## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Image-Based Methods
#### 3.3.2 All-Sky Images  
To obtain a forecast with higher temporal resolution than that provided by satellites, statistical techniques such as time series analysis or neural networks can be applied to radiometric measurements on the surface or motion vector techniques on all-sky camera measurements. This is necessary as global model information is not available for these time horizons. When investigating all-sky cameras, both mirrored and non-mirrored as well as shadow disc or non-shadow disc configurations often face a common initial challenge: cloud characterization. Clouds can scatter and absorb radiation, which affects radiation attenuation. The degree of attenuation depends on cloud micro- and macro-physical properties, making it difficult to accurately segment and classify clouds for estimating cloud attenuation when the sun passes over them. Therefore, this task is not a trivial endeavor [178]. Moreover, investigations can obtain cloud detection and tracking from all-sky imaging information. In fact, some authors even classify and characterize clouds to improve the accuracy of predicting the degree of occlusion as they pass through the solar disc. Cloud segmentation in all-sky images is a critical task for any forecasting system. However, it is a complex task due to the variability of color and intensity caused by a range of factors. These include different cloud types and layers, sun positions, aerosol interference (e.g., pollution, dust), as well as saturation and glare effects in the circumsolar region. To characterize aerosols, the Linke turbidity is calculated based on direct normal irradiance (DNI) or global horizontal irradiance (GHI) measurements using the Linke turbidity model [179]. To overcome the challenges of cloud characterization, researchers have made efforts to improve the accuracy of cloud segmentation and classification. Various approaches have been proposed in the literature, such as using neural networks, correlation between RGB (Red Green Blue) or RBR (red-blue ratio) channels, and SVM classifiers. These approaches have been validated and discussed extensively in the literature. Cloud detection and segmentation algorithms have traditionally relied on fixed thresholds applied to RGB values of images [180; 181; 182; 183; 184; 185], the red-blue ratio (RBR) [42; 43; 46; 47; 104], or comparisons with clear sky libraries (CSLs) [189; 190; 108]. While this approach performs well in most conditions, difficulties arise in cases of very dark clouds and foggy conditions near the sun. Accuracy is also limited when the color temperature of the sky changes significantly, such as near sunrise and sunset [44]. To address these challenges and reduce reliance on fixed thresholds, machine learning approaches were developed [191; 192; 193], as well as adaptive threshold methods such as MCE (Minimum Cross Entropy) based on Otsu's algorithm [194]. The MCE classification algorithm was developed and tested in [187] as part of an HYTA (hybrid thresholding algorithm). The MCE algorithm selects a threshold by minimizing cross-entropy between the original and segmented image, generating a robust and unbiased method for image histograms. Ref. [34] also utilized the MCE method and concluded that the threshold value must be limited within an estimated interval of the training set. After determining the threshold, pixels with an RBR higher than this threshold are classified as cloudy. Building on these studies, Ref. [43] developed a hybrid system that combines the MCE method with the FTM (fixed threshold method) and the CSL (clear sky library). The study uses the FTM and MCE method for cloud detection and the CSL to address image glare. The CSL method is a database of clear sky images for the different solar zenith angles. It is widely applied to remove the geometric variation of clear sky RBR values that depend on the sun pixel angle and zenith angle. This is achieved by subtracting or compensating the RBR of the input image by the CSL RBR reference corresponding to the same zenith angle [195].