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.
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
IoT is one of the significant enabling technologies in the contemporary energy landscape. This work addresses intelligent load management under fault, under-load, and overload conditions. Electrical equipment requires automatic and rapid response to avoid damage and prevent service interruption. In contrast to traditional circuit breakers that disconnect the entire system, the proposed architecture employs ESP32-based intelligent control, together with ACS712 current sensors, to provide accurate per-load current measurements and load-specific overcurrent protection. The system is also capable of selective load isolation, meaning that it maintains the operation of healthy circuits and automatically disconnects only the faulty load without affecting other loads. Cloud-based monitoring is implemented via the ThingSpeak platform, enabling real-time remote system surveillance through any internet-connected device. The faulty section of the system is readily identified from the current readings visualised on the cloud dashboard and mobile interface. The proposed system is powered primarily by solar photovoltaic sources and can also operate from the utility grid, providing flexibility across renewable and conventional supply scenarios. Experimental testing demonstrates protection response times in the sub-200 ms range and high-accuracy current monitoring with a mean absolute error below 0.05 A. The key contributions include: selective load protection, real-time IoT-based energy analysis, and a cost-effective open-hardware architecture. The work advances the safety and efficiency of smart energy control systems in distributed renewable energy environments.
Jeevitha Kandasamy, Kalaivani C, Shashank S Bhagwat et al.· 2026 4th International Confe...· 0 citations
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.
I. M. Danjuma, M. Asih, S. S. Garba et al.· International Journal of App...· 0 citations
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
Residential energy conservation is frequently hindered by a lack of real-time visibility into usage, widening the "behavior-action gap" between daily electricity utilization and delayed monthly utility statements. This paper presents the development of a low-cost, open-source, residential solar-powered Internet of Things (IoT) monitoring system. The proposed architecture segments data collection tasks into a Direct Current (DC) renewable generation node and an Alternating Current (AC) appliance-level consumption interface using dual ESP32 microcontrollers. The system collects data from precision inline INA226 shunt sensors on the solar and battery storage paths, alongside a ZMPT101B transformer and an SCT-013 current transformer wrapped in a physical 5-turn wire loop to lift low-power current boundaries above standard resolution floors. Telemetry is streamed asynchronously via Message Queuing Telemetry Transport (MQTT) over a cloud HiveMQ broker. To optimize edge node efficiency, chronological timestamping is completely decoupled from the hardware layer and managed server-side upon database ingestion into InfluxDB. Real-time and historical analytics are visualised using a centralized Grafana web dashboard interface. Experimental results show that the hardware sensing layer maintained high measurement precision, yielding low absolute errors (Verror ≤ 0.38% and Ierror ≤ 32%) and successfully resolving low-power household standby states down to single-digit wattages (e.g., 9W). The event-driven alert system achieved zero-latency multi-tier warnings, verifying that the proposed framework delivers an accessible, high-fidelity sub-metering platform for urban electrification programs.
A. Ja'afar, Aiman Haqeem Ahmad Tarmizi, H. Yusof et al.· International journal of res...· 0 citations
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