Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 689-693· 0 citations· 20 references
Abstract
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.,
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
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
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
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
Reliable electrical power availability is essential for various applications, especially in facilities that rely on continuous power supply. To address power interruptions, this study developed an Internet of Things (IoT)-enabled Automatic Transfer Switch (ATS) monitoring system based on an expert system approach for electrical energy backup. The proposed system integrates an ESP32 microcontroller with a ZMPT101B voltage sensor, PZEM-004T power meter, DC voltage sensor, relay module, SIM900A GSM module, inverter, LCD, and a 12 V 35 Ah battery. The system automatically changes the power source from the utility supply (PLN) to the backup battery when a power failure occurs and restores the utility source after normal supply conditions are detected. System conditions and electrical parameters are monitored remotely in real time using the Blynk platform, which also provides system notifications. For decision-making, expert system principles are implemented through a Mamdani Fuzzy Inference System (FIS) developed in MATLAB R2018b, evaluating utility voltage, battery voltage, and load condition to determine the appropriate ATS power-source status. Experimental evaluation at the Instrumentation and Control Laboratory, Universitas Negeri Jakarta, confirmed the system functionality, with average measurement errors of 0.45% for the ZMPT101B sensor and 0.24% for the DC voltage sensor, while the PZEM-004T produced errors of 0.45% for voltage, 3.7% for current, and 0.91% for power measurements. The ATS completed the source transfer within 3 seconds, with the inverter maintaining an output voltage between 217 and 232 VAC. Under a 50 W load, the 12 V 35 Ah battery provided backup power for 3 hours and 30 minutes, and the MATLAB simulation generated a crisp output of 1.86, corresponding to the battery-supplied load condition. Beyond these technical validations, the scientific contribution of this research lies in the successful integration of a Mamdani FIS with IoT-based monitoring to enable adaptive, rule-based decision-making that overcomes the rigidity of conventional fixed-threshold ATS systems. Practically, this intelligent backup solution offers an affordable and reliable alternative for critical infrastructures, such as small-scale healthcare facilities and data centers, particularly in regions with unstable grid conditions. The modular system architecture also provides a scalable foundation for future integration with renewable energy sources and expansion to higher-capacity industrial applications.
Rafiuddin Syam, Ara Akdzal Al Tariq, Efri Sandi et al.· SPEKTRA Jurnal Fisika dan Ap...· 0 citations
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