Computation of beam solar radiation at normal incidence using artificial neural network

• Published in: Renewable energy Vol. 31; no. 10; pp. 1483 - 1491
• Main Authors: Alam, Shah, Kaushik, S.C., Garg, S.N.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-08-2006; Elsevier Science
• Subjects: Applied sciences; Artificial neural network; Beam solar radiation; Energy; Exact sciences and technology; Feedforward back-propagation; Natural energy; Reference clearness index; Solar energy; Solar radiation; Asia; India; Beam solar radiation; Feedforward back-propagation; Artificial neural network; Reference clearness index; Clearness index; Models; Neural network; Feedforward; Solar radiation; Artificial neural networl; Algorithm; Artificial intelligence
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2005.07.010

In this paper, an artificial neural network (ANN) model is developed for estimating beam solar radiation. Introducing a newly defined parameter, known as reference clearness index (RCI), computation of monthly mean daily beam solar radiation at normal incidence has been carried out. This RCI is defined as the ratio of measured beam solar radiation at normal incidence to the beam solar radiation as computed by Hottel's clear day model. Solar radiation data from 11 stations having different climatic conditions all over India have been used for training and testing the ANN. The feedforward back-propagation algorithm is used in this analysis. The results of ANN model have been compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 1.65–2.79% for Indian region.