SANSA, Ines; BOUSSAADA, Zina; BELLAAJ, Najiba Mrabet

doi:10.1109/ICGE.2014.6835397

BOUSSAADA, Zina
ESTIA INSTITUTE OF TECHNOLOGY

BELLAAJ, Najiba Mrabet

Idioma

Communication dans un congrès

Este ítem está publicado en

2014 International Conference on Green Energy, 2014-03-25, Sfax. p. 54-59

IEEE

Resumen en inglés

The integration of photovoltaic (PV), intermittent and uncontrollable power, into the electrical grid has become one of the major challenges for power system operators. Therefore the PV power forecasting can be beneficial in system planning and balancing energies. In this paper the PV power forecasting of a real generator [1] is presented. Different Artificial Neural Networks (ANN) strategies are used to forecast the PV power from meteorological variables, the radiation and the temperature. Simulation results correspond to each ANN strategy are presented, discussed and compared. The Nonlinear Auto Regressive models with eXogenous input (NARX model) is the dynamic ANN chosen to use in this work. Its performances have proved in the different time frame PV power forecasting. The impact of type season's on PV power forecasting performances is also presented in the second part of this paper.< Leer menos

Palabras clave en inglés

PV power forecasting

ANN

NARX model

model identification

URI

https://oskar-bordeaux.fr/handle/20.500.12278/203716

DOI

http://dx.doi.org/10.1109/ICGE.2014.6835397

Centros de investigación

ESTIA - Recherche

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PV power forecasting using different Artificial Neural Networks strategies

Idioma

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Resumen en inglés

Palabras clave en inglés

URI

DOI

Centros de investigación