## 2 Materials and Methods
### Feature Selection  
In this study, meteorological factors were used as feature data and the final power data were predicted through historical meteorological data. In the field of artificial intelligence, too many feature dimensions will introduce redundant information, prolong the training time, and increase the difficulty of modeling. The Insufficient Features dimensions will lead to the failure to achieve ideal prediction results. When selecting input features, factors with strong correlation with power should be selected.  
Before selecting input factors, we first conduct a significance test to observe the statistical significance of the correlation between variables. By calculating the two-tailed test results based on the t distribution, the significant correlation p-values between each variable and Active_Power are all 0.00, which means that the correlation between each variable and Active_Power is statistically significant, proving that each variable and Active_Power are related in the population, so the correlation tests can be performed to obtain factors with strong correlations.  
The Pearson correlation coefficient is used to measure the correlation between two variables, \(X\) and \(Y\)[47]. In this study, it was used to measure the degree of correlation between meteorological factors and power generation. The formula of the Pearson correlation coefficient is:  
\[\mathrm{Y}=\frac{\sum\limits_{m=1}^{n}(X_{m}-\bar{X})(Y_{m}-\bar{Y})}{\sqrt{ \sum\limits_{m=1}^{n}(X_{m}-\bar{X})^{2}}\sqrt{\sum\limits_{m=1}^{n}(Y_{m}- \bar{Y})^{2}}} \tag{1}\]  
where \(X\) represents a value of a meteorological factor, and \(\bar{X}\) represents its mean; \(Y\) represents a value of photovoltaic power generation, and \(\bar{Y}\) represents its mean; and n represents the total number of data points for a meteorological factor.  
In the Pearson correlation coefficient, when \(\left|\left.\right|\left.\right|\right.\geq 0.8\), the two variables can be considered to be highly correlated; when \(0.5\leq\left|\left.\right|\right.<0.8\), the two variables can be considered to be moderately correlated; and when \(0.3\leq\left|\left.\right|\right.<0.5\), the two variables can be considered to be a low-degree correlation. If \(\left|\left.\right|\right.<0.3\), it can be considered that the two variables are basically irrelevant [48].  
The Pearson correlation coefficient between photovoltaic power and various meteorological factors is shown in Figure 5.  
From the analysis in Figure 5, it can be concluded that the absolute values of the correlation coefficient of Current_Phase_Average, Performance_Ratio, Weather_Relative_Humidity, Global_Horizontal_Radiation, Radiation_Global_Tilted, and Active_Power are greater than 0.3. Therefore, these factors are ultimately selected as the final meteorological feature factors. The detailed information is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Meteorological Feature Factor** & **Absolute Pearson Value** \\ \hline Current\_Phase\_Average & 1 \\ Performance\_Ratio & 0.46 \\ Weather\_Relative\_Humidity & 0.36 \\ Global\_Horizontal\_Radiation & 0.95 \\ Radiation\_Global\_Tilted & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected meteorological feature factors.