Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction
Joint Authors
Khatib, Tamer
Sopian, Kamaruzzaman
Mahmoud, M.
Mohamed, Azah
Source
International Journal of Photoenergy
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-24
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction.
The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN).
The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE).
Prediction results show that the GRNN exceeds the other proposed methods.
The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively.
FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions.
The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively.
ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%.
The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.
American Psychological Association (APA)
Khatib, Tamer& Mohamed, Azah& Sopian, Kamaruzzaman& Mahmoud, M.. 2012. Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction. International Journal of Photoenergy،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-510422
Modern Language Association (MLA)
Khatib, Tamer…[et al.]. Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction. International Journal of Photoenergy No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-510422
American Medical Association (AMA)
Khatib, Tamer& Mohamed, Azah& Sopian, Kamaruzzaman& Mahmoud, M.. Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction. International Journal of Photoenergy. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-510422
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-510422