## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Image-Based Methods
#### 3.3.2 All-Sky Images
It illustrates how images are segmented and how motion vectors are created, which are essential for analyzing dynamic changes in the observed scene. vertical extent [190]. In [204] modern methods of passive remote sensing of water and ice clouds are discussed. The water path (WP) of a cloud is a crucial parameter in determining its COT. The WP describes the vertically integrated water content (WC) and effective particle size of a cloud [205]. In particular, low- and mid-layer clouds generally have a considerably larger global WP than high-layer clouds, making the WP parameter especially significant in understanding the optical properties of clouds. Furthermore, the effective particle size of a cloud is also an important factor, as a larger particle size leads to higher absorption, while a smaller particle size increases scattering [206]. Current cloud detection and classification methodologies vary depending on the research aim. A review of the current cloud detection and classification methodologies is provided by [207]. In most cases, researchers can fine-tune the degree of cloud classification to fit within their parameters. For example, many investigations that aim to predict occlusion-type or ramp-down-type events in a solar farm generally use a simpler cloud classification system. This system may only distinguish between cloud and no-cloud types [103, 104, 105] or may use categories such as clear, partly cloudy, and cloudy [49, 50, 51]. An occlusion event occurs when the sun is covered or obscured by clouds when they move into the sun's location in the sky. In contrast, ramp-down events occur when the irradiance drops drastically in a short time interval. Both events can be predicted using cloud tracking and clustering data and are critical in solar power forecast scenarios. If the predicted solar power is not sufficient to be delivered to the utility, the PV operator may need to initialize alternative energy sources. As such, it is clear that these events can have a significant impact on solar power generation and the overall efficiency of solar farms. In contrast to the simpler cloud classification discussed above, studies that aim to predict the attenuation levels more accurately focus on a deeper classification of cloud type. Various research exemplifies these deeper and more complex cloud-type classifications. Several studies classify clouds and conditions into three distinct classification groups. Ref. [208], for example, distinguished ciriform, cumuliform, and waveform clouds with a 90.97% accuracy rate via infrared images. A similar classification case can be seen in [209]. This study used an SVM and a BPNN (back propagation neural network) to classify three cloud conditions per pixel. Alternatively, clouds may be classified into four groups. For instance, Ref. [82], divided clouds into four types based on their transmittance index. Likewise, Ref. [191] opted to unify several cloud types into four groups to avoid incorrect classifications and achieved satisfactory results with various classifiers, such as using an MLP (multi-layer perceptron) and an SVM (Support Vector Machine). These several cloud types were based on the cloud classification published by WMO in 1987 (WMO, 1987). However, it should be noted that the accuracy in this study was reduced when cirrus clouds appeared. This decrease in accuracy is due to the cloud's thin parts, which are easily confused with clear skies, and in other cases, to the brightening effect of the regions around the solar disc. There are some studies that divide clouds into even more complex groups. Ref. [210] utilized five different feature extraction methods in an automatic training classification system to recognize cumulus, towering cumulus, cumulonimbus, clear sky, and other clouds. In this study, autocorrelation, cooccurrence matrices, edge frequency, Law's features, and primitive length were used. Ref. [181] extends the classification from five groups to seven cloud types by means of the k-NN (k-nearest neighbor) classification algorithm.