Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine

With the large-scale integration of wind power into the power grid, improving the wind speed prediction accuracy is of great significance for promoting the consumption of renewable energy. In this paper, a hybrid prediction method for multi-step wind speed prediction based on the empirical wavelet transform (EWT), multi-objective modified seagull optimization algorithm (MOMSOA), and multi-kernel extreme learning machine (MKELM) is proposed. First, EWT is used to decompose the nonstationary wind speed data into a set of stationary subsequences. Then, each subsequence of wind speed is predicted by the MKELM, and the MKELM network is optimized by the MOMSOA newly proposed in this study. Finally, the inverse empirical wavelet transform (IEWT) is adopted to reconstruct the prediction results into the final wind speed prediction results. To assess the performance of the proposed combined model, four groups of experiments are carried out on four wind speed sequences, and a comparative analysis is made with 16 comparison models. The models involved in the investigation are discussed comprehensively in terms of significance and stability. The results demonstrate that the developed combined model outperforms the comparison models.