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Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks
Joint Authors
Tadili, Rachid
Loutfi, Hanae
Bernatchou, Ahmed
Raoui, Younès
Source
International Journal of Photoenergy
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-11
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco).
Results from this analysis are crucial for the conception of any solar energy system.
Especially, for the concentrating ones, as direct component is seldom measured.
For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters.
Three years of hourly data were available for this study.
For each solar component’s prediction, different combinations of inputs as well as different numbers of hidden neurons were considered.
To evaluate these models, the regression coefficient (R2) and normalized root mean square error (nRMSE) were used.
The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively.
Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation.
As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.
American Psychological Association (APA)
Loutfi, Hanae& Bernatchou, Ahmed& Raoui, Younès& Tadili, Rachid. 2017. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
Modern Language Association (MLA)
Loutfi, Hanae…[et al.]. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
American Medical Association (AMA)
Loutfi, Hanae& Bernatchou, Ahmed& Raoui, Younès& Tadili, Rachid. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168232