Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 163-170· 0 citations· 11 references
Abstract
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.
The rapid growth in electricity consumption has increased the need for intelligent energy management and protection systems. Conventional electrical monitoring systems lack real-time analysis and remote accessibility, leading to energy wastage and delayed fault response. This paper presents a Smart Energy Optimization and Protection System using Internet of Things (IoT) technology for efficient monitoring, fault detection, and energy management. The proposed system continuously monitors electrical parameters such as voltage, current, and power consumption using sensors interfaced with an embedded controller. The collected data is transmitted to a cloud platform through IoT connectivity, enabling real-time monitoring and remote access through mobile or web applications. The system identifies abnormal conditions such as overload, short circuit, over-voltage, and excessive energy consumption using threshold-based analysis. Upon detecting faults, immediate alerts and protection mechanisms are activated to prevent equipment damage and improve system safety. Additionally, the system provides energy optimization suggestions to reduce unnecessary power consumption. The integration of IoT enhances system scalability, accessibility, and efficiency, making the proposed model suitable for residential, commercial, and industrial applications.,
K. Thamizhazhakan, D. Vinoth, S. Bharanivelan et al.· 2026 6th International Confe...· 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
To ensure reliable operation of the distribution system, feeder faults, transformer stress and abnormal operating conditions need to be detected in a timely fashion. This paper proposes an edge-intelligent monitoring and real-time communication system for a three-phase 11 kV/415/230 V power distribution system, which is based on IoT. The proposed system combines the sensing of the voltage, current, active power and the LT-side and transformer-side temperature sensors with local decision logic based on thresholds and remote communication through a dedicated mobile phone and control-room interface. A mathematical model was derived to explain the following: phase voltage, phase current, power utilization, voltage deviation, overcurrent, imbalance and temperature state. The system was tested using scenarios in MATLAB/Simulink representing various operating conditions such as overloading, undervoltage, overvoltage, overheating, line-fault and normal operating conditions. The simulation results showed that the monitoring logic maintained a normal state correctly without triggering false alarms, generated alarm at warning stage when progressive overload occurred, triggered alarm at undervoltage and over-voltage at 8.0 s, and generated the alarm in the line-fault state when the voltage collapsed in combination with current surge, and triggered the alarm at warning state during the overheating simulation. Under the line fault condition, the maximum current was increased to 203.43A as compared with 94.48A under normal condition, and the overheating condition reached 98.34 °C. The results show that the integration of electrical and thermal monitoring with edge-level decision logic leads to a better situation awareness and quicker operator response. The proposed framework provides a scalable smart distribution monitoring platform and a future enhancement path towards implementation with hardware, event logging, fault-location techniques and machine-learning based fault classification.
Unknown authors· Journal of Global Social Tra...· 0 citations
The increasing demand for renewable energy has highlighted the need for efficient and reliable management of solar power plants. Solar plants, whether residential or industrial, require continuous monitoring to ensure optimal performance, detect faults, and maximize energy generation. Manual supervision is often time-consuming, prone to errors, and insufficient for detecting real-time anomalies. To address these challenges, this paper presents an IoT-based Solar Plant Monitoring System that enables remote, real-time observation and management of solar energy systems. The proposed system integrates sensors, microcontrollers, and IoT-enabled communication modules to collect key parameters such as solar panel voltage, current, temperature, and battery status. These parameters are transmitted over the internet to a centralized cloud platform, allowing plant operators to monitor system performance from any location using smartphones, laptops, or tablets. Alerts are generated automatically in case of abnormal readings, such as low voltage, panel overheating, or battery faults, facilitating proactive maintenance and minimizing downtime. By leveraging IoT technology, the system not only improves the efficiency and reliability of solar power generation but also reduces operational and maintenance costs. The proposed system supports data logging, historical analysis, and performance optimization. Additionally, the integration of cloud-based monitoring ensures scalability and flexibility, allowing expansion to multiple solar plants and large-scale installations.
S. S., A. S, D. K. et al.· International Conference Com...· 0 citations
Renewable energy has become a key solution for addressing the increasing global demand for sustainable and environmentally friendly power generation. Effective monitoring of renewable energy systems is essential to ensure efficient operation, minimize energy losses, and improve system reliability. This paper presents an Embedded System-Based Renewable Energy Monitoring System designed for real-time acquisition, processing, and transmission of operational parameters from renewable energy sources such as solar photovoltaic (PV) panels and wind energy systems. The proposed system employs a microcontroller integrated with voltage, current, temperature, and environmental sensors to continuously monitor system performance. The collected data are processed locally and transmitted to a cloud-based monitoring platform through wireless communication technologies such as Wi-Fi or GSM, enabling remote supervision and data visualization. Threshold-based fault detection mechanisms generate alerts whenever abnormal operating conditions are identified, allowing timely maintenance and reducing system downtime. Experimental evaluation demonstrates that the proposed embedded monitoring system provides accurate sensor measurements, low power consumption, reliable wireless communication, and real-time performance monitoring. The system offers a cost-effective, scalable, and energy-efficient solution suitable for residential, industrial, and smart grid renewable energy applications, thereby enhancing operational efficiency and supporting sustainable energy management.
Lenkalapally Harika, Biram Rasagna, A. K. Rathod· International Journal of Sci...· 0 citations
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