# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Y.W)1 Suzhou Research Institute of Southeast University, Suzhou 215123, China; [EMAIL_ADDRESS]. In terms of prediction methods, PV power prediction can be categorized into four main types: (1) statistical and probabilistic methods such as autoregressive modeling (AR) [3]. Statistical methods are more suitable for ultra-short-term (from seconds to under 30 min) and short-term (30 min to under 6 h) forecasting [4]. Reference [5] proposes a short-term probabilistic photovoltaic power forecasting model based on the beta distribution, which improves the forecasting process and results using a semi-parametric method. (2) Machine learning-based methods such as k-nearest neighbor (KNN), support vector machine (SVM), and gradient boosting decision trees [6; 7; 8]. These methods are also suitable for short-term forecasts. (3) Deep learning methods, which can be divided into three categories: convolutional neural network (CNN) methods, recurrent neural network (RNN) methods, and hybrid models combining both [9]. RNN usually includes long short-term memory (LSTM) and gated recurrent unit (GRU) [10]. They can automatically learn complex mappings from input to output and support multiple inputs and outputs [11]. (4) Physical modeling method is based on NWP (such as solar irradiance, temperature, wind speed, etc.) and the physical characteristics of a photovoltaic system. Such methods can provide a detailed understanding of the behavior of photovoltaic systems and are often suitable for in-depth analysis and simulation of the physical properties of the system. Reference [12] proposes a probabilistic photovoltaic power forecasting method that combines ensemble numerical weather prediction (NWP) and a chain of physical models. By incorporating quantile regression, this method improves the accuracy and reliability of the forecasts. In addition to the above methods, combinatorial forecasting methods combine the advantages of multiple forecasting methods, such as combining physical models with statistical models, machine learning, or deep learning models, taking advantage of the complementary properties of the different methods to improve prediction performance. These methods are the most widely used in current research. Photovoltaic (PV) power output forecasting is primarily divided into short-term and medium-to-long-term predictions.