Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

• Multilayer perceptrons are used to simulate the I–V curve of thin-film PV modules.
• APE from the spectral irradiance was added as an input variable to the network.
• A self-organised map is used to select the curves used for training the network.
• Curve error and maximum power error decrease when using this technique.
• This method could provide accurate estimation of the output of a PV plant.

In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation.

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