Solar Radiation Prediction Using Machine Learning Techniques

The proposal of a solar radiation estimation model using Machine Learning is submit, the processing of meteorological data measured by satellite and data measured on the land is made. The model uses two solutions using an artificial neural network and robust linear regression the climatic variables used as input to the model are solar radiation, temperature and clarity index, all get from satellite data. The main aim of this work is to propose a model that allows using the satellite data to get an estimate of the behavior of the solar resource on the ground, reducing the error between the satellite data and the data measured on the ground. The results of the model got by training an artificial neural network with hidden layers are submit, here the normal distributions of the data reported by the satellite and the data got by the proposed model are submit. In addition, the results of the daily average got by the model and the daily average values measured on land are submit. I conclude it by proposing a second estimation model using robust linear regression. A proposed model adjusted to the assumptions made during the regression process and acceptable results to those got by the satellite and reported by other works are got.