Prediction of monthly mean daily global solar radiation using Artificial Neural Network
- 237 Downloads
In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu, India. Various geographical, solar and meteorological parameters of three different locations with diverse climatic conditions were used as input parameters. Out of 565 available data, 530 were used for training and the rest were used for testing the artificial neural network (ANN). A 3-layer and a 4-layer MLFF networks were developed and the performance of the developed models was evaluated based on mean bias error, mean absolute percentage error, root mean squared error and Student’s t-test. The 3-layer MLFF network developed in this study did not give uniform results for the three chosen locations. Hence, a 4-layer MLFF network was developed and the average value of the mean absolute percentage error was found to be 5.47%. Values of global radiation obtained using the model were in excellent agreement with measured values. Results of this study show that the designed ANN model can be used to estimate monthly mean daily global radiation of any place in Tamil Nadu where measured global radiation data are not available.
KeywordsArtificial neural network back propagation global radiation multilayer perceptron network Tamil Nadu
Authors would like to convey their sincere gratitude to Dr K S Reddy, Indian Institute of Technology, Chennai, India for having given some valuable suggestions regarding the presented work. They also wish to express their sincere gratitude to the reviewers for the valuable suggestions which enabled to improve the manuscript. They thank the colleagues in the Department of English who helped in improving the presentation.
- Ahmad F and Ulfat I 2004 Empirical model for the correlation of monthly average daily global solar radiation with hours of sunshine on a horizontal surface at Karachi, Pakistan; Turk. J. Phys. 28 301–307.Google Scholar
- Augustine C and Nnabuchi M N 2009 Correlation between sunshine hours and global solar radiation in Warri, Nigeria; The Pacific J. Sci. Tech. 10 574–579.Google Scholar
- Daniel W W and Terrell J C 1992 Business statistics for management and economics; 6th edn, London, Houghton Mifflin Company, p. A53.Google Scholar
- Fadare D A, Irimisose I, Oni A O and Falana A 2010 Modeling of solar energy potential in Africa using an artificial neural network; Am. J. Sci. Indus. Res. 1 144–157.Google Scholar
- Hagan M T, Demuth H B and Beale M 1996 Neural Network Design, PWS Publishing Company, Boston Ch. 2, 10, 11 & 12.Google Scholar
- Jamil Ahmad M and Tiwari G N 2010 Solar radiation models-review; Int. J. Energy Environ. 1 513–532.Google Scholar
- Krishnaiah T, Srinivasa Rao S, Madhumurthy K and Reddy K S 2007 Neural network approach for modeling global solar radiation; J. Appl. Sci. Res. 3 1105–1111.Google Scholar
- Mohandes M, Rehman S and Halawani T 1998 Estimation of global solar radiation using artificial neural networks; In: Sixth Arab International Solar Energy Conference, Muscat, Sultanate of Oman, 29th March–1st April.Google Scholar
- Salai Selvam V and Shelbagadevi S 2010 Classification of characteristic waves of sleep EEG using first and higher order statistics and Neural Network; CiiT International Journal of Artificial Intelligent Systems and Machine Learning 2 142–150.Google Scholar