基于生成对抗网络多变量风电时间序列异常值处理

徐昊; 王永生; 许志伟; 武煜昊; 陈振

doi:10.19912/j.0254-0096.tynxb.2021-0685

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (12) : 300-311. DOI: 10.19912/j.0254-0096.tynxb.2021-0685

基于生成对抗网络多变量风电时间序列异常值处理

徐昊^1,2, 王永生^1~3, 许志伟^1,2,4, 武煜昊^1,2, 陈振^1,2

作者信息 +

OUTLIER PROCESSING OF MULTIVARIABLE WIND POWER TIME SERIES BASED ON GENERATIVE ADVERSARIAL NETWORK

Xu Hao^1,2, Wang Yongsheng^1-3, Xu Zhiwei^1,2,4, Wu Yuhao^1,2, Chen Zhen^1,2

Author information +

文章历史 +

摘要

将孤立森林算法应用于风电数据的异常值检测,利用改进的GRUI神经单元基于WGAN网络进行缺失值插补。在内蒙古风电场的真实数据集上验证了所提方法的有效性,并与KNN、GAN等方法进行对比,验证了模型的有效性,具有更好的插补精度。

Abstract

The rapid development and original data lack of wind power generation have brought difficulties and challenges to wind power prediction. Accurate and complete original data is the basis of wind power research. The isolated forest algorithm is applied to the outlier detection of wind power data. The improved GRUI neural unit is used to perform missing value interpolation based on the WGAN network. The effectiveness of the proposed method is verified on the real data set from the Inner Mongolia wind farm and compared with KNN, GAN and other methods. As the results, the model has better interpolation accuracy.

导出引用

徐昊, 王永生, 许志伟, 武煜昊, 陈振. 基于生成对抗网络多变量风电时间序列异常值处理[J]. 太阳能学报. 2022, 43(12): 300-311 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0685

Xu Hao, Wang Yongsheng, Xu Zhiwei, Wu Yuhao, Chen Zhen. OUTLIER PROCESSING OF MULTIVARIABLE WIND POWER TIME SERIES BASED ON GENERATIVE ADVERSARIAL NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 300-311 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0685

中图分类号： TP391.1

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