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Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data

Abstract : This paper deals with the prediction of wind turbines power output and proposes an approach to building a prediction model using the Artificial Neural Networks (ANN). The wind speed and output power measured on the site of Sendou, in Senegal, were used to identify the structure of the ANN. Spatiotemporal data on the climatic variables (wind speed, solar radiation, temperature, humidity, wind direction) collected on the same site were used to train the ANN. Data collected on three other sites (Goback, Keur Abdoul Ndoye and Sine Moussa Abdou), located on the northwest coast of Senegal, were used to validate the model and to analyze the influence of the spatial climatic variables on the performance of the model. Results showed the interest of considering climatic variables (wind speed, wind direction, solar radiation, temperature and humidity) as inputs to the ANN for wind turbines output power prediction. Further, this study showed that the prediction of the produced power depends strongly on the characteristics of the sites and the direction of the wind.
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https://hal.univ-lorraine.fr/hal-03059781
Contributor : Alexandre SAVA Connect in order to contact the contributor
Submitted on : Sunday, December 13, 2020 - 9:56:13 AM
Last modification on : Thursday, May 5, 2022 - 2:50:02 PM

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B. Bilal, M. Ndongo, K. Adjallah, Alexandre Sava, Cheikh M. F. Kebe, et al.. Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data. 2018 IEEE International Conference on Industrial Technology (ICIT), Feb 2018, Lyon, France. pp.1085-1092, ⟨10.1109/ICIT.2018.8352329⟩. ⟨hal-03059781⟩

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