Year: 2025
Type: article
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.
Authors: Harshal Chachane, Omesh Yeole, Shubham Umredkar, Saurabh Raut, Shreya Nitnaware, Pratiksha khedkar, Mohd. Safique, Rajendra Bhombe, Kishor Porate
Language: English
Topic: Arduino and IoT Applications
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412885841