Abstract
Global Solar Radiation (GSR) plays an important role in establishing the correct picture of the photo voltaic (PV) systems. The accurate measurement of GSR for a particular location helps in the better and optimal usage of PV systems. In respect of this, the present work estimates daily GSR for an Indian city Varanasi (25 16'N, 82 57'E) by utilizing the benefits of artificial intelligence based methodologies. Several combinations of the input variables are organized from a data set consisting of average temperature, minimum temperature, maximum temperature, relative humidity, wind velocity, extraterrestrial radiation and precipitation. Artificial Neural Networks (ANNs) with different architectures are considered for predicting the daily GSR for the location in India. Large numbers of data are used for training the feed forward ANNs to obtain the best model in predicting the GSR. Veracity of the proposed models with different input combinations are tested with the help of statistical indicators like, Mean Absolute Percentage Error (MAPE), Root Means Square Error (RMSE) and Mean Bias Error (MBE). The results of the proposed approach were compared with other empirical and hybrid models presented in the past literature. The proposed method has outperformed the other models in terms of quality of the solution and computational efficiency.Â
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Renewable Energy Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
