Journal
SOLAR ENERGY
Volume 82, Issue 2, Pages 181-187Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2007.06.003
Keywords
artificial neural networks; global solar irradiation; sunshine hours; cloud cover; maximum temperature; model
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This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m(2) and root mean square error of 0.385 MJ/m(2). The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model. (c) 2007 Elsevier Ltd. All rights reserved.
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