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High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature

Almonacid, F., Fernández, E.F., Mallick, T.K., Pérez-Higueras, P.J.
Energy 2015 v.84 pp. 336-343
energy, light intensity, neural networks, prediction, solar cells, solar collectors, systems engineering, temperature
The electrical modelling of HCPV (high concentrator photovoltaic) modules is a key issue for systems design and energy prediction. However, the electrical modelling of HCPV modules shows a significantly level of complexity than conventional photovoltaic technology because of the use of multi-junction solar cells and optical devices. In this paper, a method for the simulation of the I–V curves of a HCPV module at any operating condition is introduced. The method is based on three different ANN (artificial neural networks)-based models: one to spectrally correct the direct normal irradiance, one to predict the cell temperature and one to generate the I–V curve of the HCPV module. The method has the advantage that is fully based on atmospheric parameter and outdoor measurements. The analysis of results shows that the method accurately predicts the I–V curve of a HCPV module for a wide range of atmospheric operating conditions with a RMSE (root mean square error) ranging from 0.19% to 1.66% and a MBE (mean bias error) ranging from −0.38% to 0.40%.