## 1 Introduction
### State of the Art
References [42; 43] compared the performance of several satellite data sets with ground measurements data in Morocco and North Africa, respectively. Statistical methods for measuring errors were used in various papers as a way to validate the solar irradiation data. For instance, [PERSON] et al. [44] compared satellite data and ground measurements for various sites in the Canary Islands. [PERSON] et al. [45] employed the same validation method for various sites in Europe, detecting operational errors for some Baseline Surface Radiation Network (BSRN) stations. [PERSON] et al. [46] undertook data validation through statistical methods for Tamarrasset, Algeria, and [PERSON] et al. [47] applied it in Spain. [PERSON] et al. [48] concluded that for European and Mediterranean sites, the irradiance data retrieved from various satellite databases had low uncertainty with a negligible bias when compared to ground measurements for the same locations. For African sites however, the lack of meteorological stations in the region could prove fatal for solar projects evaluation. Because of that, satellite data are one option that most projects decide on. References [49; 50; 51] showed that the SARAH database is good enough for monitoring and analysis of solar conditions in many sites, especially for Africa. Additionally, [PERSON] et al. [52] concluded that the PVGIS database is of high quality for PV performance estimates for both Europe and Africa. Finally, in references [53; 54] it was stated that solar irradiance values have high variability in areas with variable landforms, such as those with mountains and coastal areas. The main characteristics of interest of the several references considered here are summarized in Table 1. The references have been listed either according to the long- or short-term character of the solar irradiance forecasting or according to their use of satellite-based data. \begin{table}
\begin{tabular}{c} \hline \hline
**Term of Solar Irradiance Forecasting** \\ \hline
[9] Intra-hour GHI cloud retrieval technique to develop a physics-based smart persistence model \\
[10] Intra-day GHI and DNI algorithm using cloud physical properties \\
[11] A 15 min GHI forecasting model \\
[12] Hourly-averaged GHI forecasts \\
[13] Hourly GHI and DNI clear-sky irradiance vs. RRTMG physical radiative transfer model \\
[14] Hourly and daily GHI from mesoscale atmospheric weather research forecasting model \\
[15] Hourly GHI with a three-dimensional meteorology–chemistry model including a treatment of aerosols \\
[16] Hourly GHI exponential smoothing model with decomposition methods \\
[17] A 1 min DNI under a probabilistic approach \\
[18] Short-term GHI with hybrid convolutional ANN model with spatiotemporal correlations \\
[19] Short-term GHI and DNI forecasts of a global numerical weather model \\
[20; 21] A 5–30 min GHI and DNI with machine learning techniques \\
[9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 26; 27; 28; 29; 31; 32; 33; 34; 35] \\
[23] Intra-day GHI with machine learning techniques \\
[24] A 30 min GHI with ANN algorithm \\
[25; 26] Hourly GHI ANN models \\
[27] Mean daily GHI with ANN models \\
[28] A 500 ms-5 min GHI based on k-means algorithm \\
[29] A 5–30 min GHI and DNI based on the k-nearest neighbours algorithm \\
[30] A 30 min-5 h GHI Gaussian process regression method \\
[31] Daily GHI with ANN models for for 25 Moroccan cities \\
[32] Daily GHI with empirical and machine learning models for \\
5 Moroccan cities \\
[33] Monthly mean daily GHI using time series models \\
[34] Daily GHI with hybrid ARIMA–ANN model for 3 cities in Morocco \\
[35] Daily GHI with ANN models for 35 Moroccan, Algerian, Spanish and Mauritian cities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of the considered references. Source: own elaboration based on [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35].