Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data
| Autor: | Kondo H. Adjallah, P. A. Ndiaye, M. Ndongo, Alexandre Sava, Vincent Sambou, C. M. F. Kebe, Boudy Bilal |
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| Přispěvatelé: | Laboratoire de Conception, Optimisation et Modélisation des Systèmes (LCOMS), Université de Lorraine (UL), laboratoire d'Etudes et de recherches en Statistiques et Développement (LERSTAD), Université Gaston Bergé Sénégal, Ecole Nationale d'Ingénieurs de Metz (ENIM), Institut Fondamental d'Afrique Noire (IFAN), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD), IFAN |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Meteorology
020209 energy 02 engineering and technology 7. Clean energy Turbine Wind speed Data modeling [SPI.AUTO]Engineering Sciences [physics]/Automatic [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] 0202 electrical engineering electronic engineering information engineering Astrophysics::Solar and Stellar Astrophysics Physics::Atmospheric and Oceanic Physics ComputingMilieux_MISCELLANEOUS [STAT.AP]Statistics [stat]/Applications [stat.AP] Wind power Artificial neural network business.industry [SPI.NRJ]Engineering Sciences [physics]/Electric power Humidity Wind direction Power (physics) 13. Climate action Physics::Space Physics Environmental science business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | 2018 IEEE International Conference on Industrial Technology (ICIT) 2018 IEEE International Conference on Industrial Technology (ICIT), Feb 2018, Lyon, France. pp.1085-1092, ⟨10.1109/ICIT.2018.8352329⟩ ICIT |
| DOI: | 10.1109/ICIT.2018.8352329⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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