A hybrid wind speed forecasting strategy based on Hilbert-Huang transform and machine learning algorithms

Precise wind resource assessment is one of the more imminent challenges. In the present work, we develop an adaptive approach to wind speed forecasting. The approach is based on a combination of the efficient apparatus of non-stationary time series of wind speed retrospective data analysis based on the Hilbert-Huang transform and machine learning models. Models that are examined include neural networks, support vector machines, the regression trees approach: random forest and boosting trees. Evaluation results are presented for the Irish power system based on the Atlantic offshore buoy data.