Short-Term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks

Abstract

This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological prediction global models, observations at surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model and a neural network to obtain the wind speed forecast in an specific point of the wind park. In the experiments carried out, we present some results of wind speed forecast in two aero-generators in a wind park at the south east of Spain. The results are encouraging, and show that our hybrid weather forecast models-neural network approach is able to obtain good short-term predictions of wind speed at specific points.