Using Artificial Neural Networks to Predict Direct Solar Irradiation
Author
Source
Advances in Artificial Neural Systems
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-10-11
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda.
Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications.
Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation.
A correlation coefficient of 0.998 was obtained with mean bias error of 0.005 MJ/m2 and root mean square error of 0.197 MJ/m2.
The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model.
The application of the proposed ANN model can be extended to other locations with similar climate and terrain.
American Psychological Association (APA)
Mubiru, James. 2011. Using Artificial Neural Networks to Predict Direct Solar Irradiation. Advances in Artificial Neural Systems،Vol. 2011, no. 2011, pp.1-6.
https://search.emarefa.net/detail/BIM-449069
Modern Language Association (MLA)
Mubiru, James. Using Artificial Neural Networks to Predict Direct Solar Irradiation. Advances in Artificial Neural Systems No. 2011 (2011), pp.1-6.
https://search.emarefa.net/detail/BIM-449069
American Medical Association (AMA)
Mubiru, James. Using Artificial Neural Networks to Predict Direct Solar Irradiation. Advances in Artificial Neural Systems. 2011. Vol. 2011, no. 2011, pp.1-6.
https://search.emarefa.net/detail/BIM-449069
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-449069