ML for prediction of Solar Radiation From a practical agronomi | Big Data Science

ML for prediction of Solar Radiation
From a practical agronomic point of view, an accurate assessment of solar radiation is vital because it is a key factor in crop development. Most existing weather stations around the world have temperature and rain sensors, but only some of them measure solar radiation. Measuring solar radiation is usually very expensive due to complex sensors (pyranometers and radiometers) and a lack of reliable data. Therefore, a group of researchers from the University of Cordoba has developed ML-models to predict solar radiation in southern Spain and the United States.
The created ML-models are based not only on actual measurements, but are enriched with data on the geoclimatic conditions of the area (aridity, distance to the sea, altitude, etc.). To estimate daily solar radiation, the proposed neural network algorithms from current data only need information about the air temperature, which is relatively cheap due to inexpensive sensors and IoT technologies. Bayesian algorithms are used to optimize hyperparameters, and the models themselves can be adapted to any terrain, depending on its aridity.
https://techxplore.com/news/2021-07-machine-based-thermal-solar.html