# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 5 Case Study
### MIC Correlation Analysis  
First, perform the segmented MIC correlation analysis proposed in this paper, which includes full-time correlation analysis and power-generation-time correlation analysis. The MIC correlation coefficients are shown in Table 1. In this table, total cloud cover refers to the proportion of the sky covered by clouds, usually expressed as a percentage. It significantly impacts solar radiation, particularly direct and diffuse radiation reaching the surface. The irradiance involved in the EC meteorological sources in this paper is global horizontal irradiance (GHI), which is the total irradiance on a horizontal plane, including both direct and diffuse radiation, ranging from 0 to 2000 W/m\({}^{2}\).  
From Table 1, it can be seen that cloud cover shows a moderate correlation with PV power in both full-time and power-generation-time periods. The correlation is slightly higher during the power-generation-time period, indicating that cloud cover has a more significant impact during PV power generation. Solar irradiance is highly correlated with PV power in both time periods, especially over the full-time period, with correlation coefficients exceeding 0.9. This shows that solar irradiance is a decisive factor in PV power. Meanwhile, the correlation between various seasonal meteorological variables and PV  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Full-Time PeriodCorrelation Coefficient**} \\ \hline
**Time** & **Total Cloud** & **Traalance** & **Pressure** & **Precipitation** & **Relative** & **2 m** & **Surface** & **70 m Wind** & **70 m Wind** \\
**Period** & **Cover** & **Traalance** & **Pressure** & **Precipitation** & **Humidity** & **Temperature** & **Temperature** & **Speed** & **Direction** \\ \hline January to & 0.339 & 0.919 & 0.078 & 0.063 & 0.188 & 0.113 & 0.312 & 0.119 & 0.124 \\ March & 0.309 & 0.935 & 0.092 & 0.075 & 0.241 & 0.168 & 0.397 & 0.142 & 0.110 \\ April to June & 0.283 & 0.959 & 0.078 & 0.071 & 0.450 & 0.345 & 0.625 & 0.129 & 0.071 \\ \hline October to & 0.329 & 0.943 & 0.084 & 0.061 & 0.297 & 0.215 & 0.467 & 0.131 & 0.094 \\ December & 0.270 & 0.902 & 0.061 & 0.053 & 0.162 & 0.161 & 0.360 & 0.055 & 0.065 \\ \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Power-Generation–Time Period Correlation Coefficient**} \\ \hline
**Time** & **Total Cloud** & **Traalance** & **Pressure** & **Precipitation** & **Relative** & **2 m** & **Surface** & **70 m Wind** & **70 m Wind** & **Speed** & **Direction** \\
**Period** & **Cover** & **Traalance** & **Pressure** & **Precipitation** & **Humidity** & **Temperature** & **Temperature** & **Speed** & **Direction** \\ \hline January to & 0.412 & 0.722 & 0.111 & 0.088 & 0.256 & 0.157 & 0.388 & 0.165 & 0.147 \\ March & 0.354 & 0.729 & 0.105 & 0.100 & 0.253 & 0.160 & 0.387 & 0.130 & 0.129 \\ \hline April to June & 0.307 & 0.782 & 0.101 & 0.075 & 0.398 & 0.354 & 0.619 & 0.124 & 0.089 \\ September & & & & & & & & \\ \hline October to & 0.343 & 0.781 & 0.110 & 0.101 & 0.314 & 0.159 & 0.313 & 0.143 & 0.140 \\ December & 0.302 & 0.726 & 0.109 & 0.052 & 0.226 & 0.137 & 0.274 & 0.083 & 0.080 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PV power MIC correlation coefficient.  
power varies significantly. For instance, relative humidity is much more strongly correlated with PV power in summer (July-September) than in winter. This may be because the high humidity in summer reduces the efficiency of PV panels.  
After comprehensive analysis, this paper selects total cloud cover, irradiance, relative humidity, and surface temperature as relevant weather features for PV power to proceed with the next MVMD and prediction process.