SHORT-TERM WIND POWER PREDICTION BASED ON RUBUST SPARSITY BROAD LEARNING SYSTEM

被引：0
|
作者：
Kang Y. [1 ]
Liu S. [1 ]
Lei J. [2 ]
机构：
[1] China Electric Power Research Institute, Beijing
[2] School of Energy，Power and Mechanical Engineering, North China Electric Power University, Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 05期
关键词：
broad learning system; regularization; sparsity optimization; wind power prediction;
D O I：
10.19912/j.0254-0096.tynxb.2023-0089
中图分类号：
学科分类号：
摘要：
In order to enhance the prediction accuracy，the robust sparsity broad learning system （RSBLS） prediction method is proposed. The model training is transformed into a difference- of- convex functions optimization problem based on the regularization method. The robustness of the estimation is improved by using the L1 norm as the data fidelity term. The performance of the model is enhanced by integrating the L1 - 2 norm into the objective function to ensure the sparsity of the output weights. The numerical method that integrates the advantage of the half-quadratic splitting algorithm is proposed to solve the training model efficiently. Experimental analysis based on the criteria of absolute error，relative error，mean absolute error，root mean square error，mean absolute percentage error，algorithm stability，percentage improvement of the performance，gray correlation analysis and DM test shows that the prediction quality of the new algorithm is better than popular prediction algorithms and has better robustness，which provides a new method for wind power prediction. © 2024 Science Press. All rights reserved.
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页码：32 / 43
页数：11
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