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Engineering Review : International journal for publishing of original researches from the aspect of structural analysis, materials and new technologies in the field of mechanical engineering, shipbuilding, fundamental engineering sciences, computer sciences, electrical engin, Vol. 42 No. 3, 2022.

Original scientific paper

https://doi.org/10.30765/er.1632

Wind energy potential estimation using neural network and SVR approaches

Adekunlé Akim Salami orcid id orcid.org/0000-0002-0917-1231 ; Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, P.O. Box: 1515 Lomé, TOGO
Pierre Akuété Agbessi orcid id orcid.org/0000-0002-2627-6043 ; Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, P.O. Box: 1515 Lomé, TOGO
Seibou Boureima ; Mines, Industry and Geology school of Niamey, Niger
Ayité S. Akoda Ajavon ; Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, P.O. Box: 1515 Lomé, TOGO

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Abstract

The distribution of wind speed and the optimal assessment of wind energy potential are very important factors when selecting a suitable site for a wind power plant. In wind farm design projects for the supply of electrical energy, designers use the Weibull distribution law to analyse the characteristics and variations of wind speed in order to evaluate the wind potential. In our study we used two approaches, namely, the Multilayer Perceptron (MLP) approach and the Support Vector Machine (SVR) approach to determine a distribution law of wind speeds and to optimally evaluate the wind potential. These two approaches were compared to two well-known numerical methods which are the Justus Empirical Method (EMJ) and the Maximum Likelihood Method (MLM). The results show that the neural network approach produces a better fit of the distribution curve with an Root Mean Square Error (RMSE) of 0.00005016 at Lomé, 0.000040289 at Cotonou site and a more interesting estimate of the wind potential. After that SVR show a better result too with an RMSE of 0.0095618 at the Lomé site and 0.0053549 at the Cotonou site.

Keywords

neural network; support vector regression; multilayer perceptron; wind energy; weibull distribution

Hrčak ID:

292073

URI

https://hrcak.srce.hr/292073

Publication date:

18.12.2022.

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