## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### Measuring Sensors Used in Solar Irradiance Forecasting
In terms of solar irradiance forecasting, two main systems distinguish themselves: hybrid satellite-sensors and hybrid camera-sensors. In the field of hybrid satellite-sensors, several publications can be highlighted. First, Ref. [33] proposes an ultra-short-term system that combines satellite imagery and weather information to support cloud motion predictions, specifically by means of wind data. However, Ref. [34] compares GHI forecasts at time horizons of 30, 60, 90, and 120 min, utilizing satellite imagery with the corresponding values measured by sensors at two different locations. The authors of [35] demonstrate that combining a forecast using an artificial neural network (ANN) model of ground-based data with satellite forecasts improves the global accuracy for few-hour forecasts. Conversely, Ref. [36] evaluated 110 days of satellite forecasts supported by ground-based measurements, specifically in San Diego, California. Additionally, Ref. [33] suggests an ultra-short-term GHI forecasting method by the combined use of weather information and cloud cover controlled through the satellite. The authors of [37] also contributed significantly by demonstrating that including satellite average albedos in ground-based data forecasting leads to satisfactory improvements. The use of measuring sensors in all-sky camera systems is also widespread, like in the satellite case. For instance, one study [38] combines cloud cover forecasts of solar disc using cameras with real-time ground-based measurements to extract parameters for forecasts. Another study [39] applies 99 pyranometers distributed over an area from 10 km to 12 km to validate a 2-month set of images. In contrast, Ref. [40] uses a network of pyrheliometers, pyranometers, and a ceilometer at the Plataforma Solar de Almeria in Spain to validate the results of DNI maps obtained by all-sky cameras. Recently, this study has been extended by the University of Oldenburg in Germany [41]. In addition, Ref. [42] uses historical power values from solar panels combined with sky images as input for their neural network. The authors of [43] place a pyranometer together with the cameras at each chosen location, while [44] employs pyranometers, pyrheliometers, and ceilometers, as well as weather stations at two energy centers in Newcastle and Canberra, Australia. Sky cameras, along with computational algorithms, offer a promising approach to predict and mitigate the impact of cloud cover on solar irradiance, as discussed in [45]. Meanwhile, Ref. [46] provides insights into the factors influencing the performance of ANN-based prediction models. In [47], all-sky image feature extraction is used to derive regional and global weights, which are then integrated with a long short-term memory (LSTM) training model to estimate solar irradiance. Furthermore, Ref. [48] introduces a machine learning framework that leverages all-sky cameras and satellite observations to enhance intra-hour irradiance forecasting. Finally, as mentioned above, NWP models make use of the extensive data volume gathered from various weather sensors and solar irradiance measurements. However, due to the sheer number of models available, this text focuses only on authors who have reviewed solar forecasting research based on the most widely used models [49; 50]. These models include global-scale models like GFS (Global Forecast System) [51; 52], medium-scale models such as ECMWF (European Centre for Medium-Range Weather Forecasts) [53; 54], and small-scale models like MM5 (Mesoscale Model version 5) [18; 55] and WRF (Weather Research and Forecasting) [56; 57].