## 3 Review and Evaluation of Solar Irradiance Forecasting Methods  
This section presents a study of various research studies that have utilized the technology explained earlier, categorized based on the methodology employed to obtain solar irradiance forecasting.  
To ensure a comprehensive and relevant review, we established specific inclusion and exclusion criteria for the selection of studies. The inclusion criteria were as follows:  
* Studies published in peer-reviewed journals and conferences from 2000 to 2024.
* Studies employing advanced methodologies such as statistical regression, artificial intelligence, numerical models, and image processing techniques.
* Research that provides quantitative performance evaluations using standard error metrics. Exclusion criteria included the following:
* Studies not available in English or Spanish.
* Papers that did not provide sufficient methodological details or performance metrics.
* Research focused solely on theoretical models without practical implementation or validation. These criteria ensured that the review covered a wide range of relevant and high-quality studies, providing a robust basis for the comparative analysis presented.  
The complexity of physical processes involved in atmospheric dynamics and the interaction of solar radiation with the atmosphere has hindered the development of a single, universal model for solar irradiance forecasting. The various physical factors that influence the solar irradiance received, particularly cloud conditions, make it necessary to differentiate between models. Solar forecasting techniques can be classified into three main categories based on the input data utilized: NWP-based models, statistical and machine learning techniques, and image-based methods. Hybrid methods that incorporate elements from each of these categories and apply various techniques can also be included within  
Figure 4: The number of studies carried out at each location. This figure shows the number of studies conducted at different locations, presented as a bar chart. The data indicate the intensity of research activity at each site, offering a comparative view of research focus areas.  
this classification. The NWP-based methods are well accepted for a spatial resolution of between 10 and 100 km\({}^{2}\)[108] and forecasts 6 h ahead; thereby at six to seven days the real accuracy is already estimated to be only 60% [109].  
On the other hand, statistical and machine learning models use historical data to perform deep learning of features, particularly atmospheric features. The training model then provides forecasts based on new values of the input variables, making these models applicable for a wide range of temporal and spatial horizons, being utilized mostly in hourly forecasting studies. Meanwhile, image-based methods, as discussed in the previous section, employ all-sky cameras, shadow cameras, or satellite imagery mainly for cloud segmentation and tracking. Shadow cameras are suitable for intra-hour forecasting and a spatial resolution of a few to hundreds of meters, while satellite-based methods are advantageous for a forecast horizon varying from 30 min to 6 h and a spatial resolution of about 1 km\({}^{2}\)[110]. This classification is summarized in a forecast classification by time horizon, in which the nowcasting, short-term forecasts, and forecasting techniques are differentiated and illustrated in Figure 5.  
In this section, the approaches presented in the literature for each of the aforementioned techniques will be reviewed, as well as the researchers who have combined them to create hybrid models. Moreover, the nowcasting technique using ground-based cameras, specifically all-sky cameras, through image processing will be evaluated in more depth, building on the previous section.