## 1 Introduction  
A report from the International Energy Agency (IEA) revealed that solar, wind, and hydropower energy is growing at a fast rate. In the Renewables 2018 forecast, the market analysis and forecasts from 2019 to 2024 by the IEA [1], the segment of renewables satisfying global energy demand is expected to grow by one-fifth in the following five years to reach 12.4% in 2023. South Korea is one of the most developed countries in Asia, and it is the eighth largest electricity consumer in the world [2]. South Korea has been making a great effort to increase the renewable energy portion of their energy mix [3]. The country has a strong solar photovoltaic (PV) manufacturing industry and supportive policies to achieve the national renewable energy target of 20% by 2030. Moreover, South Koreahas revised its nuclear power policy to cut its reliance on nuclear power to facilitate the solar PV adoption manifold.  
Solar PVs can be described as the process of converting light (photons) to electricity (voltage), which is called the photovoltaic effect [4]. Due to the decreasing costs of solar PVs, strong government policies, and private partnerships, various incentive schemes for the use of solar PV power generation (SPVG) have been driving the solar power market at an exponential rate in South Korea [5]. However, when connecting to the grid of a solar PV system, the generation, transmission, maintenance, and consumption and the influences of various uncertainty factors, which include sunshine, cloud cover, temperature, module behavior, and relative humidity, are considered [6]. To maintain the security and economic integration of solar PVs in the microgrid, an accurate estimation or prediction of the SPVG plays a key role in enhancing the influence of the PV power on the grid, which improves the reliability of the PV generation capacity of the power plant and the penetration level of the PV system [7, 8]. Thus, short-term PV forecasting is a challenge for researchers due to the randomness, volatility, and intermittence of SPVG [9].  
There are commonly two types of solar PV forecasting: the direct and indirect methods. The indirect approach is to forecast environmental parameters related to solar PV, such as solar irradiation, wind speed, ambient temperature, device temperature, and other meteorological factors [9, 10, 11]. Then, the active power output (kilowatts) is calculated from a mathematical prediction model that uses the environmental parameters. However, several problems occur in the accuracy of the prediction of solar PV generation using the indirect method. This approach may not address the application in the field regarding power and energy system engineering and does not consider other factors that affect PV generation, such as the panel-tracking direction or the shadow effect of trees or buildings on each PV system [9, 10]. The other approach is a method to predict PV power output directly using historical PV power output data as input variables [11].  
In view of the time frame, a solar PV forecast can be classified on a time horizon as ultra-short term (from a few minutes to less than one hour), short term (one hour to several hours ahead), medium term (several hours to one week ahead), and long term (one week to a year or more ahead) [7, 12]. In the paper, we used ultra-short-term solar PV forecasting based on the direct approach.