Wind Speed Forecasting Model Based on Extreme Learning Machines and Complete Ensemble Empirical Mode Decomposition

As there is instability in wind speed, therefore, wind speed forecasting makes sense to ensure security and economy of operating the wind power system. There is strong randomness in wind speed that as part of the non-stationary time series. In this paper, a hybrid forecasting model based on extreme learning machines (ELM) and complete ensemble empirical mode decomposition (CEEMD) is proposed to realize the wind speed forecasting. First, non-stationary time series is decomposed into a series of stable components by using CEEMD. Then, the forecast of each component based on the ELM. Finally, compose the forecast of the components to get the final forecast. Obviously,the proposed method is more steadily and effectively.