Wind energy potential estimation using neural network and SVR approaches


  • Adekunlé Akim Salami 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


Neural Network, Support vector Regression, Multilayer perceptron Wind energy, Weibull distribution.


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 MLP approach and the 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 and a more interesting estimate of the wind potential