## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Image-Based Methods
#### 3.3.1 Satellite Imagery  
From a quantitative perspective, a satellite image is a measurement of the radiance emitted or reflected by the Earth-atmosphere system, integrated over a specific wavelength range, time instant, and spatial window. For solar irradiance forecasting, the most commonly used satellite imagery includes thermal infrared images, water vapor-based cloud coverage images, and visible light spectrum images that capture surface albedo and cloud reflection. These images provide a wide range of analyses, containing valuable information for predicting solar irradiance.  
This includes cloud characterization, as well as other factors that influence the forecast such as wind direction and the Earth's surface albedo. In terms of cloud appearance and shape, geostationary satellite imagery provides information on cloud amount, shape,and thickness. Additionally, AMV (Atmospheric Motion Vector) images offer insight into atmospheric motion, including wind direction and speed in the lower, middle, and upper wind fields. Furthermore, satellite irradiance imagery is of particular interest in the field of solar energy; these are based on the amount of reflected light intensity from the ground, and thus irradiance can be combined with module temperature to forecast photovoltaic power generation [169].  
In this regard, researchers who use satellite imagery as input data typically utilize one or more of the aforementioned types of imagery. As an example, Ref. [170] developed a short-term forecasting model for solar irradiance ramps by generating irradiance maps through the projection of cloud maps obtained from satellite imagery. The model then estimates the evolution of the moving cloud projection based on wind data from NWP (numerical weather prediction) models. In contrast, Ref. [68] creates a GHI forecast model by combining information from the clear sky model of Pvlib Solis with consecutive images from the geostationary meteorological satellite by applying the Heliosat method. Ref. [171] develops a GHI and DNI forecast algorithm by deriving cloud motion vectors and utilizing the clouds' physical properties through infrared and visible spectrum imagery. Ref. [172] presents a prediction model based on deep neural networks to predict the amount of solar energy potential using geostationary satellite image data in units of one hour for more than 7 years. Ref. [34] introduces a GHI forecasting approach based on satellite observations and ANN networks; thus, the satellite images that are processed via the algorithms for velocimetry and cloud indexing are utilized as inputs to the ANN model.  
In comparison, Ref. [173] estimates the three radiation components using MSG satellite imagery and the Heliosat-2 model and achieves this by calculating the attenuation for any sky condition and the present estimated value compared to the value of the previous 15 min. However, Ref. [58] provides a study in which they designed an (ELM) extreme learning machine model to forecast the incident solar radiation (ISR) by means of extracted data from the imaging spectroradiometer satellite and applied this to 41 uniformly distributed locations. Ref. [174] developed a solar irradiance mapping model that utilizes feature importance analysis to calculate terrestrial solar irradiance using only satellite data. In a similar vein, Ref. [175] employs satellite imagery in their forecasting algorithm by testing various variables, such as the clear sky index K\({}_{\rm c}\), the solar zenith angle (SZA), the surrounding cloud pattern, and North Atlantic weather regimes as predictors.  
Moreover, several publications have compared satellite results with those provided by regional-scale numerical models, as seen in [176] in Korea. Additionally, some studies have compared satellite results with those obtained from ground station measurements, such as [70], which compared results at 25 locations in the Netherlands, and [177], which employed Meteosat East for comparison.