## 1 Introduction
### State of the Art
In another forecasting methodology approach, the use of different machine learning techniques for deterministic and probabilistic GHI and DNI forecasts using local irradiance data and sky images with forecasting time horizons ranging from 5 min up to 30 min is evaluated in [21]. Likewise, the integration of different forecasting models, by means of machine learning techniques, to improve the short-term predictions of GHI and DNI with a forecast horizon of 6 h and a temporary resolution of 15 min in the Iberian Peninsula is studied in [22], while a benchmarking of different machine learning techniques for intra-day GHI forecasting from 1 h to 6 h ahead in an insular context is proposed in [23]. An artificial neural network (ANN)-based algorithm to improve GHI forecasts obtained from the NWP model of the European Centre for Medium-range Weather Forecasts (ECMWF) with a time horizon of 72 h and a time-step of 30 min is developed in [24]. Similarly, ANN models to produce hourly GHI forecasts from 1 to 6 h ahead are designed in [25; 26], using exogenous (satellite and NWP model of the ECMWF) and ground data, and ground measurement and satellite data, respectively. In the same way, six ANN models to estimate the monthly mean daily GHI in different locations of the UAE are developed in [27]. A nonparametric method, based on k-means algorithm, for ultra-short-term forecasting of GHI to deliver predictions with a forecast horizon from 500 ms to 5 min under a probabilistic perspective is assessed in [28]. Similarly, a forecast methodology based on the k-nearest neighbours algorithm for intra-hour GHI and DNI with horizons ranging from 5 min up to 30 min using ground telemetry and sky images is proposed in [29]. In turn, a Gaussian process regression method for GHI forecast horizons from 30 min to 5 h is modelled in [30]. With a larger forecasting term, the daily GHI is predicted using ANN models for 25 Moroccan cities in [31], with empirical and machine learning models for 5 Moroccan cities in [32] and with hybrid ARIMA-ANN model for 3 cities in Morocco in [33]. The daily GHI is also forecasted with ANN models for 35 Moroccan, Algerian, Spanish and Mauritrian cities in [34] and the monthly mean daily GHI using time series models in [35]. Although hourly and sub-hourly solar irradiation data are essential for an accurate techno-economical assessment of a CSP or PV project, a pre-feasibility study using monthly solar irradiation data is a common practice, and it is usually performed for the selection of potential sites where the power plant is expected to be located [36; 37; 38]. GHI and DNI are the types of data used for evaluating these projects. For the assessment of monthly data, ANN models are used to estimate it in Saudi Arabia [39; 40] and in Uganda [41]. When it comes to the design of energy projects that involve GHI and DNI, it is important to test their model for different solar irradiation profiles to study the performance variations that the solar system could have when it is implemented in real life. Using only one set of data has the limitation that the model works completely fine if the studied conditions are met. These conditions often represent the mean values that the solar installations will experience. However, when the solar system is subjected to different input conditions, the outcome results may vary greatly from the ones projected during its planning and design phase. That is, there will be a chance that the projected profitability is not reached. As such, the variability of solar irradiation can be considered a risk since it can jeopardize the project's profitability. Since solar systems are going to be implemented increasingly in the future, this variability should be properly taken into account. Frequently, however, the availability of enough measured data is limited. For this reason, satellite-based data are used to assess the solar resource when the measured data are scarce.