# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
In terms of utilizing satellite cloud images, reference [24] presents a time-segmented PV power prediction model based on cloud images. This model improves short-term prediction methods and processes through real-time adjustment of clear-sky PV power estimates and cloud transmittance, achieving high prediction accuracy under various weather conditions and having potential application value in practical grid dispatching. The methods discussed provide an overview of various existing techniques for PV power prediction, including statistical, probabilistic, machine learning, and deep learning approaches. While these methods have significantly improved prediction accuracy, they still face notable limitations. Traditional methods typically focus on decomposing the PV power data itself without accounting for environmental conditions, leading to incomplete decomposition results. EMD and VMD are single-sequence decomposition methods, and traditional multivariate PV power and meteorological sequence decompositions require multiple applications of these algorithms. The frequencies of the decomposed subsequences cannot be aligned, preventing time-frequency synchronous analysis of multivariate sequences and reducing prediction accuracy. Additionally, as neural network models become more complex with increasing layers, they face issues like low feature learning efficiency, slow training speeds, and overfitting. These challenges hinder the accurate prediction of PV power generation, particularly when dealing with long sequence data and multivariate inputs. To address these issues, this paper proposes a short-term photovoltaic (PV) power generation forecasting method based on multivariate variational mode decomposition (MVMD) and the Informer model. First, the most relevant weather features to PV power are extracted through maximal information coefficient (MIC) correlation analysis and incorporated into the PV power prediction, enhancing the model's adaptability to environmental conditions. Next, to comprehensively describe the relationship between PV power and environmental conditions, MVMD is employed for multivariate variational mode decomposition [25; 26; 27; 28; 29]. This method performs time-frequency synchronous analysis on the PV power sequence combined with the weather data having the highest MIC correlation, obtaining intrinsic mode functions (IMFs) modes characterizing PV power features and corresponding weather feature IMFs modes. By obtaining these frequency-aligned multivariate intrinsic modes, multiple weather factors are introduced into the data decomposition-based prediction method, improving the completeness of data decomposition and the accuracy of predictions. Finally, the Informer neural network model is adopted in the prediction phase [30; 31]. Based on the respective power IMF sequences, combined with the relevant weather modes of each power IMF, separate Informer prediction models are constructed for training and prediction. The predicted results of different IMF modes are then aggregated to obtain the overall PV power generation forecast. The proposed method's effectiveness and accuracy are validated using historical data from a PV power station in Shengzhou, Zhejiang Province. In summary, this paper systematically extracts weather features related to PV power through MIC correlation analysis, employs MVMD for time-frequency synchronous analysis, and achieves the integration of PV power with multidimensional weather factors, thereby improving the completeness of data decomposition and the accuracy of predictions. Additionally, the combination with the Informer neural network model enhances the model's learning ability and prediction accuracy while significantly reducing computational complexity and memory requirements.