Paper
8 May 2022 Improved Bayesian ridge regression based data missing reconstruction of smart meters
Weisong Chen, Yong Xiao, Jianbin Deng, Fusheng Li, Bin Guo, Fan Zhang, Lijuan Xu
Author Affiliations +
Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022); 1224915 (2022) https://doi.org/10.1117/12.2636621
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 2022, Xiamen, China
Abstract
In order to promote the digitalization of the power grid, many smart meters are installed on the distribution network side to monitor the operating status of the power grid and equipment information in an all-round way. Frequent data missing phenomenon will lead to misjudgment in the thematic analysis of abnormal electricity consumption in distribution network. In this paper, a missing data reconstruction method based on Improved Bayesian Ridge Regression (IBRR) is proposed. Regularization methods are added to the posterior distribution estimation of parameters to automatically filter redundant information in massive data, thereby avoiding Overfitting phenomenon of maximum likelihood estimation, and improve model training speed and generalization ability. A mutation processing mechanism is proposed for the abnormal fluctuations in the reconstruction results. The results show that, compared with the traditional method, the proposed method has better reconstruction accuracy, and the reconstruction speed is greatly improved.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weisong Chen, Yong Xiao, Jianbin Deng, Fusheng Li, Bin Guo, Fan Zhang, and Lijuan Xu "Improved Bayesian ridge regression based data missing reconstruction of smart meters", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 1224915 (8 May 2022); https://doi.org/10.1117/12.2636621
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KEYWORDS
Data modeling

Reconstruction algorithms

Statistical analysis

Statistical modeling

Computer simulations

Data analysis

Power supplies

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