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Authors: Chaimae Zedak ; Abdelaziz Belfqih ; Faissal El Mariami ; Jamal Boukherouaa ; Abdelmajid Berdai and Anass Lekbich

Affiliation: Energy and Electrical Systems Laboratory, National Higher School of Electricity and Mechanics, Hassan II University, Casablanca, Morocco

Keyword(s): Artificial neural networks; performance; short-term forecasting; wind power; wind speed.

Abstract: Wind energy forecasting is an important part of the electrical system because of its intermittent nature. It has become a challenge for many researchers to find the most accurate prediction method since an accurate, reasonable and scientific forecasting of electrical power is a critical step in planning the electricity grid, maintaining the supply-demand balance and more generally forming a scientific basis for the energy planning. This paper presents the prediction of wind power by applying the technique of neural networks to the power data of a wind farm in Spain with wind speed and wind direction data as these two parameters have an influence on wind power. The performance of the proposed neural network was evaluated according to the regression coefficient R and the Root Mean Square Error (RMSE) and by comparing the one hour ahead predicted values of wind power for May 31 to the real available values.

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Paper citation in several formats:
Zedak, C.; Belfqih, A.; El Mariami, F.; Boukherouaa, J.; Berdai, A. and Lekbich, A. (2020). Artificial Neural Networks for Short-term Wind Power Estimation. In Proceedings of the 1st International Conference of Computer Science and Renewable Energies - ICCSRE; ISBN 978-989-758-431-2, SciTePress, pages 21-25. DOI: 10.5220/0009776400210025

@conference{iccsre20,
author={Chaimae Zedak. and Abdelaziz Belfqih. and Faissal {El Mariami}. and Jamal Boukherouaa. and Abdelmajid Berdai. and Anass Lekbich.},
title={Artificial Neural Networks for Short-term Wind Power Estimation},
booktitle={Proceedings of the 1st International Conference of Computer Science and Renewable Energies - ICCSRE},
year={2020},
pages={21-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009776400210025},
isbn={978-989-758-431-2},
}

TY - CONF

JO - Proceedings of the 1st International Conference of Computer Science and Renewable Energies - ICCSRE
TI - Artificial Neural Networks for Short-term Wind Power Estimation
SN - 978-989-758-431-2
AU - Zedak, C.
AU - Belfqih, A.
AU - El Mariami, F.
AU - Boukherouaa, J.
AU - Berdai, A.
AU - Lekbich, A.
PY - 2020
SP - 21
EP - 25
DO - 10.5220/0009776400210025
PB - SciTePress

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