Application of artificial neural networks for forecasting photovoltaic system parameters
- Authors: Miloudi L.1, Acheli D.1, Kesraoui M.1
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Affiliations:
- University M’HamedBougara of Boumerdès
- Issue: Vol 53, No 2 (2017)
- Pages: 85-91
- Section: Direct Conversion of Solar Energy to Electricity
- URL: https://journal-vniispk.ru/0003-701X/article/view/149271
- DOI: https://doi.org/10.3103/S0003701X17020104
- ID: 149271
Cite item
Abstract
The main element which justifies the installation of a photovoltaic system is the solar energy potential. Various structures of artificial neural networks (ANNs) are used for predicting the sun location, the global solar radiation (GSR) at horizontal and inclined plans. Real meteorological data have been exploited in order to validate the computation results. The ANNs are also carried out to predict the current-voltage characteristics of the photovoltaic module. It can be concluded that the ANNs effectively predict the behavior of photovoltaic system parameters with good a coefficient of determination.
About the authors
Lalia Miloudi
University M’HamedBougara of Boumerdès
Author for correspondence.
Email: lamiloudi@univ-boumerdes.dz
Algeria, Boumerdès
Dalila Acheli
University M’HamedBougara of Boumerdès
Email: lamiloudi@univ-boumerdes.dz
Algeria, Boumerdès
Mohamed Kesraoui
University M’HamedBougara of Boumerdès
Email: lamiloudi@univ-boumerdes.dz
Algeria, Boumerdès
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