## 1 Introduction  
Recently, considerable research has been conducted on low pollution, renewable energy sources to address carbon emissions and environmental problems, including the Republic of Korea's \"Implementation Plan for Renewable Energy 2030\" [1]. This proposed strategy would increase the country's share of renewable energy from 7.6% (15.1 GW) in 2017 to 20% (63.8 GW) in 2030 by encouraging the use of clean energy such as solar, wind, hydropower, biofuels, and waste recycling. Among these energy sources, solar power generation is one of the most widespread renewable energy industries and has been used extensively as an alternative to existing power generation methods. The solar power supply was expected to account for 30% (5.7 GW) of the country's renewable energy in 2017, and supply 57% (36.5 GW) of renewables by 2030. Photovoltaic (PV) power generation is the most efficient among renewable energy resources for expanding small-scale, distributed power supplies, and it is expected to continue to grow because its power generation costs are decreasing the fastest [1, 2]. Power supply utility companies operate large, centralized power plants to meet supply and demand for electricity. The utilities have generators to supply baseload power, and additional capacity to meet peaks in power demand. Most have the option to import additional production from other power companies if necessary, to meet high demand loads, resulting in some clear limitations. Some power plants can take a long time to come online at maximum production, while others are expensive to operate and are only used for peak-shaving. The demand on some plants can at times be greater than the total power plant output. Power supply companies try to prepare for surges and declines in electrical demand by controlling the energy production and transmission system through the use of predicted power consumption, and operating via demand response (DR) that helps to effectively reduce peaks [3; 4]. The energy management system (EMS) controls energy in all areas of the power system, including power generation, transmission, distribution, and consumption. The EMS software has been adapted for buildings (BEMS), factories (FEMS), and homes (HEMS). The purpose of the EMS is to ensure efficiency in energy use and operation, although individual systems and details differ depending on the area of use. Until recently, electric power systems have consisted of centralized power generation and one-way, distributed consumption, with little more than experience and guesswork to manage and operate energy grids. The development of smart grids incorporating various types of EMS aids intelligent and efficient operation of power systems. The prediction of small-scale, distributed solar power generation that may feed into these electrical grids is very important [5; 6]. For solar power forecasting approaches, different methodologies are preferred, depending on factors such as forecasting horizons, model inputs, and data characteristics. These methods can be broadly divided into three categories: physical, data-driven, and hybrid approaches. 1. The physical approach forecasts solar power using mathematical modeling that takes into consideration weather data (air pressure, temperature, humidity, jet streams, etc.) and environmental characteristics (orography, topography, land use, etc.) [7; 8; 9]. This approach requires a large volume of data, because the accuracy of the prediction increases in proportion to the amount of data. As such, the predictive model is often difficult to construct, and the resulting model structure is complicated because it must mathematically deal with many variables. The calculation process is complicated and requires a long computation time. The most widely used application for physical models is numerical weather prediction (NWP) [10; 11; 12], which is more suitable for long-term forecasting than for short-term and medium-term forecasting because of the large number of computational resources utilized. 2.