Abstract
Predicting the response of solar panels has a big potential impact on the economical viability of the insertion of alternative energy sources in our societies, diminishing the dependence on polluting fossil fuels. In this paper we approach the modeling of the electrical behavior of a commercial photovoltaic module Atersa A-55 using Extreme Learning Machines (ELMs). The training and validation data were extracted from the response of a real photovoltaic module installed at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). The resulting predictive model has one input (\(V_{PV}\)) and one output (\(I_{PV}\)) variables. We achieve a Root Mean Squared Error (RMSE) of 0.026 in the electrical current measured in Amperes.
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Acknowledgments
The research was supported by the Computational Intelligence Group of the Basque Country University (UPV/EHU) through Grant IT874-13 of Research Groups Call 2013–2017 (Basque Country Government).
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Lopez-Guede, J.M., Ramos-Hernanz, J.A., Estevez, J., Garmendia, A., Torre, L., Graña, M. (2018). Electrical Behavior Modeling of Solar Panels Using Extreme Learning Machines. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_61
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DOI: https://doi.org/10.1007/978-3-319-92639-1_61
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