Authors :Harshal Chachane , Prof. Mohd. Safique, Prof. Rajendra Bhombe, Dr. Kishor porate
DOI : 10.46335/IJIES.2025.10.2.1
Abstract- The proposed IoT-based smart energy monitoring system integrates advanced signal processing techniques with the ESP32 microcontroller and PZEM-004T sensor fusion to achieve a high measurement accuracy of 98.7%. By implementing adaptive sampling, the system effectively reduces power consumption by 37% while maintaining precise readings. A dual-cloud architecture enhances its functionality, utilizing ThingSpeak for MATLAB-based analytics and Firebase for real-time monitoring. Additionally, a machine learning-based load forecasting model provides an accurate prediction of energy usage patterns with 92% accuracy. Extensive testing over six months has demonstrated an impressive system reliability of 99.86%, while the cost is reduced by 60% compared to commercially available alternatives. Designed as an open-source and modular solution, the system is suitable for residential, commercial, and industrial applications, ensuring compliance with IEC 62053-21 energy metering standards.
