Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions-Reference-Cited by-同舟云学术

Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions

Published:2018-09 Issue:5 Volume:10 Page:053505
ISSN:1941-7012
Container-title:Journal of Renewable and Sustainable Energy
language:en
Short-container-title:Journal of Renewable and Sustainable Energy

Author:

Tossa Alain K.¹²,Soro Y. M.²^ORCID,Coulibaly Y.²,Azoumah Y.¹,Migan-Dubois Anne³,Thiaw L.⁴,Lishou Claude⁴

Affiliation:

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

3. GeePs—Laboratoire Génie électrique et électronique de Paris, 11, rue Joliot Curie, Plateau de Moulon, 91192 Gif sur Yvette Cedex, France

4. Ecole Supérieure Polytechnique de Dakar, Corniche Ouest BP: 5085 Dakar-Fann, Senegal

Publisher

AIP Publishing

Subject

Renewable Energy, Sustainability and the Environment

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5042217

Reference47 articles.

1. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

2. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks

3. Estimation of photovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations

4. Experimental analysis of the maximum power point's properties for four photovoltaic modules from different technologies: Monocrystalline and polycrystalline silicon, CIS and CdTe

5. PV technologies performance comparison in temperate climates

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