# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 4 PV Power Generation  
Based on the methods mentioned above, this paper proposes a short-term PV power generation method using MVMD feature extraction and the Informer neural network model. The structure of the prediction model is shown in Figure 2.  
Firstly, to more accurately extract the weather features most correlated with PV power stations, this paper uses the MIC correlation analysis method to optimize the selection of multidimensional weather factors. MIC can quantify the nonlinear relationship between  
Figure 2: Flowchart of short-term PV power generation based on MVMD and informer. various weather factors and PV power, which is crucial for capturing the impact of complex weather variables on PV power generation. Considering that PV power stations have almost no power generation at night, this paper proposes a strategy of performing correlation analysis in different time segments, namely, full-time correlation analysis and power-generation-time correlation analysis. Full-time correlation analysis covers 24 h data, helping reveal common, overall weather-influencing factors. However, since PV power generation mainly occurs during the day, especially during periods of high solar irradiance, power-generation-time correlation analysis focuses on these critical periods, directly identifying the most crucial weather factors affecting PV power. This dual analysis method provides a comprehensive understanding of the impact of weather factors on PV power and allows for a thorough evaluation of the most critical weather features. Additionally, this analysis method covers not only the daylight hours but also the nighttime. Although photovoltaic stations generate almost no power at night, nighttime meteorological conditions, such as temperatures and humidity, can affect the temperature of the modules during the night and the following morning, thereby impacting the efficiency of power production the next day. For example, if the nighttime temperatures are lower, it may slow down the heat loss from the photovoltaic panels, ensuring that the panels start the day at a relatively lower temperature. As solar irradiance increases, the efficiency of the panels could be higher. Moreover, nighttime weather conditions, such as frost or dew formation, could affect the cleanliness of the photovoltaic panels' surface, further impacting the next day's power production efficiency. Next, to more comprehensively describe the relationship between PV power and environmental conditions, this paper employs MVMD to perform time-frequency synchronized analysis on PV power time series in combination with the meteorological data most correlated to it via MIC analysis. MVMD, an advanced signal decomposition tool, can simultaneously decompose multiple time series and extract their respective intrinsic mode functions. Using MIC-based correlation analysis, the weather factors most strongly correlated with PV power are selected as input variables. These input variables, along with the PV power time series, are input into the MVMD model and, through decomposition, produce frequency-aligned multivariate intrinsic modes. These frequency-aligned modes not only retain the essential characteristics of each data set but also reveal the coupling relationships between PV power and weather factors across different frequency components, providing multidimensional feature input for subsequent PV power generation. Decomposing PV power and multivariate meteorological sequences into simpler subsequences also reduces the effects of nonlinearity and volatility in PV power time series. To further enhance the model's learning ability, an Informer neural network model with a Huber loss function is used during the prediction phase. Based on the corresponding PV power IMFs (intrinsic mode functions), combined with relevant weather modal components, multiple Informer prediction models are built for training and forecasting.