Artificial Intelligence Based Global Solar Radiation Prediction

Solar energy is one of the cleanest renewable energy sources and has no environmental impact. Solar radiation data is important to solar engineers, designers and architects which is also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Solar energy is mainly used to meet the growing electricity demand and decline the amount of CO2 emission thus preserving fossil fuels and natural resources. The temperature and sunshine duration are measured by most of the meteorological services all over the world but global solar radiation measurements are limited due to the restricted number of solar radiation measuring stations and some of the data are missing. In order to estimate the solar radiation in the other areas where the meteorological stations are not established, the theoretical solar radiation estimation models proposed by various researchers have proved handy. One of the main important assignments is to recognize the site with high solar energy potential for renewable power generation. This assists in accomplishing the target of the Indian solar mission project by the year 2022. The present work aims at the prediction of solar radiation using artificial neural network models which are applied to four different locations across India. As validation, measured and estimated solar radiation data were analyzed in terms of the square of the correlation coefficient and RMSE. The outcomes of this study will play a vital role in the estimation of global solar radiation with less percentage of error. 

Keywords: Artificial intelligence, ANN, Global solar radiation, Prediction, Solar energy.