## 1 Introduction  
The assessment of solar irradiance has become one of the most important topics in the field of solar energy, such as the photovoltaic industry (PV), concentrating solar power (CSP), and the solar chemistry process [1; 2; 3; 4]. In the solar power industry, constant assessment of the solar resource is essential to establish the viability of exploiting a specific location and predicting the factors that may affect its energy production. Having an accurate assessment of the solar resource is an important advantage that can help in establishing solar energy as a more competitive alternative. Currently, a number of maps and databases describing the solar resource distribution can be found on the Internet; however, confidence intervals of data are not provided in any of them [5]. The proposal in [1] argued that the assessment and forecasting of the solar resource are essential tasks, since they allow better management of the solar field in real time, reducing the maintenance activities, thus improving the expected benefits. Currently, the installation of PV and CSP plants has generated an increase in the production of electricity with renewable energy, and it is to be expected that this trend will continue increasing. However, the fluctuating nature of solar energy poses particular problems in delivering the generated energy to the electricity grids, not to mention the high initial investment cost, which require a short-term forecast of the solar irradiance availability, also known as \"nowcasting\" [1; 2; 6; 7]. This makes the construction and improvement of \"nowcasting\" systems very important. There are two main approaches that allow the forecast of solar irradiance [8]. The first one is the physical-statistical method, which deals with the forecast of the availability of solar irradiance by means of the so-called numerical weather prediction models (NWP). These prediction models have a spatial resolution around of 100 km\({}^{2}\) and deliver resolutions up to 10 km\({}^{2}\)[9], with long-term irradiance prediction [10; 11]. On the other hand, there are models based on real-time prediction by means of satellite images. The spatial resolution of the satellite prediction models is 1 km\({}^{2}\) with a medium temporal resolution greater than 6 h [2]. The need for low temporal resolution (minutes) has been addressed by the development of sky imagers. These systems provide a hemispherical view of the whole sky by using a fish-eye lens or a convex dome mirror and a camera; allowing the Sun motion and cloud vector to be estimated [12]. Generally, the forecast of the NWP has a better performance in the temporal perspective than satellite and sky imagers. However, the fluctuation in the initial conditions and the limited resolution mean that the NWP numerical models cannot predict the position of the clouds accurately, nor their effects on the solar irradiance in a specific location [2; 9; 13]. Therefore, it is inescapable to make better forecasts by short-term measurements with high temporal and spatial resolutions. Most of solar power plants look for reliable long-, medium-, and short-term data to estimate more accurately the amount of energy they can produce daily and thus establish energy generation strategies. In general, for these processes, irradiance sensors are used to obtain global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) data. However, properly calibrated and well-maintained sensors have a high cost, not only for the communication networks required to centralize and store the data, but also for the installations that must be carried out, the acquisition, and maintenance of the sensors themselves. It could be said that the forecasting of solar irradiance is a technology that allows optimizing the costs, offering a better quality of energy supplied by the electrical network, as long as the solar irradiance variation can be predicted with great accuracy [14].