## 1 Introduction
### State of the Art  
The scientific literature about solar irradiation is widely extended and covers all the relevant aspects, from those studies focusing on the determination and forecasting of the  
Figure 1: CSP capacity installed in the MENA region. Source: own elaboration based on [3]. global horizontal irradiation (GHI) as well as the beam or direct normal irradiation (DNI) on a horizontal surface to those on a tilted surface, at different time frames (see Figure 2). The existing literature aimed at forecasting solar irradiation and/or irradiance is quite extensive. Likewise, so are the different methods used to carry it out. As extracted from [7], which presents an in-depth analysis of the performance of different solar irradiance forecasting models, the predominant forecasted variable in most studies is the GHI, followed by the DNI. Moreover, the most frequent forecasting horizon is the short term, i.e., intra-hour, intra-day or, to a lesser degree, day-ahead predictions. This same trend is observed in [8], where an overview focused on solar irradiation forecasting methods using machine learning approaches is given. In this context, some references can be highlighted dealing with short-term solar irradiance forecasting [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] and, to a lesser extent, some focused on monthly solar predictions [36; 37; 38; 39; 40; 41]. Intra-hour GHI forecast employing a cloud retrieval technique to develop a physics-based smart persistence model is improved in [9], and an algorithm using cloud physical properties for intra-day GHI and DNI forecasting with time horizons of 0-4 h at a 15 min temporal resolution is developed in [10]. A GHI forecasting model based on satellite data from Finland with a forecast horizon of 4 h and a 15 min temporary resolution is developed and validated in [11], while the error obtained from the Japan meteorological agency mesoscale model in the hourly-averaged GHI forecasts from 2008 to 2012 is assessed in [12]. The predictions of 15 clear-sky irradiance models by comparison with the RRTMG physical radiative transfer model, acting as a benchmarking reference, for hourly GHI and DNI over a whole year are evaluated in [13], while the differences induced in the hourly and daily GHI predictions by the mesoscale atmospheric weather research forecasting model in Greece when using different shortwave radiation are assessed in [14]. Likewise, hourly GHI in the Arabian Peninsula using a three-dimensional meteorology-chemistry model including a state-of-the-art prognostic treatment of aerosols is simulated in [15]. In another prediction methodology vision, the performance of an exponential smoothing model with decomposition methods to improve its hourly GHI forecasting accuracy and computational efficiency is analyzed in [16], while prediction intervals for DNI estimates from GHI observations at the minute scale over the Korean Peninsula using the Engerer model under a probabilistic approach are calculated in [17]. Furthermore, a hybrid convolutional neural network-long short-term memory model with spatiotemporal correlations to improve the  
Figure 2: Characterization of solar irradiation in the scientific literature, dealing with either daily (H), average daily (H), hourly (I) or average hourly (I) global, beam (b) and diffuse (d) values. Source: own elaboration. accuracy of short-term GHI prediction for ensuring the optimum utilization of photovoltaic power generation sources is proposed in [18]. In turn, the precision of short-term forecasts for GHI and DNI of a global numerical weather prediction (NWP) model in Portugal is evaluated in [19], while the performance of three NWP models in forecasting daily GHI for Australia is assessed in [20].