Wind Speed Forecasting Based on a Hybrid EMD-BLS Method

Wind power is a kind of environmentally friendly and economical renewable natural energy resources. Exact speed of wind forecasting act an important part in wind power planning. However, the time series (TS) of wind speed is non-linear and non-stationary, which make it difficult to be forecasted. A novelty forecasting method of one-step ahead wind speed which based on empirical mode decomposition (EMD) and broad learning system (BLS) methods is proposed. EMD is a powerful tool for nonlinear data decomposition in such a noisy environment and the purpose is to decompose a complex TS into different frequency components. The components after decomposing can reduce the non-stationary of TS. Subsequently, a newly developed network framework called BLS provided another way to learn deep structures and is trained to forecasting each component. The final prediction result is obtained from superposition of the prediction value of each component. Moreover, the effectiveness of the hybrid EMD-BLS method is verified through two real-world wind speed data sets.