## 1 Introduction  
Since 2017, the Korean government has promoted an energy policy that has a goal of renewable energy reaching 20% in the national gross power generation by 2030 [1]. Owing to this policy, Korea's capacity of renewable energy power facilities has increased by approximately five times from 2010 to 2019. In 2019, the renewable energy facility capacity share was 13% of the gross power generation facility capacity, while the solar power facility capacity accounted for 67% of the gross renewable energy facility capacity [2]. As it converts sunlight into electricity, solar power generation heavily depends on the weather conditions of the region where the facility is installed [3]. For example, insolation, which significantly influences solar power generation, fluctuates each month. Figure 1 shows the monthly insolation (MJ/m\({}^{2}\)) averaged from 2011 to 2019 over the entire region of Korea. In Korea, it reaches the maximum in May and the minimum in December, as shown in Figure 1. Rain also affects solar power generation. There were several rainy days in September 2020, which was approximately 1.7 times higher than those in May 2020 when the highest amount of solar power was generated in the entire year [4]. Such irregular weather conditions make it difficult to ensure stable solar power generation. Because every power plant must respond in a timely manner to the changing electricity demands over time, solar power plants should be capable of predicting the amount of power required in the near future and respond accordingly to changing electricity demands [5]. Accordingly, several studies have tried to predict the amount of solar power generation as accurately as possible. Solar power generation has been predicted by utilizing mathematical relationships with linear regression models [6, 7], autoregressivemodels [8; 9], and recurrent neural network models [10; 11]. The prediction models might be divided into the two categories: short- and long-term prediction models depending on if the prediction period is longer than a day [12]. The short-term prediction model can effectively predict near-future power generation, but the long-term prediction model is also needed to consider unexpected and extreme weather conditions such as a long rainy season [13]. Recently, researchers have adopted predictive modeling techniques such as \"artificial neural networks,\" \"fuzzy predictions,\" and \"support vector regressions\" [14]. However, most of these models have been unable to make accurate predictions because they did not have sufficient raw data, which means that the predictability of the models could be improved if more raw data are accumulated [13]. This study aimed to develop a model in order to easily predict solar power according to the changes in the meteorological variables, as well as identify the meteorological variables significantly impacting the solar power generation in Korea. To achieve this objective, a multiple regression analysis technique was applied to the big data on the solar power generation and weather conditions around the area where the solar power plant was installed. The multiple regression analysis has advantages of the variables being added to and removed from the model easily in the middle of the regression process, and thus a quick calculation was possible [15]. For the regression analysis, the packages in R, a large data analysis software, was used [16]. In this study, two types of regression model were developed. First, irrespective of month, a regression model for the entire dataset was developed. Second, as insolation intensity in Korea considerably varied from month to month. As such, 12 regression models for each month were developed to increase the predictability of the model. For the regression, the dependent variable was the quantity of solar power generated by a solar power plant in Samcheonpo, Korea, and the independent variables were the meteorological data provided by the Korean Meteorological Administration, which were screened sequentially during regression analysis.