A robust decomposition-ensemble framework for wind speed forecasting

Accurate forecasting of wind speed is vital in renewable power system management. However, wind speed series is an extremely complex system with outliers. Considering the dilemma, we propose a robust extreme learning machine algorithm where a huber loss works as the optimized function for extreme learning machine training. And a decomposition-ensemble method is developed in modelling wind speed. In our hybrid system, the proposed robust extreme learning machine is employed to model high-frequent sub-signals, while least square extreme learning machine is used to model low-frequent sub-signals. Validated by forecasting a 5-minutely wind speed in China, our proposed forecasting framework can provide more accurate predictions.