AI+IoT Based Energy Monitoring & Predictive Power Analytics System: A Review

Abstract

The increasing demand for electricity has created a need for intelligent and efficient energy monitoring and management systems. Traditional energy monitoring systems mainly provide basic consumption measurements and have limited capabilities for prediction and abnormal usage detection. This review paper presents an overview of AIoT-based intelligent energy monitoring and predictive power analytics systems, focusing on the integration of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), cloud platforms, and real-time dashboards. The review examines existing techniques for real-time measurement of voltage, current, power, and energy consumption, along with AI/ML methods used for energy consumption forecasting and anomaly detection. It also compares existing approaches based on monitoring capability, prediction accuracy, anomaly detection, cloud integration, and intelligent analytics. The study identifies major challenges and research gaps in developing a unified system that can provide real-time monitoring, accurate power-demand prediction, abnormal usage detection, alerts, and energy cost estimation. Finally, the paper discusses future opportunities for smart homes, industries, offices, and commercial buildings, with the aim of improving energy efficiency, reducing energy wastage and supporting sustainable energy management.

Introduction

The proposed AI-IoT Based Intelligent Energy Monitoring and Predictive Power Analytics System is designed to improve energy management by combining IoT-based real-time monitoring with AI/ML-based intelligent analysis. Traditional energy meters only display electricity consumption and generally cannot predict future demand or detect abnormal usage, which can lead to energy wastage and higher electricity costs.

The system uses PZEM-004T, ZMPT101B, and ACS712 sensors to measure voltage, current, power, and energy consumption. An ESP32 microcontroller collects this data and sends it through Wi-Fi to cloud platforms such as ThingSpeak or Firebase. A web dashboard then presents real-time and historical energy data.

AI/ML techniques are used to analyze consumption patterns, predict future power demand, detect abnormal or excessive energy usage, generate alerts, and estimate electricity costs. The proposed system aims to provide a single platform for intelligent energy monitoring and management.

References

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Copyright

Copyright © 2026 Neha N. Shewale, Samruddhi S. Yadav, Shravani A. Nalawade, Nilesh C. Yadav. 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.