Evaluation of XGBoost vs. other Machine Learning models for wind parameters identification

Abstract

Wind energy is one of the most promising renewable energies. But wind is a quite unstable resource due to its continuous variation and random nature. This uncertainty affects the production cost. Therefore, accurate forecasting of wind and energy is very interesting for energy markets. In this work, we test a recent and powerful intelligent technique, extreme gradient boosting (XGBoost), for wind prediction. The forecasting models of some wind features with XGBoost are compared with Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Neural Networks (NN) models. Specifically, the three features predicted are the active power generated by the turbine, the wind speed, and the wind direction. The results conclude that these techniques are useful for wind and energy forecasting, with XGBoost being the most outstanding one, especially for short-term predictions.