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

Xu Hao; Wang Yongsheng; Xu Zhiwei; Wu Yuhao; Chen Zhen

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

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 300-311. DOI: 10.19912/j.0254-0096.tynxb.2021-0685

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

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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.

Key words

wind power / time series / generative adversarial network / outlier detection / missing interpolation

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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

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