MeterGuard: Multi-Class Electricity Theft Detection and Proactive Countermeasures for Smart-Home Energy Systems
DOI:
https://doi.org/10.15662/IJEETR.2026.0802054Keywords:
Rabies Diagnoses, CNNs, ResNet50, LSTMs, Transfer Learning, Zoonotic Diseases, Deep Learning, Multimodal Classification Systems, Veterinarian AI Applications, Public Health ScreeningAbstract
: Power theft is a serious issue in the modern smart grid setting and is a source of significant losses to power distribution incidence. Even though there are some traditional methods of detecting theft, they do not always help to detect sophisticated or concealed theft patterns.
In order to address these shortcomings, this paper proposes a new system called MeterGuard whose framework is based on machine learning to identify electricity theft in residential settings. The model takes the consumption data collected by smart meters and determines abnormal use behavior through the use of the XGBoost classification method.
The suggested system will be used to identify different categories of fraud cases such as physical manipulation of meters, computer attacks, and deliberate manipulation of consumption data. Besides detecting, the system provides safe communication of data between smart meters and central monitoring unit through the use of Advanced Encryption Standard (AES) encryption.
As the experimental analysis shows, the suggested strategy improves the capabilities of detection, and it considerably increases the security and reliability of smart grid systems
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