Photovoltaic electric power estimation with a machine learning algorithm based on neural networks and validated with deterministic approaches

Photovoltaic generation systems are widely used around the electrical network. There is a large variety of photovoltaic models for power production estimation. However, the use of machine learning algorithms in this application is yet to be explored. In this paper, a comparison between statistical and deterministic approaches is presented. The statistical models proposed use machine learning methodologies with neural network algorithms and automatic strategies to clean the data. To validate the model a comparison with two different deterministic models in different situations and locations is analyzed. The accuracy of the neural network algorithms when trained and tested in the same location makes the proposed model a good performance ratio tool for a photovoltaic power station.