Original scientific paper
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.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
Abstract
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.
Keywords
Sunshine duration; Solar energy; Artificial neural networks; Root mean square error; Meteorological parameters
Hrčak ID:
206022
URI
Publication date:
30.9.2018.
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