## 4 Conclusions  
This study investigated the correlation between solar power generation and the meteorological variables by deriving multiple linear regression models. For this, a large data analysis software, R, was applied to the solar power generation and meteorological variable datasets. In the regression models, solar power generation was set as the dependent variable and the meteorological variables as the independent variables. The independent variables first considered were the insolation intensity at the peak time (MJ/h), insolation intensity during daylight hours (MJ/m\({}^{2}\)), daylight time (h), average relative humidity (%), minimum relative humidity (%), and evaporation amount (mm). Through statistical analysis, the insolation intensity at the peak time was excluded from the further regression  
Figure 5: Comparison of the actual and the predicted solar power generations in 2019: (**a**) predicted by the regression model for the entire yearly data; (**b**) predicted by the 12 monthly regression models.  
model as it possessed multicollinearity. For the resulting regression model, \(R^{2}\) was 0.7738, adjusted \(R^{2}\) was 0.7735, and RMSE was 69.06.  
In addition, 12 monthly regression models were derived to improve the predictability because the difference in monthly insolation had to be considered. The regression model with the highest accuracy was for January, with \(R^{2}\) being 0.8985, adjusted \(R^{2}\) being 0.8966, and RMSE being 40.66. The regression model with the lowest accuracy was for December, with \(R^{2}\) being 0.7228, adjusted \(R^{2}\) being 0.7178, and RMSE being 68.39. To check the predictability of the regression models, comparison between the actual daily power generation and the predicted power generation by the monthly regression models was shown in Figure 5.  
Regression analyses showed the degree of correlation between solar power generation and each meteorological variable. The effect of each meteorological variable on solar power generation varied month-to-month. Among the two meteorological variables, the insolation intensity during daylight hours and daylight time had the highest correlation with solar power generation throughout the year. Interestingly, the quantity of evaporation impacted the solar power generation in January and November in a fairly big way.  
This paper presented improved predictability for regression models by deriving the 12 monthly models used to consider the difference in monthly conditions of meteorological variables. Accordingly, we assumed that the predictability of the regression analysis for the data, including the variables, significantly varied month-to-month and could be improved by deriving monthly regression models rather than deriving an entire regression model.  
Formal analysis, Y.S.K. and H.Y.J.; Investigation, J.W.K.; Project administration, J.H.M.; Validation, H.Y.J., J.W.K. and S.Y.J.; Visualization, S.Y.J.; Writing--original draft, Y.S.K.; Writing--review & editing, J.H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Found of Korea (NRF), grant number 2020M2D2A2062436, and by the Korea Foundation of Nuclear Safety (KoFONS), grant number 2003019.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT). (No. 2020M2D2A2062436) and in part by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2003019).  
The authors declare no conflict of interest.