## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### Measuring Sensors Used in Solar Irradiance Forecasting  
As introduced earlier in this section, solar irradiance measuring sensors are often used in hybrid prediction systems where they serve as backup technology and/or validate data obtained from satellite, camera, or all-sky camera network images. However, the sensors used to measure weather variables and solar irradiance have an even greater impact when utilized as initial data inputs for numerical weather prediction (NWP), alongside radiosonde and meteorological satellite data. In these models, the system of differential equations is quite complex and can only be solved through laborious numerical methods. This is why it is known as numerical prediction. Additionally, the amount of data required for these models is enormous. These techniques were only developed with the advent of computers, and they only began to achieve a certain degree of accuracy when computing power significantly increased. New studies are published every year with the aim of expanding the possibilities for predicting solar radiation. One of the most recent studies [14] presents new prediction models that use the latest available technology. Various studies demonstrate significant progress in improving the efficiency of solar production through the prediction of irradiation [15]. These results showcase the potential of solar forecasting methods to enhance the effectiveness of solar energy production. Several noteworthy authors have implemented direct measurement methods for weather variable forecasting. On one hand, Ref. [16] uses present values of mean daily solar irradiance and air temperature to predict solar irradiance for a 24 h horizon. Conversely, Ref. [17] utilizes adaptive linear models of solar power data from 21 rooftop PV systems to forecast values for every hour up to a horizon of 36 h.  
In contrast, Ref. [18] takes irradiance values every 1 and 10 min from pyranometers located at different sites to forecast at 2 h resolution. Another study by [19] collects irradiance values from a weather station every 30 min and compares them to a set of profiles representing the energy patterns. In another approach, Ref. [20] recommends optimum locations for an irradiance sensor network to cover an area of 50 km \(\times\) 50 km with a prediction frame of 30 s to 6 h. Another recommendation is including the use of an automated solar panel tracking system [21]. To obtain maximum solar energy, this system ensures that the solar panel is placed at a 90-degree angle to the sun's position. Moreover, historical data from multiple sensor measurements such as irradiance [22; 23; 24], air temperature [24; 25; 26], aerosol optical depth (AOD) [27; 28; 29], or relative humidity [24; 29] can be processed to predict solar energy output. By analyzing these historical measurements, clear patterns can be identified, leading to more accurate predictions. As the need for improved solar collection technology becomes increasingly pressing, new methods for climate prediction are being developed. For example, one study [30] focuses on developing automated methods to estimate the spectrally resolved normal direct irradiance for solar energy applications. In another study [31], possible solutions to the problem of sensors in the shortwave regime (0.2-2.5 \(\upmu\)m) are evaluated by simulating the spectral response to the standard AM 1.5 solar spectrum. These efforts demonstrate the ongoing commitment to advancing solar forecasting technology to improve the performance of solar energy systems. The effect of feature selection on solar prediction is also a crucial consideration. In [32], learning models are applied to determine the significance of features in predicting direct normal irradiance (DNI) data. Understanding which features are most important can improve the accuracy and efficiency of solar irradiance forecasting models. To provide more versatile and efficient systems, the use of backup or validation technology is becoming more common in hybrid solar irradiance forecasting.