### Related Work  
In the literature, significant performance differences from the different ML and DL applications in solar radiation forecasting were reported. [PERSON] et al. (2014) used five machine learning models based on Feedforward, Echo State, 1D-Convolutional, LSTM neural networks, and Random Forest (RF) method. They used in their study six parameters: the cloud cover, air temperature, relative humidity, dew point, wind bearing, and sunshine duration. The Root Mean Square Error (RMSE) equals 6.60% for hourly forecasting. However, since some significant parameters are not considered in the study, this could impact the prediction accuracy negatively. In (2015), the convolutional LSTM method is used to forecast solar irradiance on several locations simultaneously; the obtained RMSE is less \begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Parameter** \\ \hline
1 & Solar radiation (sum of direct and diffuse short-wave radiation) (W/m\({}^{2}\)) \\
2 & Direct short-wave radiation \\
3 & Diffuse short-wave radiation \\
4 & Temperature (2 m above ground) \\
5 & Vapor pressure deficit (VPD) at 2 m \\
6 & Relative humidity (2 m above ground) \\
7 & Growing degree days (2 m) estimates plants’ growth and development, depending on the temperature variation \\
8 & Sunshine duration \\
9 & Soil temperature (0–10 cm under the ground level) \\
10 & Total cloud cover (percent) \\
11 & Low cloud cover (percent) \\
12 & Geopotential (height 500 mb) represents the average air temperature in the vertical column \\
13 & Evapotranspiration represents the sum of evaporation from the land \\  & surface plus transpiration from plants \\
14 & Soil moisture (0–10 cm under the ground level) \\
15 & Wind speed (10 m above ground) \\
16 & Total precipitation amount (mm/m\({}^{2}\)) \\
17 & Medium cloud cover (percent) \\
18 & Snowfall amount (cm/m\({}^{2}\)) \\
19 & Wind direction (80 m above ground) \\
20 & High cloud cover (percent) \\
21 & Wind gust (10 m above ground) \\
22 & Wind speed (80 m above ground) \\  & Convective available potential energy CAPE (180 mb) measures the air \\
23 & parcel’s potential energy per kilogram of the air mass. High CAPE value \\  & means that atmosphere is unstable and would produce a strong updraft. \\
24 & Wind Direction (10 m above ground) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The twenty-four parameters employed in our study for solar radiation forecasting. than 15%. However, in this study, the proposed DL technique could require additional training (transfer learning) if different locations are considered. On the other hand, the DL applications have been successfully applied in the last few years. For example, the hybrid DL model proposed by [PERSON] et al. [16] was applied for one-year forecasting solar radiation, comparing the obtained RMSE values for different time intervals related to the four seasons. However, the performed study considered only short time intervals to generate solar irradiance predictions. In [17], the Support Vector Machines (SVM) and Random Forest (RF) models were applied to forecast solar irradiance using weather parameters, such as temperature, humidity, rainfall and wind speed. In this work, the authors utilize SVM and RF models to predict individual PV generator output and compare their performances. Convolutional Neural Network (CNN) and LSTM have been used to forecast solar power, utilizing input datasets related to temperature, wind speed, humidity, ground temperature; the obtained RMSE equals 0.0987 [18]. In [PERSON] et al. [19], the local and global meteorological data (air temperature and wind speed), in the time interval from December 2017 to May 2018, were employed for the forecasting process based on an auto-regressive time-series (ARTS) model, achieving an accuracy of 80%. However, in this research work, the air temperature and wind speed are the only weather variables considered in the prediction process. [PERSON] et al. [20] considered air pressure, zenith angles, temperature, and humidity parameters.