## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### All-Sky Cameras
Ongoing research aims to optimize these novel predictive methodologies by developing higher-accuracy data acquisition systems that obtain local information from solar power plants. Two techniques have emerged in this context: the use of cameras that capture cloud images for very short-term forecasting, and irradiance sensor networks. However, the latter has a disadvantage, as it requires a large installation area for all the sensors to be sufficiently far from the solar farm to obtain accurate cloud cover forecasts with adequate separation  
Figure 1: Distortion correction of fisheye lens. Figure 2: All-sky camera modalities and associated images. This figure presents various modalities of all-sky cameras, including examples of the images captured by each type. from each other. This prevents the redundancy of the obtained information. Unfortunately, locating large areas near a solar power plant for such a system can be unviable in many cases. Therefore, the use of cameras that capture cloud images provides a more feasible option for acquiring local data, improving the accuracy of solar power plant forecasts. In contrast, it is possible to obtain an accurate characterization of the cloud cover at a given time and place with just a few cameras installed around a solar farm. Although to obtain three-dimensional estimations it is essential to utilize at least two cameras, despite this, it is still a lower-cost infrastructure in terms of investment and dimensions compared to sensor networks. Regarding the spatial horizon that can be covered by solar forecasts, two types can be distinguished: Firstly, point forecasts, which predict solar occlusion by clouds and, therefore, only process forecasts for the camera location. Secondly, forecasts that calculate the height of the cloud base [91], an aspect that allows the cloud shadow projection on the ground to be deduced [92]. The total prediction area covered by these systems depends on the configuration in which the array of cameras is installed, typically ranging several kilometers. However, a disadvantage of using a single camera is the inability to accurately characterize multi-layer cloudy sky conditions due to its limited 2D view [93; 94]. In cases where the system is equipped with multiple cameras, methods have been developed to model clouds in three dimensions and account for up to four different heights. In this regard, geolocation of clouds can be achieved by stereoscopic approaches [95] or via complementary remote sensors to calculate cloud heights (ceiometers [96; 97]) [39; 98; 99]. Typically, the research that calculates cloud height via multi-camera systems aims to use this information to perform a cloud projection over a solar farm, i.e., so-called solar irradiance maps. For example, Ref. [40], spatially distributed four all-sky imagers to estimate predictions by means of individual 3D models for each cloud, extracting characteristics such as height, position, surface, volume, transmittance, motion vector, etc. The same author [98; 100], a year later, downscaled the system to two 3 megapixel (MP) cameras to correlate height measurements with transmittance measurements of the clouds by employing pyrheliometers. Alternatively, Ref. [101] separates 4 cameras at 500 m and 900 m distances from each other in order to produce sets of DNI maps every minute in a 30 km \(\times\) 30 km region at a spatial resolution of up to 10 m. Meanwhile, Ref. [102] proposes a network of three cameras to provide sufficient coverage for a 32 MW photovoltaic solar power plant, which estimates the height and motion of each cloud layer based on the multiple images obtained. Moreover, Ref. [90] suggests a 4-camera system (WobaS-4 cam, 3 MP and 6 MP) to obtain irradiance maps in a 50 MW solar field, which are evaluated with a shadow camera reference system. In relation to single-camera systems, investigations commonly focus on cloud tracking and/or classification in order to predict the instant when the sun will be covered. For example, Ref.