IoT-Based Smart Meter with AI Analytics and Safety Features

Abstract

The proliferation of Internet of Things (IoT) technologies has created unprecedented opportunities to transform traditional energy infrastructure into intelligent, responsive systems. Traditional electromechanical and basic digital meters function as passive \"black boxes,\" offering no real-time data, safety mechanisms, or actionable insights. This paper presents the design, implementation, and evaluation of an IoT-based smart metering system that integrates artificial intelligence (AI) analytics with safety features for real-time energy monitoring and management. This all-in-one system is designed to continuously monitor all crucial electrical parameters—including true RMS Voltage, RMS Current, Active Power, Power Factor, and cumulative Energy Consumption (kWh)—in real-time. This high-resolution data provides an unprecedented, transparent view into the home\'s electrical health and efficiency. Utilizing an ESP32 microcontroller as its powerful yet low-cost core, along with an ACS712 current sensor and a ZMPT101B voltage sensor, the meter captures high-fidelity data directly from the mains. This data is processed on-device for immediate fault detection before being securely transmitted via Wi Fi to a persistent cloud platform (such as Firebase or Blynk). This architecture enables users to access a comprehensive mobile or web application, transforming their smartphone into a powerful energy command center. A remote dashboard is designed to perform analysis of detailed historical usage graphs and get accurate, itemized billing estimates. The system\'s core innovation, however, lies in its integrated, active safety features, including overload, shock, and leakage detection. These mechanisms automatically trigger an onboard, high-power relay to instantaneously disconnect the load and prevent accidents, providing a level of active protection and peace of mind completely absent in conventional meters. Furthermore, the project establishes a rich data-collection pipeline, laying the essential groundwork for future AI-driven analytics. This will enable advanced features like anomaly detection for failing appliances, predictive usage patterns, and smart recommendations, truly empowering users to make smarter, safer, and more sustainable energy decisions

Introduction

This work presents a smart IoT-based energy metering system that transforms traditional electricity meters into intelligent, real-time monitoring and control devices.

Conventional meters are passive and lack safety, automation, and insight. With the rise of IoT, edge computing, and AI, the proposed system enables real-time energy monitoring, appliance-level usage detection, fault identification, and even automatic safety actions like overload protection. It combines edge AI for instant decisions with cloud analytics for long-term forecasting and insights.

The literature review shows that existing smart metering systems and AI models (like NILM and LSTM-based approaches) either focus on analytics or communication but lack real-time safety intervention, edge intelligence, affordability, or full system integration. This work addresses that gap by integrating AI, IoT, safety control, and cloud connectivity into a single low-cost system.

The system architecture is modular and safety-focused, separating high-voltage and low-voltage components. Key components include:

• Sensors (ZMPT101B, ACS712) for safe voltage and current measurement
• ESP32 microcontroller for real-time processing and AI-based decision-making using dual-core RTOS tasks
• Relay module for automatic protection actions like disconnecting load during faults
• LCD display for local monitoring
• Cloud platform (Firebase/Blynk) for data storage, analytics, and remote control
• Mobile/web app for user interaction and visualization

The ESP32 continuously calculates power metrics, detects faults in real time, and sends data to the cloud while maintaining strict separation between safety-critical and background tasks. The system also supports remote monitoring, alerts, and control through a mobile interface.

Conclusion

This paper has presented a comprehensive IoT-based smart metering system that integrates AI analytics and advanced safety features to transform energy measurement from a passive accounting function into an active intelligence platform. The study demonstrates that the convergence of the three foundational technologies—IoT for real-time data acquisition, Cloud Computing for seamless remote accessibility, and Artificial Intelligence for deriving actionable insights—extends beyond theoretical exploration and represents a practical, cost-effective solution. Through this integration, the conventional electricity meter evolves from a passive recording device into an intelligent, dynamic energy management system. The proposed framework equips users with meaningful insights and control mechanisms, enabling safer, more efficient, and economically optimized energy consumption. In doing so, it not only fulfils its academic purpose by offering a scalable and research-oriented platform but also establishes a strong foundation for a commercially viable product with significant real-world applicability. Furthermore, the system facilitates a critical transition from passive energy consumption to proactive energy management, empowering users to become informed and responsible energy prosumers. This advancement lays the groundwork for a future in which residential and commercial environments are inherently intelligent, adaptive, and energy-aware.

References

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Copyright

Copyright © 2026 Priyank Gokhale, Ayush Borse, Atharva Chandratre, Aryan Kolathapully. 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.