Secure IoT Smart Energy Monitoring with AI Billing and NILM

Abstract

This project presents a Secure Smart Energy Monitoring and Protection System using IoT with encrypted communication. The system is designed to measure electrical parameters such as voltage, current, and power consumption in real time using an energy sensing module interfaced with a microcontroller. A protection mechanism using a relay is implemented to handle overload and short-circuit conditions effectively. To ensure secure data transmission, the Advanced Encryption Standard (AES) algorithm is used to encrypt energy data before sending it to the cloud platform. The encrypted data is transmitted through an IoT module and decrypted for remote monitoring and analysis. The system also incorporates Non-Intrusive Load Monitoring (NILM) for appliance-level estimation and an AI-based model for predicting electricity consumption. The overall system demonstrates accurate monitoring, enhanced safety, and secure communication, making it suitable for smart energy applications.

Introduction

The text describes a proposed Secure Smart Energy Monitoring and Protection System that uses IoT, AI, and encryption to improve traditional energy metering.

It explains that conventional energy meters only provide basic consumption readings and lack intelligence, prediction, appliance-level monitoring, protection, and data security. To address these issues, the proposed system introduces a smart IoT-based solution using an ESP32 microcontroller to continuously measure electrical parameters such as voltage, current, power, and energy.

A key feature of the system is real-time monitoring and automatic protection, where overload or short-circuit conditions trigger a relay to disconnect the load, preventing damage and improving safety. The system also uses AES encryption to secure data during transmission to the cloud, making it resistant to unauthorized access or tampering.

For smarter energy management, the system includes Non-Intrusive Load Monitoring (NILM) to estimate appliance-level consumption without extra sensors and an AI-based prediction model to forecast electricity usage and billing based on historical data. This helps users better understand and optimize their energy consumption.

Conclusion

One of the major achievements of the system is the integration of an automatic protection mechanism using a relay module. The system effectively detects abnormal conditions such as overload and short circuits and immediately disconnects the load to prevent damage to electrical appliances and ensure user safety. This combination of monitoring and protection in a single system enhances reliability and makes it more practical for real-world applications. Another important aspect of the project is secure data communication. By implementing the Advanced Encryption Standard (AES) algorithm, the system ensures that energy data is encrypted before transmission to the cloud. This protects the data from unauthorized access and cyber threats, making the system suitable for IoT-based smart grid environments where data security is a critical concern. In addition to basic monitoring, the system incorporates intelligent features such as Non-Intrusive Load Monitoring (NILM) and AI-based prediction. NILM enables appliance-level energy estimation without the need for additional sensors, reducing system complexity and cost. The AI-based model analyses historical data to predict future energy consumption and electricity billing, helping users plan their energy usage more efficiently. The integration of IoT technology allows users to monitor energy data remotely through cloud platforms, providing convenience and flexibility. Features such as data logging, time-based billing using RTC, and real-time display further enhance the functionality of the system.

References

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Copyright

Copyright © 2026 Vallarasu N, Sunil Vishnu B, Dinesh E, Govindarajan R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.