Electricity Consumption Data Cleansing and Imputation Based on Robust Nonnegative Matrix Factorization

被引：0
|
作者：
Liu Q. [1 ,2 ]
Zhong Y. [2 ]
Lin C. [1 ,2 ]
Li T. [2 ]
Yang C. [1 ]
Fu Z. [1 ]
Li X. [1 ]
机构：
[1] School of Electrical Engineering, Chongqing University, Shapingba District, Chongqing
[2] Measurement Center of Yunnan Power Grid Co., Ltd., Yunnan Province, Kunming
来源：
Dianwang Jishu/Power System Technology | 2024年 / 48卷 / 05期
关键词：
cleansing; imputation; low rank; nonnegative matrix; outlier; sparse;
D O I：
10.13335/j.1000-3673.pst.2023.0557
中图分类号：
学科分类号：
摘要：
Given the quality problems of noise, outlier, and loss in the collection and transmission of electricity consumption data under operating conditions, low eigenvalue and sparse outlier of the spatiotemporal distribution of electricity consumption data of a single user are used, and then a unified processing framework for data missing filling, noise reduction, and outlier elimination based on the low-rank matrix completion is proposed. Firstly, because of the huge differences in actual multi-user electricity consumption scenarios and characteristics, a data matrix with low-rank characteristics is constructed only according to the inherent similarity of a single user's electricity consumption behavior. Furthermore, considering the effects of additive background noise such as column and sparse anomalies, a non-negative matrix complete optimization model with low rank lifting regular constraints is constructed. Finally, the iterative least square method is used to solve the optimization problem to fill in the missing data and eliminate the multiple background noise. The effectiveness and accuracy of the proposed algorithm are verified by simulation and experimental results. © 2024 Power System Technology Press. All rights reserved.
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页码：2103 / 2112
页数：9
相关论文
共 36 条
  • [1] ALAHAKOON D, Xinghuo YU, Smart electricity meter data intelligence for future energy systems：a survey[J], IEEE Transactions on Industrial Informatics, 12, 1, pp. 425-436, (2016)
  • [2] WANG Yi, Qixin CHEN, HONG Tao, Review of smart meter data analytics：applications，methodologies，and challenges[J], IEEE Transactions on Smart Grid, 10, 3, pp. 3125-3148, (2019)
  • [3] GENES C，, ESNAOLA I, PERLAZA S M, Robust recovery of missing data in electricity distribution systems[J], IEEE Transactions on Smart Grid, 10, 4, pp. 4057-4067, (2019)
  • [4] ZHOU Li, ZHAO Lujun, GAO Weiguo, Application of sparse coding in detection for abnormal electricity consumption behaviors[J], Power System Technology, 39, 11, pp. 3182-3188, (2015)
  • [5] WU Yufeng, LI Fusheng, YU Tao, Power data compression and high-precision reconstruction based on residual dual attention mechanism network[J], Power System Technology, 46, 8, pp. 3257-3268, (2022)
  • [6] YANG Gengjie, WEI Xiancan, GAO Wei, Error estimation method for smart meters considering sudden misalignment[J], Power System Technology, 46, 9, pp. 3662-3671, (2022)
  • [7] CHAWLA S，, GIONIS A．k-means–：a unified approach to clustering and outlier detection[C], Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 189-197, (2013)
  • [8] FU Letian, Peng LI, GAO Lian, Improved LSSVM algorithm considering sample outliers[J], Chinese Journal of Scientific Instrument, 42, 6, pp. 179-190, (2021)
  • [9] YANG Jian, WANG Li, SONG Dongran, Diagnostic method of zero-point shifting of wind turbine yaw angle based on isolated forest and sparse Gaussian process regression[J], Proceedings of the CSEE, 41, 18, pp. 6198-6211, (2021)
  • [10] Shengyuan LIU, Yuxuan ZHAO, Zhenzhi LIN, Data-driven event detection of power systems based on unequal-interval reduction of PMU data and local outlier factor[J], IEEE Transactions on Smart Grid, 11, 2, pp. 1630-1643, (2020)
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