# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
For short-term forecasting (less than 6 h), reference [13] proposes a combined ultra-short-term PV power prediction method considering ground-based cloud images and chaotic characteristics, significantly improving prediction accuracy under sudden irradiance changes caused by moving clouds. For medium-to-long-term forecasting (more than 6 h), reference [14] presents a method based on copula functions and LSTM networks. This method effectively extracts key meteorological factors and deeply explores long-term dependencies within limited data samples, significantly enhancing prediction accuracy. Reference [15] introduces a new adaptive discrete gray model, which improves the accuracy and reliability of long-term PV power prediction by incorporating time power terms and periodic terms and optimizing using genetic algorithms. In recent years, data-driven PV forecasting methods have been extensively studied and applied. These methods integrate machine learning, deep learning, time-series analysis, and feature engineering for PV output prediction. Reference [16] proposes a deep learning model based on an intelligent fusion gate mechanism for a bimodal feature fusion network, aimed at short-term point-interval prediction of PV power. This model effectively extracts and fuses global and local features of PV power sequences, significantly improving prediction accuracy and stability. In terms of similar day data processing, reference [17] employs the K-means clustering method to select similar days, reduces multidimensional feature data through factor analysis, and finally models and predicts using the extreme gradient boosting (XGBoost) algorithm, significantly improving prediction accuracy. Combining time-frequency analysis, weather characteristics, and algorithm optimization, reference [18] uses variational mode decomposition (VMD) technology and K-means clustering for time-frequency analysis of weather-conditioned grouped datasets. The resulting sub-signal sequences are then predicted using an LSTM neural network model optimized by the grey wolf optimizer (GWO). Reference [19] employs multiscale permutation entropy (MPE) technology to quantitatively evaluate different weather patterns, using these patterns as input features to construct a prediction model with the least squares support vector machine (LSSVM). The study verifies LSSVM's performance advantage in handling small-scale dataset prediction tasks. However, detailed parameter tuning of the LSSVM model was not conducted in this study, which might affect the model's generalization performance. Reference [20] proposes a CNN-LSTM-Transformer hybrid model, where CNN extracts spatial features, LSTM extracts temporal features, and the Transformer's encoder-decoder strategy improves prediction accuracy by learning the combination of spatiotemporal features. For model capability enhancement, reference [21] introduces a hybrid model based on guided LSTM, using an improved chicken swarm optimization (ICSO) and predator-prey mechanism to further enhance particle search capabilities, optimizing neural network (NN) weights and biases to improve prediction accuracy. Addressing issues such as data redundancy in VMD, insufficient VMD accuracy, and limited generalization capability of LSSVM in PV power prediction models, reference [22] proposes an improved VMD (IVMD)-grey wolf optimizer least squares support vector machine (GLSSVM) method, combining grey relational analysis (GRA) and Kendall correlation coefficient (KCC). This integrated approach effectively reduces the complexity of raw data, improves VMD decomposition accuracy, and enhances the model's generalization capability by optimizing LSSVM parameters, thereby improving PV power prediction accuracy and reliability. In comparative optimization of various models, reference [23] proposes a meta-learning method that dynamically mixes the base predictions of multiple deep learning models to learn the conditions under which each model performs best, improving the deterministic prediction of PV systems one hour in advance.