Prospective Energy Generation in Photovoltaic Systems, Based on the Comparison and Evaluation of Different Configurations Based on Artificial Neural Networks (ANN)

EasyChair Preprint no. 9091

Abstract

For photovoltaic systems to be sustainable over time, it is necessary to make better use of solar energy, so probabilistic predictions of variables that directly influence the production of electrical energy must be taken into account when making decisions. Solar irradiation is the important variable for these predictions, which allows managing the production of photovoltaic energy. In the present work, the application of RNA artificial neural networks properly configured to interact with the information that is available on climatological variables is proposed in order to be able to predict solar irradiation. The correlation between these variables has been analyzed in order to be able to compare different configurations of MLP-type neural networks for one variable and multiple variables, in addition to using GRU-type recurrent neural networks. According to the MAE, the GRU type networks are the ones that provide the best results, however, considering the ease of training, the MLP type networks are acceptable, being the multivariable type the ones that give the best results. Looking to the future, the use of these networks using pattern recognition in sky images considering the movement of clouds and the earth with respect to the sun is proposed.

Keyphrases: Artificial Neural Networks, irradiation, irradiation., modeling, Photovoltaic generator, Prediction fromenergy

@Booklet{EasyChair:9091,
  author = {Ramos Cutipa José Manuel and Condori Yucra Reynaldo and Shuta Llocclla Henry and Chayña Velasquez Omar and Paredes Pareja Walter Oswaldo and Vilca Choque Mary Cymbel and Beltrán Castañón Norman Jesús},
  title = {Prospective Energy Generation in Photovoltaic Systems, Based on the Comparison and Evaluation of Different Configurations Based on Artificial Neural Networks (ANN)},
  howpublished = {EasyChair Preprint no. 9091},

  year = {EasyChair, 2022}}