Horizontal Extrapolation of Wind Speed Distribution Using Neural Network for Wind Resource Assessment

Nihad AGHBALOU, Abderafi CHARKI, Saida RAHALI ELAZZOUZI, Kamal REKLAOUI

Abstract: To evaluate the wind potential on a site for future wind energy project, an accurate representation of the wind speed distribution is required. However, due to the lack of observations, wind engineers are conducted to use some statistical tools to estimate the characteristics of wind by the measurements from a nearby reference or data obtained from a short period. In this work, we aim at applying an information processing paradigm that is inspired by biological neurons, formal neurons, for the assessment of wind speed distribution. Two different learning algorithms are used so as to generate Artificial Neural Network with one hidden layer. Results prove that learning by means of Bayesian regularization, in comparison with LevenbergMarquardt learning algorithm, gives the best performance. In addition, the proposed network allows significant results in horizontal wind extrapolation.

Keywords: Wind Speed Distribution, Weibull distribution, Energy Performance, Neural Network, Bayesian Regularization, LevenbergMarquardt

How to Cite?: Nihad AGHBALOU, Abderafi CHARKI, Saida RAHALI ELAZZOUZI, Kamal REKLAOUI, "Horizontal Extrapolation of Wind Speed Distribution Using Neural Network for Wind Resource Assessment", Volume 6 Issue 12, December 2017, International Journal of Science and Research (IJSR), Pages: 1498-1504, https://www.ijsr.net/getabstract.php?paperid=ART20178810, DOI: https://dx.doi.org/10.21275/ART20178810