as follows:  
1. The proposed segmented MIC correlation analysis strategy, which includes full-time and power-generation-time correlation analysis, effectively captures the nonlinear relationships between PV power and multidimensional weather factors. The analysis revealed that solar irradiance, total cloud cover, relative humidity, and surface temperature are the most influential factors in the experimental region. 2. A multivariate decomposition method based on MVMD is proposed. Compared to the VMD method, this method can perform synchronized time-frequency analysis of PV power generation and various meteorological factors, such as temperature, humidity, wind speed, and solar radiation intensity, decomposing them into frequency-aligned multivariate intrinsic modes. This leads to a more comprehensive and accurate decomposition of the PV power series (see Figures 3 and 4). 3. The Informer model enhanced with the Huber loss function excels at capturing long-term dependencies and handling large-scale time-series data, improving prediction accuracy and robustness. Experimental results show that the RMSE, MAE, and MAPE values of the MVMD-Informer model are 0.939 MW, 0.758 MW, and 4.31%, respectively. Compared to the CNN-LSTM model, RMSE is reduced by 81.67%, MAE by 79.92%, and MAPE by 43.40%. Compared to the Informer model, RMSE is reduced by 62.48%, MAE by 52.18%, and MAPE by 20.92%. 4. The MVMD-Informer model demonstrates notable advantages in computational efficiency. While the inclusion of the MVMD process slightly increases training time, it significantly enhances prediction accuracy. Compared to the MVMD-CNN-LSTM model, the training time is reduced by about 13%. Compared to models without MVMD, such as WaveNet, the Informer model's training efficiency improves by at least 15%. These results indicate that the self-attention mechanism of the Informer model improves both prediction accuracy and computational efficiency. 5. In summary, the proposed short-term PV power forecasting method based on MVMD feature extraction and the Informer model has demonstrated excellent performance, achieving the expected research goals. This method effectively extracts key weather features, performs multivariate decomposition, and improves prediction accuracy and computational efficiency, showing extensive practical application potential in PV station monitoring systems, grid dispatch, energy management systems, and smart grids. Despite its significant advantages, there are potential limitations and challenges in its practical application. The quality and acquisition cost of high-quality meteorological and PV power data are crucial for model performance but can be difficult or costly to obtain in some regions. Furthermore, large-scale applications still require substantial computational resources, posing challenges for resource-constrained scenarios. As the PV industry develops, more data and richer meteorological information will become available, providing new opportunities to further optimize and enhance short-term PV power forecasting methods. **Author Contributions:** [PERSON] and [PERSON] analyzed the data and wrote the paper; [PERSON] and [PERSON] performed the simulation and modeling; [PERSON] and [PERSON] analyzed the results and reviewed the modeling and text; [PERSON] and [PERSON] prepared the data and contributed analysis tools. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Jiangsu Province International Cooperation Project, grant number BZ2021012. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.