## 3 Results and Discussion  
The coefficients of the entire regression model between the solar power generations over a year for the past 9 years and the meteorological variables are listed in Table 2. As observed in Table 2, the VIF values for the two independent variables--namely the insolation intensities at the peak time and during daylight hours--exceed 10. This indicates that these two variables possessed multicollinearity. Thus, the insolation intensity at the peak time was excluded from the regression model. The revised regression models and their statistics were obtained after removing the insolation intensity at peak times, as summarized in Tables 3 and 4, respectively. For the regression models given in Table 3, \(R^{2}\) and adjusted \(R^{2}\) were 0.7738 and 0.7735, respectively. The \(p\)-value is generally interpreted as statistically significant when it is less than 0.05. As the \(p\)-value is less than 0.05, the null hypothesis can be rejected, and the alternative hypothesis can be adopted [33]. In other words, the equation using coefficients in Table 3 can be used as a multiple linear regression model. To consider the difference in monthly insolation, we derived 12 monthly regression models. Table 5 lists the regression models for the monthly solar power generation, which was averaged from 2011 to 2019. The \(R^{2}\)s for the monthly regression models in Table 5 are larger than in Table 4. This means that the goodness of fit of the regression models in Table 5 was better than that of the regression model in Table 4. Therefore, the monthly regression models estimated solar power generation was better than the entire regression model. The regression model with the highest accuracy was for January and that with the lowest accuracy was for December. Figure 5 compares the actual daily power generation of the Samcheonpo power plant in 2019, as well as the predicted monthly regression models in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  &
\begin{tabular}{c} **Coefficient** \\ **(\(\beta_{i}\))** \\ \end{tabular} & **Std. Error** & \(p\)**-Value** & **VIF** \\ \hline Constant & 174.97 & 8.48 & \(2.2\times 10^{-16}\) & - \\ \hline Insolation intensity at peak time (MJ/m\({}^{2}\)) & 49.73 & 4.99 & \(2.2\times 10^{-16}\) & 13.14 \\ \hline Insolation intensity during daylight hours (MJ/m\({}^{2}\)) & 4.42 & 0.75 & \(4.5\times 10^{-9}\) & 18.61 \\ \hline Daylight time (h) & 13.64 & 0.67 & \(2.2\times 10^{-16}\) & 4.87 \\ \hline Average relative humidity (\%) & \(-1.30\) & 0.16 & \(2.9\times 10^{-16}\) & 4.21 \\ \hline Minimum relative humidity (\%) & \(-0.74\) & 0.15 & \(6.7\times 10^{-16}\) & 6.06 \\ \hline Quantity of evaporation (kg/h) & \(-9.00\) & 0.87 & \(2.2\times 10^{-16}\) & 2.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multiple linear regression model for the yearly data collected over the past 9 years (2011–2019). Table 5 also shows that the two variables (insolation intensity during daylight hours (X\({}_{1}\)), and daylight time (X\({}_{2}\))) were the dominant variables impacting the solar power generation. For January, June to September, and November to December, the insolation intensity during daylight hours (X\({}_{1}\)) was the most dominant meteorological variable. For February to May and October, the daylight times (X\({}_{2}\)) were the most dominant meteorological variable. Interestingly, the evaporation quantity fairly impacted solar power generation in January and November.