## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Image-Based Methods
#### 3.3.2 All-Sky Images
This algorithm is best for recognizing clear sky conditions and cirrus clouds. It is found in Heinle's study that the most confusing causes for inaccuracy are low-cloudy conditions (lower than 30%) and the glare effect of the circumsolar region. To improve the classification of seven cloud types proposed by Heinle, Ref. [39] applied an SVC support vector classifier. Moreover, Ref. [184] highlights Heinle's seven cloud classification model, detecting these types successfully in 78 to 95% of the cases. Finally, Ref. [211] references up to eleven cloud types at high resolution. For such numerous classifications, the main issues arise from three cloud types: stratocumuluswith altostratus, cirrostratus, and stratus clouds. This complication is due to a mutual transformation between the three cloud types. Despite this, the same classification is also proposed by [39]. This study achieved an accuracy of 92% after a ten-fold cross-validation. However, the accuracy is low for cirrus and cirrostratus clouds as the RBR threshold is not able to distinguish properly. After cloud segmentation and classification, the velocity and direction of cloud motion are typically determined using a cross-correlation method (CCM) applied to two consecutive sky images. This method identifies the movement of a block of pixels between two images by finding the region in the second image that has the highest correlation with each block of the first image. The resulting motion vector grid contains information about the observed motion direction and velocity. However, cloud formation, deformation, and evaporation can occur during the process. When this occurs, the correlation is reduced considerably, even though the assumption of spatial homogeneity of cloud velocities is made. Additionally, the appearance of clouds can change over time due to variations in white balance settings, cloud illumination, and different viewing geometries [108]. Several approaches have been proposed to monitor cloud formation and deformation during cloud motion. For instance, Ref. [212] used a non-rigid structure to model the complex dynamics of cloud motion, achieving a 19% improvement over the rigid technique presented in [108]. In fact, the study utilized optical flow techniques, specifically the Thirions Demons algorithm [213] and the variational approach of [214]. In contrast, Ref. [215] showed that common computer vision techniques, such as the Lucas-Kanade algorithm [216] and optical flow, perform better for cloud tracking in cases of low pixel displacement between successive images. In Ref. [217], a deep learning-based cloud detection and classification algorithm was developed for advanced Himawari image measurements (AHI) from the Himawari-8 geostationary satellite. Additionally, Ref. [218] incorporated an IPSI-based multi-transform-fusion (MTF) method to improve accuracy in cloud motion displacement (CMD) calculations and [219] propose a forecasting method based on a phase correlation algorithm for motion estimation between subsequent cloud maps derived from Meteosat-9 images. To conclude, Table 2, found in Appendix A, outlines visually the works discussed in this section to provide detailed information on the investigations carried out. As in Table 1, Table 2 excludes those studies that concentrate on evaluating and comparing previous models or those that focus on additional aspects from the main subject of this article.