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Your search keyword '"Bessissa, Lakhdar"' showing total 14 results
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14 results on '"Bessissa, Lakhdar"'

10. Performance of Organic Solar Cells with Recovery

Author

Boukezzi Larbi, Djeddaoui Naas, and Bessissa Lakhdar

Subjects
Glovebox, Organic solar cell, Environmental science, Pulp and paper industry, Roof
Abstract

In this paper, we have studied the degradation of organic solar cells (OSCs) in different way considering the recovery phenomenon. The carried out work is based on the application of outdoor test in the SDU laboratory (Sonderborg Denmark) by using the protocol ISOS-O-1. The test took 14 days, this duration is divided into two parts: first part 5 days where the samples were put in the roof for the degradation for 7 hours and we measure the electric parameters before putting them on the roof and in the glovebox. In the second part the samples were put in the glovebox for all the remaining days and different parameters have been measured two times per-day (morning-evening) the duration of the second part is 9 days.

Published
2019

11. Degradation Behavior of Scalable Nonfullerene Organic Solar Cells Assessed by Outdoor and Indoor ISOS Stability Protocols

Author

Greenbank, William, primary, Djeddaoui, Naas, additional, Destouesse, Elodie, additional, Lamminaho, Jani, additional, Prete, Michela, additional, Boukezzi, Larbi, additional, Ebel, Thomas, additional, Bessissa, Lakhdar, additional, Rubahn, Horst-Günter, additional, Turkovic, Vida, additional, and Madsen, Morten, additional

Published
2020
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14. ARTIFICIAL NEURAL NETWORK BASED MODEL OF PHOTOVOLTAIC CELL.

Author

AZZOUZI, Messaouda, BESSISSA, Lakhdar, MOUSSA, Mona Fouad, POPESCU, Dumitru, and PETRESCU, Catalin

Subjects
PHOTOVOLTAIC cells, ARTIFICIAL neural networks, PERFORMANCE of photovoltaic cells, ELECTRIC potential, MACHINE learning, MATHEMATICAL models
Abstract

This work concerns the modeling of a photovoltaic cell and the prediction of the sensitivity of electrical parameters (current, power) of the six types of photovoltaic cells based on voltage applied between terminals using one of the best-known artificial intelligence technique, which is the Artificial Neural Networks. The results of the modeling and prediction have been shown and then compared between them. NEWFF learning algorithm was used with specified number of iteration that gave the best results. The error was calculated in all cases to check the accuracy of the used method. [ABSTRACT FROM AUTHOR]

Published
2017
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