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Application of Data Mining Technology in Anti Electricity Theft System

Published: 31 July 2024 Publication History

Abstract

The current phenomenon of electricity theft has also undergone new changes, with the emergence of many high-tech and well concealed methods of electricity theft, and even the trend of intelligent and professional electricity theft, which undoubtedly creates more difficulties for anti electricity theft work. This article attempts to conduct research on the application of DM (Data Mining) technology in anti theft systems. In the process of statistical and analytical analysis of data testing and the construction of anti electricity theft mathematical models, a set of user electricity consumption characteristics is constructed, and based on the record of characteristic attributes, the type of electricity consumption of users is predicted, achieving effective statistics and analysis of abnormal electricity consumption behavior of users. The research results indicate that the summer daily load curve changes of the distribution network in Region A are analyzed, and the overall load fluctuation is relatively obvious, with certain differences between peak and valley. As the distribution load continues to increase, the difference in peak and valley changes gradually increases. The fluctuation of power grid load in region A during winter is relatively small, and its peak valley gap and overall trend are relatively stable. It can be seen that DM technology is undoubtedly an effective method for improving the efficiency of power theft investigation.

References

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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