Jul 2026· Journal of Electrical Engineering and Automation· Vol 8, pp. 222-232· 0 citations· 10 references
TL;DR
Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms, proving the feasibility of the proposed system design.
Abstract
Intelligent monitoring and control of modern microgrids have become essential to increase the efficiency, reliability and fault tolerance of the microgrid. This research work proposes an autonomous microgrid management system with AI-powered load and fault prediction that combines renewable energy sources, IoT and cloud computing in one embedded system. The proposed system encompasses an ESP32 microcontroller that collects the values of voltage and current using sensors, calculates electrical parameters, and transfers data to the cloud database (Firebase) via Wi-Fi connection. A web dashboard is used to provide real-time monitoring of the system, while a machine learning algorithm is used to predict future load and detect abnormal states for proactive fault management. An automatic relay-based load control scheme is applied to protect the system from abnormal load faults. Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms. The proposed AI prediction model has achieved 95.3% accuracy, proving the feasibility of the proposed system design.
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
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.
Nagendra Singh, Andrei Dzeikalo, Bhagyanagar Rajagopal et al.· International journal of mat...· 0 citations
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
V. R. Kumar, P. Poornima, M. Abinesh et al.· International Conference on...· 0 citations
The increasing integration of renewable energy and distributed resources makes accurate forecasting essential for reliable and efficient microgrid energy management; however, existing TFT-based forecasting studies primarily focus on prediction and lack direct integration with predictive control and cyber-resilient operation. Therefore, this study proposes a TFT-MPC-CR framework that combines Temporal Fusion Transformer (TFT) forecasting, Model Predictive Control (MPC), AI-based False Data Injection (FDI) detection, and adaptive resilient control. The framework is implemented in Python using PyTorch, with the Microgrid PV-EV Charging Dataset obtained from Kaggle, where PV generation, load demand, battery SOC, EV charging, and grid variables are used for learning. TFT predicts future PV generation and energy demand, and MPC optimizes battery, EV, and grid power allocation, while the cybersecurity layer detects manipulated measurements and activates resilient MPC. As a target experimental outcome, the proposed framework is designed to achieve at least 5–10% lower forecasting error than conventional TFT and improve operational resilience under FDI attacks. The framework is expected to provide accurate prediction, efficient energy management, rapid attack response, and secure autonomous microgrid operation.
Abosalah Solaman, Ali Khezoo, Abdusalam Moustafa et al.· American Journal of Manageme...· 0 citations
A Smart HVAC system that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, cloud-based analytics, and machine learning to enhance energy efficiency, thermal comfort, and reliability is presented.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
Accurate estimation of battery State of Charge (SOC) is essential for the reliable and efficient operation of Battery Management Systems (BMS) in electric vehicles, renewable energy storage, and portable electronics. Conventional techniques such as open-circuit voltage measurement and Coulomb counting suffer from limitations including sensitivity to operating conditions, cumulative errors, and reduced accuracy under dynamic loads. This paper presents an integrated hardware–software framework for real-time SOC estimation and intelligent power control using a machine learning approach. An Arduino-based sensing layer acquires voltage, current, temperature, and discharge time, which are processed using a hyperparameter-optimized Random Forest regression model implemented in Python. The predicted SOC is transmitted to an ESP32 microcontroller that executes a threshold-based load management strategy using optocoupler-isolated relay switching. Experimental results demonstrate high prediction accuracy with a Mean Absolute Error of 0.0062% and R² of 99.9997%, outperforming conventional and deep learning-based approaches. The proposed system offers a low-cost, scalable, and IoT-enabled solution for precise battery monitoring and adaptive load control in real-time applications.
Unknown authors· Engineering Research Express· 0 citations
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