Electric Larceny Detection Based on Support Vector Machine

DOI

10.2991/pntim-19.2019.68How to use a DOI?

Keywords

Component; Lean Management, Management Line Loss, Support Vector Machine, SMOTE+Bagging, Unbalanced Sample

Abstract

The design and application of power system line loss calculation and lean management system have important guiding significance in guiding loss reduction and energy saving and promoting line loss management. In recent years, the electric energy data acquire system, as a tool that can effectively meet the power enterprise's demand for power consumption information, has also accumulated a large amount of user power consumption data while meeting the power supply marketing automation needs. These power consumption data contain huge user power usage information. Therefore, the user data collected by the power electric energy data acquire system can be analyzed and processed to identify users with high suspicion of power severance, so as to reduce the management line loss. To this end, this paper studies a small-volume user anomaly power detection scheme based on Support Vector Machine (SVM), which can effectively identify the abnormal power consumption mode by tracking and screening the load data of the user for a period of time. An unbalanced sample synthesis processing model based on SMOTE+Bagging is constructed. The differential evolution algorithm is used to optimize the SVM parameters, which solves the problem that SVM classification performance is more affected by parameters. At the same time, the operational efficiency of the SVM-based Bagging integrated classification model is guaranteed.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Songnong
AU  - Zeng Yan
AU  - Ye Jun
AU  - Sun Hongliang
PY  - 2019/11
DA  - 2019/11
TI  - Electric Larceny Detection Based on Support Vector Machine
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 331
EP  - 334
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.68
DO  - 10.2991/pntim-19.2019.68
ID  - Songnong2019/11
ER  -