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© 2007, INSInet Publication Neural Network Approach for Modelling Global Solar Radiation

By T. Krishnaiah, S. Srinivasa Rao, K. Madhumurthy and K. S. Reddy

Abstract

Abstract: India lies in sunny belt between 6 ° N and 32 ° N latitudes, its geographical position favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for estimating hourly global solar radiation (HGSR) in India. The ANN models are presented and implemented on real meteorological data. The solar radiation data from seven stations are used for training the ANN and data from two stations are used for testing the predicted values. Multi layer feed forward neural network with backpropagation learning is used for the modelling. Forecasting performance parameters such as root mean square error (RMSE), mean bias error (MBE) and absolute fraction of 2 variance (r) are presented for the model. The average values of MBE, RMSE and absolute fraction of 2 variance (r) for testing locations are found respectively 0.3133%, 4.61 % and 0.999954. A comparison of estimated global solar radiation with well-known models is carried out. The ANN model predicts better than other models. The estimated global solar radiation data are in reasonable agreement with the actual values. The results indicate the generalization capability of the ANN technique over unseen data and its ability to produce accurate predictions. Key words: hourly global solar radiation, artificial neural networks, modelling, root mean square error, mean bias erro

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.133.9870
Provided by: CiteSeerX
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