Prediction of monthly mean daily global solar radiation using Artificial Neural Network

• Published in: Journal of Earth System Science 2012-12, Vol.121 (6), p.1501-1510
• Main Authors: SIVAMADHAVI, V, SELVARAJ, R SAMUEL
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Auroral kilometric radiation; Back propagation networks; Climatic conditions; climatic factors; Daily; Earth and Environmental Science; Earth Sciences; earth system science; Error analysis; geographical variation; Global radiation; Mathematical models; Meteorological parameters; Meteorology; Monthly; Multilayers; Neural networks; Parameters; prediction; Propagation; Radiation data; Radiation measurement; Solar radiation; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; t-test; Ultraviolet radiation
• ISSN: 0253-4126; 0973-774X
• DOI: 10.1007/s12040-012-0235-1

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.