An Embedded System-Based Real-Time Renewable Energy Monitoring Framework

Intelligent IoT based Load Management System with Real Time Monitoring in Renewable Energy System

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.

Integrated IoT Platform with Web Interface for Wireless Health Monitoring and Battery Energy Management

Battery energy storage (BES) systems are vital in the enhancement of sustainability, performance and reliability in today’s power systems particularly for Backup Power Systems (BES), electric vehicles (EV) and for requiring renewable energy integration. This paper presents the development/evaluation of an ESP32-based smart battery energy storage and health monitoring system with remote access and real time monitoring capabilities. The proposed system uses the appropriate sensors and data collection/monitoring methods to provide continuous monitoring of critical battery parameters i.e. Voltage, Current, Temperature and State of Charge (SOC). The results of experiments proved the system operated as expected under the following parameters: A safe working voltage range for the batteries was maintained (12.1–12.8 V) the maximum allowed charging rate of the batteries was approximately +5 A; overcharging and discharging current rates were managed within acceptable limits. Additionally, accurate tracking of SOC confirmed that energy estimates were reliable, with remarkably consistent and almost linear fluctuations of approximately 5% to 7% during the regulated charging/discharge cycles. The system also identified risk factors such as Deep Discharge (below 40% SOC), Overcharging (above 85% to 90% SOC) and Excessive Rise in Temperature (above 45°C); therefore, the system enabled prompt preventive action to mitigate potential battery deterioration and failure. Due to the low power consumption, built-in Wi-Fi capability and cost-effectiveness of the ESP32, the system is suitable for scalable and remote deployment.

A Low-Cost MQTT-Based IoT Framework for Real-Time Monitoring of Battery Energy Storage Systems

An Intelligent Hybrid Renewable Energy System with AI-Based Energy Management for Sustainable Smart Grid Applications

Hybrid Renewable Energy Systems (HRES) have emerged as an effective solution for addressing the increasing global demand for clean, reliable, and sustainable energy while reducing dependence on fossil fuels. By integrating multiple renewable energy sources such as solar photovoltaic (PV), wind turbines, biomass, and small hydropower with energy storage systems and intelligent energy management strategies, HRES can overcome the intermittent nature of individual renewable resources and ensure continuous power supply. This paper presents a comprehensive hybrid renewable energy framework that combines renewable generation, battery energy storage, smart power converters, and IoT-enabled monitoring for efficient energy utilization in residential, commercial, and remote applications. The proposed system employs intelligent control algorithms to optimize power generation, storage scheduling, and load distribution under varying environmental and load conditions. Real-time monitoring and predictive energy management improve system reliability, reduce operational costs, and enhance grid stability while minimizing carbon emissions. The integration of artificial intelligence and optimization techniques further enables adaptive decision-making, fault detection, and energy forecasting, leading to higher overall system efficiency and resilience. Simulation-based performance evaluation demonstrates that the proposed HRES achieves improved renewable energy utilization, enhanced power quality, reduced energy losses, and increased system reliability compared with conventional standalone renewable energy systems. The proposed architecture offers a scalable, cost-effective, and environmentally sustainable solution for future smart grids and decentralized energy systems. Keywords— Hybrid Renewable Energy System (HRES), Solar Photovoltaic, Wind Energy, Battery Energy Storage System (BESS), Smart Grid, Energy Management System (EMS), Internet of Things (IoT), Artificial Intelligence (AI), Renewable Energy Integration, Power Quality, Energy Optimization, Sustainable Energy.

Power Quality Management for Solar-Based Microgrids Using Integrated Artificial Intelligence and Internet of Things Technologies

An integrated artificial intelligence and Internet of Things-based smart power quality management system for a solar microgrid, incorporating a DC–DC boost converter and a maximum power point tracking system for improving the power conversion efficiency.

Intelligent Battery Management System with Predictive Maintenance and PCM Cooling for Enhanced Energy Storage Reliability

Modern energy storage systems depend on Battery Management Systems (BMS) to be safe, effective and long-lasting. In order to improve battery performance and reliability, this paper integrate a Smart BMS with Phase Change Material (PCM) Cooling Technique and Predictive Maintenance. For real-time monitoring and control, the system uses a number of sensors, including temperature, voltage, current and flame sensors, which are connected to an Arduino Uno and an ESP32-based Internet of Things module. Sensor data is analyzed to anticipate any malfunctions, allowing for proactive maintenance and preventing unplanned downtime. By efficiently controlling battery temperature, PCM cooling method reduces thermal runaway and lengthens battery life. Real-time warnings and remote monitoring are made possible by IoT architecture through a web or mobile interface. Improved safety, operational effectiveness and battery lifespan are demonstrated by experimental evaluation, which makes this strategy a viable option for cutting-edge energy storage applications.