In this paper, artificial neural networks (ANNs) have been used for the performance ratio modelling of four photovoltaic (PV) modules. The PV modules are selected from three different silicon technologies including one monocrystalline, two polycrystalline, and one micromorph (a-Si/μc-Si) modules. The adopted ANN architecture is a multilayer perceptron (MLP). The inputs of the ANN models are the solar irradiance on the PV module plane and air ambient temperature, while the output is the PV module performance ratio. It is shown that ANN models with three layers and five hidden neurons accurately model the performance ratio regardless of PV module technology. The results obtained from the ANN model are compared with those obtained from the five parameter model (L5P). The model comparison is done through two widely used forecasting errors: the root mean square error (RMSE) and the mean absolute percentage of error (MAPE). The values of both RMSE and MAPE are less than 0.02 for MLP based models and are about three to nine times lower than those obtained from the electrical model. It is also shown that the poor fit of the L5P model is due to the bad estimation of series and shunt resistances.
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September 2018
Research Article|
October 23 2018
Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions
Alain K. Tossa;
Alain K. Tossa
1
KYA-Energy Group
, 08BP 81101 Agoenyivé, Lomé, Togo
2
LESEE-2iE, Laboratoire Energie Solaire et Economie d'Energie, Institut International d'Ingénierie de l'Eau et de l'Environnement
, 01 BP 594 Ouagadougou 01, Burkina Faso
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Y. M. Soro
;
Y. M. Soro
a)
2
LESEE-2iE, Laboratoire Energie Solaire et Economie d'Energie, Institut International d'Ingénierie de l'Eau et de l'Environnement
, 01 BP 594 Ouagadougou 01, Burkina Faso
a)Author to whom correspondence should be addressed: moussa.soro@2ied-edu.org. Tel.: (+226) 68 76 88 22. Fax: (+226) 25 49 28 01.
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Y. Coulibaly;
Y. Coulibaly
2
LESEE-2iE, Laboratoire Energie Solaire et Economie d'Energie, Institut International d'Ingénierie de l'Eau et de l'Environnement
, 01 BP 594 Ouagadougou 01, Burkina Faso
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Y. Azoumah;
Y. Azoumah
1
KYA-Energy Group
, 08BP 81101 Agoenyivé, Lomé, Togo
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Anne Migan-Dubois;
Anne Migan-Dubois
3
GeePs—Laboratoire Génie électrique et électronique de Paris
, 11, rue Joliot Curie, Plateau de Moulon, 91192 Gif sur Yvette Cedex, France
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L. Thiaw;
L. Thiaw
4
Ecole Supérieure Polytechnique de Dakar
, Corniche Ouest BP: 5085 Dakar-Fann, Senegal
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Claude Lishou
Claude Lishou
4
Ecole Supérieure Polytechnique de Dakar
, Corniche Ouest BP: 5085 Dakar-Fann, Senegal
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a)Author to whom correspondence should be addressed: moussa.soro@2ied-edu.org. Tel.: (+226) 68 76 88 22. Fax: (+226) 25 49 28 01.
J. Renewable Sustainable Energy 10, 053505 (2018)
Article history
Received:
May 30 2018
Accepted:
October 03 2018
Citation
Alain K. Tossa, Y. M. Soro, Y. Coulibaly, Y. Azoumah, Anne Migan-Dubois, L. Thiaw, Claude Lishou; Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions. J. Renewable Sustainable Energy 1 September 2018; 10 (5): 053505. https://doi.org/10.1063/1.5042217
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