Identification of customer electricity usage anomalies based on random matrix theory

Shuo Zhou; Qihui Wang

doi:10.1117/12.3014405

9 January 2024 Identification of customer electricity usage anomalies based on random matrix theory

Shuo Zhou, Qihui Wang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692M (2024) https://doi.org/10.1117/12.3014405
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

A detection algorithm of maximum and minimum eigenvalues based on random matrix theory is proposed for the problem of abnormal detection of customer electricity consumption. Firstly, the data source matrix is constructed by time alignment and superimposed Gaussian white noise, and the sliding window method is used to obtain the window data indicating the operation status at each moment; secondly, the window data are standardized, feature extraction and other operations are performed, and the difference and the sum of the maximum and minimum eigenvalues are compared to construct the feature detection indexes and thresholds; finally, the algorithm is studied and verified by simulation. The results show that the algorithm does not depend on any model, can analyze the operation status of the system more comprehensively and adequately, and realizes the effective detection of abnormal data

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuo Zhou and Qihui Wang "Identification of customer electricity usage anomalies based on random matrix theory", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692M (9 January 2024); https://doi.org/10.1117/12.3014405

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