Abstract
Automated and smart meters are tools that can track the power users' energy usage in real time. Because of the real-time consumption data, they can gather and the increasingly complex billing systems they can enable, they are seen as essential technological facilitators of the smart grid. Previous solutions use machine-learning models based on fine- grained readings from individuals, which exposes their privacy by revealing their lifestyle, to identify these attackers. This paper provides an efficient method for identifying power theft, calculating user bills, and monitoring payload while maintaining users’ privacy. To safeguard personnel, it is intended to communicate to GPS who encode their readings using functional encryption and the OS using encryption texts, so that they may compute bills using a new pricing method, analyze load balance, and analyze machine learning to recognize current theft. This project's primary goal is to alert the electricity board to instances of electric power theft. Through embedded technology, it persists. This system is able to measure both the power transmitted over the load and the power used by the load over time. IoT is used to monitor parameters. The system will automatically trip if the user doesn't pay the bill.
Published Version
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