Fast cleaning method for smart grid data based on sparse self-coding

Peiyao Xu; Jianyong Wang; Fengtao Huang; Chao Lin; Chennan Zhou

doi:10.1117/12.2671323

28 April 2023 Fast cleaning method for smart grid data based on sparse self-coding

Peiyao Xu, Jianyong Wang, Fengtao Huang, Chao Lin, Chennan Zhou

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126100O (2023) https://doi.org/10.1117/12.2671323
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

At present, the data cleaning method based on time series realizes the data cleaning by classifying the data in the time series. Due to the lack of dimensionality reduction, the cleaning efficiency is low. For this reason, this paper proposes a method for rapid cleaning of smart grid data based on sparse self-coding. In this paper, the encoder neural network is constructed to reduce the dimension of the data, and Logsf algorithm is used to obtain the optimal weight of the data, obtain the main characteristics of the data, and achieve clustering cleaning of the data. In the experiment, the cleaning efficiency of the proposed method was verified. The experimental results show that the method proposed in this paper has a short time delay and high cleaning efficiency for smart grid data cleaning.

Citation Download Citation

Peiyao Xu, Jianyong Wang, Fengtao Huang, Chao Lin, and Chennan Zhou "Fast cleaning method for smart grid data based on sparse self-coding", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126100O (28 April 2023); https://doi.org/10.1117/12.2671323

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