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Photovoltaic module simulation by neural networks using solar spectral distribution

A novel methodology based on artificial neural networks is proposed as an alternative to algebraic and numerical procedures to determine the I‐V curve of a module under different conditions. Although there are methods that use neural networks for approximating the I‐V curve, this is the first time that the measurement of the spectrum is incorporated as an input. In addition, a suitable selection of the training samples used to build the model is fundamental in order to get an accurate approximation. This is why a training sample selection based on a Kohonen self‐organizing map is performed in this paper instead of a random selection. With the use of this preliminary step, the performance of the network trained with spectral information improves over the one without spectral information. Copyright © 2012 John Wiley & Sons, Ltd.