The short-term prediction of wind power is one of the most important means to reduce the influence of wind power uncertainty on the stable operation of the power system. This paper proposes a short-term prediction of wind power based on the ensemble empirical mode decomposition (EEMD) algorithm, the biogeography-based optimization (BBO) algorithm and the extreme learning machine (ELM) algorithm (EEMD-BBO-ELM). First, the EEMD algorithm is used to decompose the original wind power sequence, and then the ELM algorithm optimized by BBO is used to predict the power sequence. Finally, the experimental data verifies that the prediction performance of this algorithm is excellent, the convergence speed is fast, which has high engineering value.
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