Aug 2026· International Journal of Applied and Advanced Engineering Research· 0 citations
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.
The existing energy metering systems lack real-time monitoring capabilities as well as cloud-based access. Smart grid technology helps in increasing energy efficiency and reliability of electrical power systems. This research work intends to design and develop an IoT-based smart energy metering system for real-time monitoring and visualization. The proposed system encompasses ESP32 microcontroller integrated with ZMPT101B voltage sensor and SCT-013 current sensors. However, the collected data is then processed using the EmonLib library in order to evaluate voltage, current, power, power factor, and energy consumption. The processed information is later sent to the Blynk cloud using Wi-Fi technology. As a result of the performed experimental tests, voltage, current, power, and energy consumption measurement errors has not exceeded 0.80%, 4.55%, 4.00%, and 2.06%, respectively.
R. G, Pavalam J., R. S et al.· Journal of Electrical Engine...· 0 citations
This study aims to develop an Internet of Things (IoT)-based electrical energy (kWh) monitoring system using an ESP32 microcontroller integrated with a web monitoring platform. The system employs ACS712 current and ZMPT101B voltage sensors to measure voltage, current, power, and electrical energy in real time. The research method used is Research and Development (R&D) with the 4D model, consisting of define, design, development, and dissemination stages. The test results indicate that the ZMPT101B sensor has an error rate of 0.49%–0.81%, while the ACS712 sensor has an error rate of 4.0%–14.2%. Measurement data were successfully transmitted and displayed on the monitoring website through an internet connection. The developed system is capable of providing real-time electrical energy monitoring with adequate accuracy and easy accessibility for users.
Traditional energy management at universities is characterised by manual monitoring, static control systems, and lack of real-time data, resulting in excessive energy consumption and high operational costs. This study presents the design, implementation, and evaluation of a Smart Energy Management System (SEMS) at the University of Calabar, Cross River State, Nigeria, with the objective of reducing energy consumption and operational costs. The SEMS integrates Internet of Things (IoT) sensors, real-time data analytics, and automated control mechanisms to monitor and manage energy consumption dynamically. Key hardware components include motion sensors, smart meters, and environmental monitors, all connected to a centralised dashboard that provides actionable insights for energy optimisation. A six-month pilot deployment using a three-tier IoT architecture demonstrated energy savings of 30–40%, improved operational efficiency, with automated response times of 2–4 seconds and system uptime exceeding 95%. Comparative analysis confirmed that the SEMS outperforms traditional energy management in responsiveness (automated response time of 2.5–3.5 seconds versus delayed manual response), cost-effectiveness (30–40% reduction in energy expenditure with low long-term operational costs), and data visibility (real-time, room-level consumption data versus monthly, incomplete utility bills). The study provides a scalable framework for implementing smart energy solutions in university settings.
Ofem Ajah Ofem, Iniobong Ime, Osowomuabe Njama-Abang et al.· Global Journal of Pure and A...· 0 citations
The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.
Mohammed Sabah, Akram Elmitwally, A. Eladl· Engineer· 0 citations
This work reports the design and experimental assessment of a portable electrical measurement platform that uses Internet of Things (IoT) technology for continuous measurement and remote observation. The prototype combines 40 W and 100 W resistive lamps, an ESP32 controller, a PZEM-016 energy sensor, and a cloud dashboard to collect voltage, current, and power data. The main contribution is the integration of conventional electrical measurement with IoT data acquisition, remote supervision, and automatic recording in one portable unit. Performance was examined experimentally with series and parallel circuits and different load arrangements. Readings from the IoT module were checked against conventional instruments, and values were calculated from Ohm's law and the electrical power relation. The prototype reproduced the electrical behaviour of both circuit arrangements. The series connection produced unequal voltage sharing and much lower power delivery, whereas the parallel connection kept the supply voltage nearly constant and delivered substantially greater power. Across the tested quantities, the difference between IoT and reference readings was below 5%. The system also supports live visualisation, remote access, and automatic storage of measurement records, indicating its suitability for electrical engineering measurement and system analysis.
S. Haryudo, Widi Ariwibowo, Ervin Yohanes et al.· E3S Web of Conferences· 0 citations
In an era of rising energy costs and environmental concerns, traditional monthly electricity bills fail to provide households with the real-time insights needed to optimize their energy usage. To address this gap, this project details the development of a House Energy Monitoring System (HEMS) designed to continuously and accurately measure domestic energy consumption. The HEMS prototype utilizes an ESP32 microcontroller equipped with Wi-Fi capabilities as its central processing unit. It integrates a ZMPT101B voltage sensor and an SCT-013-050 non-invasive AC current sensor to safely read analog electrical signals directly from the home's distribution box. The microcontroller processes these signals to calculate essential electrical parameters, including Root Mean Square Voltage (VRMS), Root Mean Square Current (IRMS), real power (Watts), and cumulative energy consumption (kWh). For user accessibility, the processed data is displayed locally on an LCD screen and transmitted wirelessly to a user's mobile phone via the Blynk application. This dual-display setup provides data logs updated every five seconds, allowing users to track immediate power dynamics and identify high-energy appliances. While prototype testing revealed minor sensor noise and inaccuracies at very low currents (0-0.9 Amps), the system successfully empowers users to adjust their daily habits and strategically utilize off-peak electric tariff blocks to save money. Ultimately, this HEMS aligns with the United Nations Sustainable Development Goals, specifically promoting affordable and clean energy (SDG 7) and responsible consumption (SDG 12). By illuminating hidden power usage, the system empowers consumers to reduce their carbon footprints, lower electricity bills, and actively contribute to a more sustainable energy future.
W. Bukhari· International journal of res...· 0 citations
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