Improved regularized extreme learning machine short-term wind speed prediction based on gray correlation analysis

Abstract

Accurate wind speed prediction is of great significance to the stable operation of the power grid when large-scale wind power is connected to the grid. This article proposes a new multi-step wind speed combination prediction model based on gray correlation analysis. First, the gray correlation analysis is performed on wind speed–related attributes, the more relevant attribute factors are selected as the input set of the prediction model, and the regularized extreme learning machine improved by the cuckoo optimization algorithm is used to perform multi-step prediction of wind speed. Then, an error self-tuning model is established to further improve the prediction accuracy. Finally, the measured results of different wind farms and seasons are selected to simulate the prediction effect of the proposed model, and the prediction accuracy and generalization ability of the proposed model are verified through comparative analysis.

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