DESIGN AND IMPLEMENTATION OF AN IoT-BASED SMART ENERGY METER WITH REAL-TIME MONITORING AND MATLAB-BASED DATA ANALYTICS
TL;DR
The findings indicate that integrating IoT technology with cloud communication and MATLAB analytics provides a practical, low-cost, and scalable solution for intelligent energy monitoring.
Abstract
Conventional electricity metering systems are limited by their inability to provide real-time monitoring, remote access, and accurate information on energy consumption, which in turn reduces the efficiency of energy management and billing processes. This study aimed to design and implement an Internet of Things (IoT)-based smart energy meter capable of real-time monitoring and MATLAB-based data analytics. A prototype development methodology was adopted, using an ESP32 microcontroller integrated with ACS712 current and ZMPT101B voltage sensors, an LCD module, Wi-Fi communication, and the Blynk cloud platform. Electrical parameters were measured under varying load conditions and transmitted wirelessly for remote monitoring, while MATLAB was used to analyse and visualise the collected data. The developed system successfully measured voltage, current, power, and energy consumption, with recorded values of approximately 220 V, 0.40–1.20 A, 88–264 W, and 0.088–0.264 kWh, respectively. The graphical outputs clearly illustrated voltage stability, load variation, instantaneous power consumption, and cumulative energy usage, thereby improving users’ ability to monitor electrical energy in real time. Although slight measurement deviations caused by sensor calibration, signal noise, and network dependence were observed, the system demonstrated reliable performance throughout the testing period. The findings indicate that integrating IoT technology with cloud communication and MATLAB analytics provides a practical, low-cost, and scalable solution for intelligent energy monitoring. The study concludes that the proposed system offers significant improvements over conventional metering systems by enabling continuous monitoring, remote access, and informed energy management. Future work should focus on improving sensor calibration, incorporating artificial intelligence for predictive energy analysis, supporting three-phase installations, and providing offline data storage to further enhance system reliability and scalability.