Modeling of Photovoltaic characteristics of pyronine thin film/P-Si single

Abstract

In this article, the Photovoltaic properties of pyronine (G) thin film/P-Si solar cell have been stimulated by artificial neural networks (Anns). For this purpose, the experimental data of the (current-voltage) in both the darkness and under illumination at different distances were determined. The best neural network structure which achieves minimum mean squared error (MSE) has been obtained. High precision comparisons between (Ann) outputs and the experimental data as targets have been carried out. This comparison demonstrates the excellent closeness between the objects and the trained outputs. Also, it found that the (Ann) model successfully predicts new results that are not measured experimentally with high accuracy. Modeling results are clear proof that the (Ann) model is a really powerful tool in predicting the photovoltaic properties of pyronine and has an excellent ability to discover the forms of objects.