Wind speed modelling for the estimation of the wind energy generation

Abstract

This paper proposes the design and implementation of wind speed modelling for the estimation of the wind energy generation, based on different series of data collected from meteorological stations at the Gran Canaria Airport and Tenerife Sur Airport (Canary Islands, Spain), that helps to generate green energy from wind by the estimation of wind speed. The classification method applied, in order to obtain a prediction, was Artificial Neural Network (ANN) multilayer perceptron, using a window size of meteorological data of 5 samples. The mean average error reached at the study of wind speed prediction was 0.85 meters per second.