Issue |
E3S Web Conf.
Volume 261, 2021
2021 7th International Conference on Energy Materials and Environment Engineering (ICEMEE 2021)
|
|
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Article Number | 01029 | |
Number of page(s) | 5 | |
Section | Energy Development and Energy Storage Technology Research and Development | |
DOI | https://doi.org/10.1051/e3sconf/202126101029 | |
Published online | 21 May 2021 |
Abnormal Power Consumption Detection Based on Data-Driven
1
Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai, China
2
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
* Corresponding author: zc2640@sjtu.edu.cn
Based on high dimensional random matrix theory and machine learning algorithm, a method to detect abnormal power consumption behaviour of users is proposed. Firstly, the K-means clustering algorithm is used to divide the power loads into load types that obey specific distribution law or with random fluctuation. Then the linear eigenvalue statistics (LES) index can be used to detect the abnormal power consumption behaviour for the former such as unimodal load or bimodal load. And the difference between the actual and predicted value of regression model based on XGBoost algorithm can be used as the basis for judging abnormal power consumption behaviour of the latter. The method proposed in this paper is applicable to different types of loads and can implement a good discriminant effect.
© The Authors, published by EDP Sciences, 2021
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