Abstract
This paper deals with the problem of designing an accurate and computationally fast model of a particular real photovoltaic module. There are a number of well known theoretical models, but they need the fine tuning of several parameters, whose values are often is difficult or impossible to estimate. The difficulty of these calibration processes has driven the research into approximation models that can be trained from data observed during the working operation of the plant, i.e. Artificial Neural Network (ANN) models. In this paper we derive an accurate ANN model of a real ATERSA A55 photovoltaic module, showing all the steps and electrical devices needed to reach that objective.
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López-Guede, J.M., Ramos-Hernanz, J.A., Graña, M. (2014). Artificial Neural Network Modeling of a Photovoltaic Module. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_40
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DOI: https://doi.org/10.1007/978-3-319-01854-6_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01853-9
Online ISBN: 978-3-319-01854-6
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