## 1 Introduction  
Electricity from renewable energy resources (RESs), especially from photovoltaics (PV) is a key solution to the increasing global environmental and social challenges. Among these, carbon emissions and energy shortages have major concerns. The international renewable energy agency (IRENA) has predicted a solar generation capacity of up to 8500 gigawatts (GW) by 2050 [1]. Meanwhile, in the United States, PV capacity on the utility-scale is projected to grow by 127 GW between 2020 and 2050 [2], and the Republic of Korea is aiming to add 30.8 GW of solar power generation by 2030 [3]. Similarly, China is following the PV capacity expansion trend and plans to achieve capacities of up to 450 GW and 1300 GW for the years 2030 and 2050, respectively [4]. As energy from PV is clean, green, and naturally replenished over large areas, it is considered to be the most promising alternative to fossil fuels [5]. However, the variability in weather conditions creates uncertainties and variations for the PV resource, thus threatening the reliability and stability of the electric power system. This resource intermittency can lead to significant uncertainties related to the planning, management, and maintenance of electrical energy systems; hence, the accurate forecasting of PV power generation is essential [6]. Nevertheless, due to the volatile and random nature of this power source, accurate prediction remains a challenging task. There is presently widespread interest in the optimal integration of solar energy on various scales of the power system. The electrical energy produced by PV plants can be classified into the following three levels: (i) the distributed generation level, (ii) the microgrid level, and (iii) the large-scale power generation level [7]. To obtain energy from PV, solar irradiance (SI) is utilized as a resource [8]. Therefore, the forecasting of SI is of increasing interest among researchers. A highly accurate SI forecasting leads to a secure and optimized solution for the power system [9]. In addition, SI forecasting has been performed for various operational and management applications such as scheduling, reserve management, congestion management, and so on [10]. These applications are associated with specific forecasting time scales and horizons. The time scales are ranged from a few seconds ahead to years ahead. Forecasting horizons are defined for specific ranges of the time scales according to the applications' requirements. Figure 1 depicts various applications of the power systems related to each forecasting horizon and time scale [11; 12; 13]. In the present study, a forecasting framework is proposed for the day-ahead prediction of SI. In a power system, day-ahead SI forecasting is essential for power system operations and day-ahead market (DAM). Accurate forecasting in unit commitment and economic dispatch provides an improved and economically efficient operation. In addition, accurate forecasting leads to better utilization of PV power and hence, results in less curtailment, reduced uncertainty in power system operations, and minimized overall operating cost. The study on the impact of the day-ahead solar energy forecasting for an independent system operator in New England (ISO-NE) on system operation and overall operating cost is presented in [14]. Day-ahead forecasting is also important to deal with the management of the operating reserve [15]. In [16], the analysis on improved SI forecasting is performed in order to see the effects on flexibility reserves while dealing with imbalanced markets. Results have shown that better forecasting leads to a reduction in flexibility reserves. Likewise, in [17] for the improved day-ahead SI forecast, authors have reported a reduction in spinning reserve requirements for various system operators and utilities. For a concentrated solar-thermal (CST) plant in Australia, [18] shows significant economic benefit for six months of operation considering the improvement in short-term SI forecasting.