Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1551430 | Solar Energy | 2008 | 7 Pages |
Abstract
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/m2 and root mean square error of 0.385 MJ/m2. The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.
Keywords
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
J. Mubiru, E.J.K.B. Banda,