Enhancing the efficiency of polytetrafluoroethylene-modified silica hydrosols coated solar panels by using artificial neural network and response surface methodology

Abstract

Abstract In this article, an attempt was made to improve the efficiency of coated solar panels by using artificial neural networks (ANNs) and response surface methodology (RSM). Using the spray coating technique, the glass surface of the photovoltaic solar panel was coated with silicon dioxide nanoparticles incorporated with polytetrafluoroethylene-modified silica sols. Multilayer perceptron with feed-forward back-propagation algorithm was used to develop ANN models for improving the efficiency of the coated solar panels. Out of the 200 sets of data collected, 75% were used for training and 25% were used for testing. On evaluating the models using performance indicators, a four-input technological parameter model (silicon dioxide nanoparticle quantity, coating thickness, surface temperature and solar insolation) with eight neurons in a single hidden layer combination was observed to be the best. The prediction accuracy indicator values of the ANN model were 0.9612 for the coefficient of determination, 0.1971 for the mean absolute percentage error, 0.2317 for the relative root mean square error and 0.00741 for the mean bias error. Using a central composite design model, empirical relationships were developed between input and output responses. The significance of the developed model was ascertained by using analysis of variance, up to a 95% confidence level. For optimization, the RSM was used, and a high efficiency of 17.1% was predicted for the coated solar panel with optimized factors; it was validated to a very high level of predictability. Using interaction and perturbation plots, a ranking of the parameters was done.