Simulation of Dye Synthesized Solar Cell using Artificial Neural Network

Abstract

The primary goal of present examination is to foresee every day worldwide solar cell efficiency in view of meteorological factors, utilizing distinctive counterfeit neural system (ANN) procedures. In the present examination we report the impact of Dye Synthesized solar cell. A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Dye Synthesized solar cell based on 100 experimental sets. In the present examination we report the impact of Dye Synthesized solar cell. The effect of operational parameters such as short circuit current (Jsc),Open circuit voltage(Voc),Fill factor(FF) were studied to optimize the conditions to check the efficiency of Dye Synthesized solar cell. Experimental results showed that the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 20 neurons and a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm (LMA) was used with a minimum mean squared error (MSE) of 0.00350141. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.9993 for six model variables used in this study.