A Study on Feature Selection Methods for Wind Energy Prediction

This work deals with wind energy prediction using meteorological variables estimated by a Numerical Weather Prediction model in a grid around the wind farm of interest. Two machine learning techniques have been tested, Support Vector Machine and Gradient Boosting Regression, in order to study their performance and compare the results. The use of meteorological variables estimated in a grid generally implies a large number of inputs to the models and the performance of models might decrease. Hence, in this context, the use of feature selection algorithms might be interesting in order to improve the generalization capability of models and/or reduce the number of attributes. We have compared three feature selection techniques based on different paradigms: Principal Components Analysis, ReliefF, and Sequential Forward Selection. Energy production data has been obtained from the Sotavento experimental wind farm. Meteorological variables have been obtained from European Centre for Medium-Range Weather Forecasts, for a 5\(\,\times \,\)5 grid around Sotavento.