Wind Energy Forecasting with Artificial Intelligence Techniques: A Review

Abstract

The World Wind Energy Association (WWEA) forecasts that installed wind capacity worldwide will reach 800 GW by the end of 2021. Because wind is a random resource, both in speed and direction, the short-term forecasting of wind energy has become an important issue to be investigated. In this paper, a Systematic Literature Review (SLR) on non-parametric models and techniques for predicting short-term wind energy is presented based on four research questions related to both already applied methodologies and wind physical variables in order to determine the state of the art for the development of the research project “Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm”. The results indicate that artificial neural networks (ANN) and support-vector machines (SVMs) were mainly used in related studies. In addition, ANNs are highlighted in comparison with other techniques of Wind Energy Forecasting.