基于鲁棒稀疏宽度学习系统的短期风电功率预测

康逸群; 刘厦; 雷兢

doi:10.19912/j.0254-0096.tynxb.2023-0089

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (5) : 32-43. DOI: 10.19912/j.0254-0096.tynxb.2023-0089

基于鲁棒稀疏宽度学习系统的短期风电功率预测

康逸群¹, 刘厦¹, 雷兢²

作者信息 +

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

Kang Yiqun¹, Liu Sha¹, Lei Jing²

Author information +

文章历史 +

摘要

为改善预测质量,提出一种基于鲁棒稀疏宽度学习系统（RSBLS）的预测方法。基于正则法将模型训练转化为一个difference-of-convex functions优化问题,利用L₁范数作为数据忠诚项以提高估计的鲁棒性,将L_1-2范数集成到目标函数中确保输出权的稀疏性以提升模型性能,并提出一种融合half-quadratic splitting算法优势的数值方法有效求解该训练模型。基于绝对误差、相对误差、平均绝对误差、均方根误差、平均绝对百分误差、算法稳定性、预测误差改进百分比、灰色关联分析和DM检验等准则进行实验分析,结果表明新算法的预测质量优于流行的预测算法,并具有较好的鲁棒性,为风功率预测提出一种可行的新方法。

Abstract

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 L_1-2 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.

导出引用

康逸群, 刘厦, 雷兢. 基于鲁棒稀疏宽度学习系统的短期风电功率预测[J]. 太阳能学报. 2024, 45(5): 32-43 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0089

Kang Yiqun, Liu Sha, Lei Jing. SHORT-TERM WIND POWER PREDICTION BASED ON RUBUST SPARSITY BROAD LEARNING SYSTEM[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 32-43 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0089

中图分类号： TM614

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