Aug 2026· Global Trends in Science and Technology· Vol 2, pp. 1-23· 0 citations· 76 references
TL;DR
This review highlights the role of machine learning techniques, deep learning models, and their applications in various areas such as smart grid, renewable energy prediction, fault detection, energy prediction, and predictive maintenance, and covers benchmark datasets, evaluation platforms, and the performance of AI algorithms.
Abstract
The integration of Artificial Intelligence (AI) has emerged as a crucial technology for making electrical systems intelligent and efficient.AI has been a pivotal technology in making electrical systems intelligent and efficient, enhancing automation, reliability, and decision-making. In this review, the integration of AI within the field of electrical engineering is explored, highlighting the role of machine learning techniques, deep learning models, and their applications in various areas such as smart grid, renewable energy prediction, fault detection, energy prediction, and predictive maintenance. It also covers benchmark datasets, evaluation platforms, and the performance of AI algorithms. In addition, the review identifies key challenges, such as Interpretability of the Models, Data Quality, Security of Data and Computational Complexity, and investigates new trends like Explainable AI, Digital Twins, Federated Learning and Edge AI for future electrical systems.
With the development of modern energy systems, there is a need to operate increasingly intelligent electricity systems. Smart grids are advanced electricity networks that allow for integrating new technologies, increasing the reliability and performance of the power system, addressing the issues of renewable energy sources, growing electricity consumption, and real-time monitoring of the operating processes. Artificial Intelligence (AI) can be applied in different areas of smart grid to make the electricity system more reliable and competitive. The methods include Machine Learning, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Fuzzy Logic, and Expert Systems. They are used to perform demand side management, predict electricity consumption, forecast renewable energy production, detect and diagnose grid failures, optimize maintenance, enhance protection and security, and manage energy consumption. However, there are challenges for implementing AI in the smart grid, such as data privacy, security, complexity, lack of transparency, and legislation. The current research explores the different aspects of applying AI technologies to smart grids. The paper provides some of the most common applications of AI and discusses the challenges and future outlook for AI-driven smart grids. The study emphasizes the significance of artificial intelligence for developing the next-generation smart grid.
A. Faiz, A. S, A. T· International Journal of Res...· 1 citation
Battery Management Systems (BMS) are indispensable for ensuring the operational safety, reliability, and performance optimization of rechargeable energy storage systems, particularly lithium-ion batteries employed in electric vehicles, grid-scale energy storage, and portable electronic devices. Conventional BMS methodologies predominantly rely on electrochemical models, equivalent circuit models, and rule-based control strategies, which often exhibit limited adaptability under dynamic operating and environmental conditions. Recent advancements in Artificial Intelligence (AI), including machine learning, deep learning, and reinforcement learning techniques, have demonstrated significant potential in enhancing battery state estimation, fault diagnosis, degradation prediction, and energy management. AI-driven approaches facilitate the extraction of complex nonlinear relationships from battery data, thereby improving estimation accuracy and operational efficiency. This review comprehensively examines the latest developments in AI-based battery management systems, highlighting various intelligent algorithms, their applications, associated challenges, and prospective research directions for next-generation energy storage technologies.
Keywords: Artificial Intelligence, Battery Management System, Lithium-Ion Batteries, Machine Learning, Deep Learning, State of Charge Estimation, State of Health, Electric Vehicles, Energy Storage Systems.
Deepali Amit Shinde Deepali Amit Shinde, Padmavati Ramesh Nagansure Padmavati Ramesh Nagansure· International Scientific Jou...· 0 citations
AI and Machine Learning (ML) are powerful and rapidly evolving technologies, reshaping intelligent decision-making, automation, and data-driven problem-solving in various industries. In recent years, the potential of intelligent systems to manage complex data, to learn adaptive patterns, and to assist in autonomous decision-making has been greatly improved by the emergence of new technologies, such as Deep Learning, Transformer-based Models, Generative AI, Large Language Models (LLMs), Explainable AI (XAI), Federated Learning, Edge AI, and Digital Twin. These advancements have empowered the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors with enhanced efficiency, productivity, and service quality, driving faster AI adoption across these industries. The innovations have contributed to improved efficiency, productivity, and service quality, leading to increased adoption of AI across the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors. The fundamentals of intelligent AI systems, key learning paradigms, notable technological advances, and applications are discussed in this chapter, providing a comprehensive review of intelligent AI systems and advanced ML. It also explores the potential of AI to solve real-world problems and discusses some of the critical challenges associated with data privacy, model interpretability, computational complexity, algorithmic bias, and ethical considerations. Finally, the chapter proposes new research directions towards the development of trustworthy, explainable, sustainable, and human-centric AI systems. This review offers a brief overview of current research progress and prospects on the development of intelligent AI systems and advanced machine learning, which will facilitate the future generation of trustworthy and ethical AI-based solutions.
Dr.S. Gopi, D. Kumar, Dr Vontela Neelima et al.· Journal of Intelligent Decis...· 0 citations
The rapid emergence of Industry 4.0 technologies has significantly transformed manufacturing industries by
integrating Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cloud Computing, Big Data Analytics, and CyberPhysical Systems. Among these advancements, predictive maintenance has emerged as one of the most promising applications
for improving operational efficiency and equipment reliability. Traditional maintenance strategies, such as corrective and
preventive maintenance, often lead to increased operational costs, unnecessary maintenance activities, and unexpected
equipment failures. Consequently, organizations are increasingly adopting AI-powered predictive maintenance systems that
utilize deep learning techniques to predict machine failures before they occur. Deep learning models, including Convolutional
Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders, Recurrent Neural Networks (RNN), and
Transformer-based architectures, have demonstrated remarkable capabilities in analyzing large volumes of industrial sensor
data and identifying hidden patterns associated with equipment degradation. This study provides a comprehensive review of AIpowered predictive maintenance using deep learning approaches, examining its applications, benefits, challenges, and future
opportunities. The study further proposes a conceptual framework integrating AI, IIoT, and deep learning technologies to
improve maintenance decision-making. The findings indicate that deep learning significantly enhances fault diagnosis,
Remaining Useful Life (RUL) prediction, anomaly detection, and maintenance optimization. However, challenges such as data
quality issues, model interpretability, cybersecurity concerns, and integration complexities continue to influence industrial
adoption. The study concludes that AI-powered predictive maintenance will become a fundamental component of smart
manufacturing and Industry 5.0 initiatives.
D. Mohamed, N. Malathi, Mrs. P. Vanithamani et al.· International Journal for Re...· 0 citations
Artificial intelligence (AI) has emerged as a transformative technology capable of addressing complex engineering challenges through advanced data-driven modeling, prediction, and optimization techniques. In the context of sustainable energy systems, hydrogen production via water electrolysis has attracted considerable attention as a promising pathway toward carbon-neutral energy generation. However, the widespread deployment of electrolyzer technologies remains constrained by challenges related to energy efficiency, operational stability, system degradation, and dynamic process control. In response, machine learning (ML) techniques have increasingly been integrated into electrolyzer systems to enhance performance prediction, adaptive control, predictive maintenance, and operational optimization. This review presents a comprehensive and engineering-oriented analysis of recent advancements in ML applications for hydrogen production via electrolysis. The study first outlines the fundamental principles of ML, including major learning paradigms, data processing approaches, and commonly adopted algorithms for electrochemical systems. Subsequently, the review critically examines ML-driven strategies for hydrogen production optimization, hyperparameter tuning, intelligent process control, system design enhancement, and degradation monitoring in electrolyzer technologies such as Proton Exchange Membrane Water Electrolyzers (PEMWE) and Anion Exchange Membrane Water Electrolyzers (AEMWE). Comparative analysis of algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINN) is also discussed from both electrochemical and engineering perspectives. Furthermore, current challenges involving data quality, model interpretability, computational complexity, and industrial deployment are critically evaluated. Overall, this review provides strategic insights into future intelligent electrolyzer systems and highlights critical research directions for scalable and sustainable green hydrogen production.
Muhammad Asyraf Abdullah, A. Sulong, E. Majlan et al.· Jurnal Kejuruteraan· 0 citations
The integration of artificial intelligence into instrumentation and measurement systems has emerged as a transformative force across industrial, environmental, and scientific domains. This article provides a systematic overview of AI applications in instrumentation, encompassing intelligent sensor data processing, predictive maintenance, automated meter reading, and fault diagnosis. Drawing upon recent advances documented in the literature, we examine the methodological landscape ranging from conventional machine learning to deep learning and large language models. Key benefits include enhanced measurement accuracy through intelligent compensation, reduced downtime through predictive maintenance, and improved operational efficiency through automation. However, significant challenges persist, including the blackbox nature of AI models, data scarcity, uncertainty quantification, and the gap between laboratory performance and realworld deployment. We argue that the future of intelligent instrumentation lies in hybrid approaches that integrate datadriven AI with conventional modeldriven methods, thereby combining the pattern recognition capabilities of AI with the interpretability and rigor of physicsbased models.
Fujie Lu· Academic Journal of Manageme...· 0 citations