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https://doi.org/10.13044/j.sdewes.d6.0226

Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study

Maamar Laidi   ORCID icon orcid.org/0000-0002-8977-9895 ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, BD de L’A.L.N Ain D’heb Médéa, Médéa, Algeria
Salah Hanini ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, BD de L’A.L.N Ain D’heb Médéa, Médéa, Algeria
Abdallah El Hadj Abdallah ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, BD de L’A.L.N Ain D’heb Médéa, Médéa, Algeria

Puni tekst: engleski, pdf (334 KB) str. 405-414 preuzimanja: 224* citiraj
APA 6th Edition
Laidi, M., Hanini, S. i El Hadj Abdallah, A. (2018). Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study. Journal of Sustainable Development of Energy, Water and Environment Systems, 6 (3), 405-414. https://doi.org/10.13044/j.sdewes.d6.0226
MLA 8th Edition
Laidi, Maamar, et al. "Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study." Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 6, br. 3, 2018, str. 405-414. https://doi.org/10.13044/j.sdewes.d6.0226. Citirano 31.07.2021.
Chicago 17th Edition
Laidi, Maamar, Salah Hanini i Abdallah El Hadj Abdallah. "Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study." Journal of Sustainable Development of Energy, Water and Environment Systems 6, br. 3 (2018): 405-414. https://doi.org/10.13044/j.sdewes.d6.0226
Harvard
Laidi, M., Hanini, S., i El Hadj Abdallah, A. (2018). 'Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study', Journal of Sustainable Development of Energy, Water and Environment Systems, 6(3), str. 405-414. https://doi.org/10.13044/j.sdewes.d6.0226
Vancouver
Laidi M, Hanini S, El Hadj Abdallah A. Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study. Journal of Sustainable Development of Energy, Water and Environment Systems [Internet]. 2018 [pristupljeno 31.07.2021.];6(3):405-414. https://doi.org/10.13044/j.sdewes.d6.0226
IEEE
M. Laidi, S. Hanini i A. El Hadj Abdallah, "Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study", Journal of Sustainable Development of Energy, Water and Environment Systems, vol.6, br. 3, str. 405-414, 2018. [Online]. https://doi.org/10.13044/j.sdewes.d6.0226

Sažetak
This work deals with the potential application of artificial neural networks to model sunshine duration in three cities in Algeria using ten input parameters. These latter are: year and month, longitude, latitude and altitude of the site, minimum, mean and maximum air temperature, wind speed and relative humidity. They were selected according to their availability in meteorological stations and based on the fact that they
are considered as the most used parameters by researchers to model sunshine duration using artificial neural networks. Several network architectures were tested to choose the most accurate and simple scheme. The optimum number of layers and neurons was determined by trial and error method. The optimized network was obtained using
Levenberg-Marquardt back-propagation algorithm, one hidden layer including 25 neurons with Tan-sigmoid transfer function. The model developed in this study has the ability to estimate sunshine duration with a mean absolute percentage error value equals to 2.015%, a percentage root mean square error of 2.741% and a determination
coefficient of 0.9993 during test stage.

Ključne riječi
Sunshine duration; Solar energy; Artificial neural networks; Root mean square error; Meteorological parameters

Hrčak ID: 206022

URI
https://hrcak.srce.hr/206022

Posjeta: 423 *