Influential Study and Development of Global Solar Radiation Prediction Model Using ANN

An increase in global air pollution level diminishes the surface solar radiation due to prolonged usage of fossil fuel combustion. Hence, solar energy is used as an alternate and eco-friendly resource to improve the human health and other environmental issues prevailing around the world. In this paper, influential study of meteorological parameters and air pollutions data was analysed using ANOVA and their impact on solar radiation was identified using Response surface methodology (RSM). From the study, 8 parameters were considered as influential parameters. The influential parameters were considered in predicting global solar radiation using feedforward back-propagation neural network model in Alandur, Chennai. The accuracy and performance of the model showed 0.48 as Mean Square Error (MSE) and a correlation coefficient (R) of 0.89 was observed. From the results, it was observed that the air pollution concentration had a greater effect on solar radiation than meteorological parameters.