Mostrar el registro sencillo del ítem
A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation
| dc.rights.license | open | en_US |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | BOUSSAADA, Zina | |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | CUREA, Octavian
ORCID: 0000-0002-5030-2088 IDREF: 68259131 | |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | REMACI, Ahmed | |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | CAMBLONG, Haritza | |
| dc.contributor.author | MRABET BELLAAJ, Najiba | |
| dc.date.accessioned | 2023-05-09T07:58:06Z | |
| dc.date.available | 2023-05-09T07:58:06Z | |
| dc.date.issued | 2018-03 | |
| dc.identifier.issn | 1996-1073 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/173572 | |
| dc.description.abstractEn | The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically. | |
| dc.language.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject.en | prediction | |
| dc.subject.en | solar radiation | |
| dc.subject.en | clear sky model | |
| dc.subject.en | cloud cover | |
| dc.subject.en | Nonlinear Autoregressive Exogenous (NARX) | |
| dc.title.en | A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.3390/en11030620 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics] | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Energie électrique | en_US |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | en_US |
| bordeaux.journal | Energies | en_US |
| bordeaux.volume | 11 | en_US |
| bordeaux.hal.laboratories | ESTIA - Recherche | en_US |
| bordeaux.issue | 3 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | Bordeaux Sciences Agro | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-01933133 | |
| hal.version | 1 | |
| hal.export | false | |
| workflow.import.source | hal | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Energies&rft.date=2018-03&rft.volume=11&rft.issue=3&rft.eissn=1996-1073&rft.issn=1996-1073&rft.au=BOUSSAADA,%20Zina&CUREA,%20Octavian&REMACI,%20Ahmed&CAMBLONG,%20Haritza&MRABET%20BELLAAJ,%20Najiba&rft.genre=article |
