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In the development of the wind power industry, short-term wind speed forecasting is necessary, and many researchers have made substantial efforts to establish wind speed prediction models. However, realizing the accurate prediction of wind speeds remains a challenging task. The current prediction models do not consider the preprocessing of the data, and each model has various shortcomings. Considering the disadvantages of the available models, in this paper, an advanced combined forecasting system is applied that utilizes a data preprocessing strategy and parameter optimization strategy to obtain accurate prediction values. The proposed prediction system employs linear and nonlinear models that can take into account the characteristics of wind speed sequences, successfully combine the advantages of various single models, and yield accurate and stable prediction values. Finally, according to the experimental analysis and discussion, the proposed combined prediction system outperforms the compared models in prediction. In conclusion, the powerful combined prediction model provides a feasible scheme for wind power prediction.

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Wind Speed Forecasting System Based on the Variational Mode Decomposition Strategy and Immune Selection Multi-Objective Dragonfly Optimization Algorithm