Short-term Wind Power Prediction Method Based on Random Forest

Authors

Liu Xing, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

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.

Recommended Citation

Xing, Liu; Yan, Wang; and Ji, Zhicheng (2021) "Short-term Wind Power Prediction Method Based on Random Forest," Journal of System Simulation: Vol. 33: Iss. 11, Article 8.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0705
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss11/8

First Page

2606

Revised Date

2021-07-15

DOI Link

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

Last Page

2614

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.21-FZ0705