Short-Term Wind Power Forecasting Method Based on Mode Decomposition and Feature Extraction

Accurate and stable wind power forecasting is an inevitable requirement for efficient use of renewable energy. This paper proposes a short-term wind power combination forecasting method based on mode decomposition and feature extraction. The original wind speed time series is decomposed into multiple subsequences by the ensemble empirical mode decomposition (EEMD). The generalized mutual information (GMI) is used to extract the optimal input feature set of each subsequence. The prediction values of each subsequence are obtained based on the least squares support vector machine (LSSVM) model, and then the final prediction results are obtained by combining them. Finally, the paper proves that the proposed method can predict short-term wind power more accurately and stably by setting up the contrast experiment.