[u' @article{lopez_guede_dual_2017, title = {Dual model oriented modeling of monocrystalline {PV} modules based on artificial neuronal networks}, doi = {https://doi.org/10.1016/j.ijhydene.2017.02.062}, abstract = {The deep insight into the different elements that compose photovoltaic (PV) systems is capital to boost the optimization of each one of them and consequently, increment of the overall performance of the whole PV systems. In this paper we address the open problem of obtaining empirical accurate models of monocrystalline PV modules in a systematic and unattended fashion. In order to tackle this issue, we used a dual model oriented modeling approach based on artificial neural networks (ANN) due to their advantages, being the generalization capability the most outstanding one. We tried two different model approaches with different input/outputs specifications to learn the electrical behavior of a monocrystalline PV module Atersa A-55 placed on the roof of the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). Following these approaches we found two season oriented models of IPV with a RMSE accuracy of 0.20 mA and 0.26 mA respectively, which is better than the precision of the measurement devices. After comparing these results with the state-of-art ones, we conclude that we have outperformed the previously existing results.}, journal = {International Journal of Hydrogen Energy}, author = {Lopez Guede, Jose Manuel and Ramos Hernanz, Jose Antonio and Zulueta, Ekaitz and Fernandez Gamiz, Unai and Azkune, Gorka}, month = mar, year = {2017}, keywords = {Artificial Intelligence, JCR4.229, Neural Networks, Q1, machine learning}, } ']