Short-term Wind Power Prediction Method Based on Random Forest

doi:10.16182/j.issn1004731x.joss.21-FZ0705

Abstract

Abstract: In order to effectively predict the power and value fluctuation range of the short-term wind, a wind power prediction method based on clustering and kernel principal component analysis combined with random forest algorithm is proposed. The clustering analysis data processing method is used to preprocess the meteorological wind power generation data to improve the data quality, and the kernel principal component analysis method is used to reduce the dimensionality of the eight groups of characteristic data to remove the correlation of the wind power data, the random forest algorithm is used to forecast the wind power, to obtain the predicted wind power value. The results show that, compared with the traditional prediction model, based on the clustering and kernel principal component analysis, combined with the random forest algorithm, the prediction model can reduce the prediction error and track the change of wind power more accurately.

Key words: clustering analysis, combination forecast, kernel principal component analysis, random forest algorithm, wind power prediction

CLC Number:

TP391.9

Liu Xing, Wang Yan, Ji Zhicheng. Short-term Wind Power Prediction Method Based on Random Forest[J]. Journal of System Simulation, 2021, 33(11): 2606-2614.

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