Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Alexa Apaza-Pinto, Jose Esquicha-Tejada, Patricia Lopez-Casaperalta, Jose Sulla-Torres

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

One of the critical aspects in the mining sector is energy, being of great importance for the operation since if it were to stop, one of the consequences would be the loss of large amounts of money. The research objective is to predict the State of Charge of Batteries of equipment powered by photovoltaic solar panels in the mining sector based on automatic supervised learning techniques. A monitoring system records each energy variable programmed in the photovoltaic system, for which an analysis of the data extracted from the monitoring system was carried out. The data were evaluated using automatic supervised learning techniques using the RapidMiner tool, whose prediction average was 90.12%. The technique of automatic supervised learning of artificial neural networks was chosen to predict the state of charge of batteries for photovoltaic systems. A software tool was built with the neural network. The analysis and discussion of the results of the training of the model were carried out, the contribution of this research being to determine the prediction of the state of charge of batteries in photovoltaic systems in the mining sector using techniques of supervised machine learning which was the neural network. Finally, with the model correctly trained, validation was carried out that allowed comparing the predictive data with the data in real-time, obtaining a good relationship and satisfactory results.

Idioma originalInglés
Páginas (desde-hasta)134307-134317
Número de páginas11
PublicaciónIEEE Access
Volumen10
DOI
EstadoPublicada - 2022
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
© 2013 IEEE.

Huella

Profundice en los temas de investigación de 'Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector'. En conjunto forman una huella única.

Citar esto