## 1 Introduction  
Solar photovoltaic (PV) power has been widely utilized to cope with global warming and climate change, which are dominantly caused by the use of fossil fuels. Owing to its simple adaptation and low maintenance cost, solar PV technology is also becoming more common compared to other clean energies. However, the intermittent nature of solar irradiance is a major drawback that leads to unpredictable and uncontrollable electricity production from solar PV systems. Solar energy strongly depends on local meteorological factors, such as clouds and aerosol molecules, as well as sun position at a specific site. Therefore, solar irradiance data are critical for the optimization of solar PV power generation and the operation of solar PV systems [1; 2; 3; 4]. The most reliable solar irradiance data are obtained from direct measurement using a pyranometer and pyrheliometer on the ground. Nowadays, the technology for irradiance measurement has advanced to the point that real-time data can be automatically collected and monitored. Nonetheless, ground measurement systems are expensive and require careful maintenance. As a result, ground measurement has limited spatial coverage, and the measurement database is still deficient for satisfying the demands of a PV-using society. Despite the attempts of some scholars to overcome the limited spatial coverage of direct measurement using conventional approaches, such as interpolation, the accuracy of the interpolated data is still not adequate [5; 6]. Satellite data play an essential role in gathering information on solar irradiance on the earth's surface. [PERSON] et al. [5] showed that the accuracy of satellite-derived data is comparable with that of interpolated data from a station 25 km from the measurement site. The national solar radiation database in the United States has already taken advantage of satellite-driven irradiance data for nationwide coverage [7]. Geostationary satellites, whose orbit speed is identical to that of earth rotation, can repeatedly capture images of a specific area on the earth's surface. The new generation of geostationary satellites are capable of delivering data images with a high spatial resolution of 1 \(\times\) 1 km\({}^{2}\) for each visible channel image up to a 15-min time interval. Therefore, the solar irradiance data extracted from geostationary satellite images have a high potential for efficiently and inexpensively satisfying the demands of the solar energy sector. Many researchers have developed and improved models to extract global horizontal irradiance (GHI) from satellite images [1; 2; 3; 6; 8; 9; 10; 11; 12]. The GHI estimation models are commonly in a physical or semi-empirical form. The physical models theoretically compute the extraterrestrial solar irradiance transmission through the atmosphere by calculating the radiative transfer equation. On the other hand, the semi-empirical models count on a linear interrelation between the satellite-recorded surface albedo and the clear-sky index, along with the consideration of important physical phenomena. In terms of reliability and versatility, physical models have shown higher performance than semi-empirical models, as shown in Table 1. However, physical models technically require more input of atmospheric data, such as aerosol optical depth (AOD), ozone content, and precipitable water. Such atmospheric data are often less accessible at a specific location than ground measurement data of solar irradiance. In contrast, semi-empirical models rely on simple assumptions and statistically determined coefficients. As a result, semi-empirical models require only a few input variables, and furthermore most of them are geometric factors. Semi-empirical models offer easier calculation, simpler implementation, and faster operation [13; 14]. Due to their simplicity, semi-empirical models are often favorable for forecasting solar irradiance [8]. GHI forecasting means that the future values of input variables need to be known.