AI-Driven Innovations in Sustainable Energy Systems: Advances in Optimization, Storage, and Conversion

Submission Deadline: 01 September 2025 View: 84 Submit to Special Issue

Guest Editors

Prof. Talal Yusaf

Email: t.yusaf@cqu.edu.my

Affiliation: School of Engineering and Technology, Central Queensland University, QLD, 4702, Australia

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Research Interests: hydrogen, solar energy

Prof. Kumaran Kadirgama

Email: kumaran@umpsa.edu.my

Affiliation: Faculty of Mechanical and Automotive Engineering Technology, University Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan 26600, Malaysia

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Research Interests: mechanical engineering, fluid mechanics, heat transfer, thermal energy storage, thermal management, solar energy

Assoc. Prof. Sudhakar Kumarasamy

Email: sudhakar@umpsa.edu.my

Affiliation: Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan, 26600, Malaysia

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Research Interests: heat transfer, thermal energy storage, thermal management, solar energy

Summary

The transition to sustainable energy systems is critical in addressing global energy challenges, reducing carbon emissions, and ensuring energy security. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, revolutionizing energy generation, storage, distribution, and consumption. AI-driven techniques such as machine learning, deep learning, and predictive analytics are enhancing energy efficiency, optimizing grid operations, and enabling smarter decision-making in energy management. With the increasing integration of renewable energy sources like solar, wind, and hydropower, AI is playing a crucial role in overcoming intermittency issues, improving demand forecasting, and automating energy storage solutions. Additionally, AI-driven predictive maintenance is extending the lifespan of energy infrastructure, minimizing operational costs, and maximizing performance. The convergence of AI with emerging technologies such as digital twins, edge computing, and the Internet of Things (IoT) is further advancing the landscape of intelligent energy systems.

This Special Issue aims to highlight cutting-edge research and practical applications of AI in sustainable energy systems. It seeks contributions that explore AI-driven innovations in energy optimization, smart grid management, energy storage solutions, and efficient energy conversion technologies. The issue will serve as a platform to showcase theoretical advancements, computational models, and experimental studies that push the boundaries of AI applications in energy engineering. This Special Issue invites interdisciplinary contributions that bridge the gap between AI, computational science, and sustainable energy engineering. Researchers, industry practitioners, and policymakers are encouraged to submit original research articles, review papers, and case studies that address the latest challenges and opportunities in AI-powered energy solutions.

Potential topics for this Special Issue include, but are not limited to:
· AI in Renewable Energy Systems
· AI-driven forecasting for solar, wind, and hydro energy generation
· Optimization of hybrid renewable energy systems
· AI-enhanced energy harvesting and conversion efficiency
· AI for Energy Storage and Management
· Machine learning for battery performance prediction and management
· AI-driven hydrogen production and storage optimization
· Smart energy storage integration with AI-based control systems
· Smart Grid and Energy Distribution
· AI for real-time grid monitoring and fault detection
· Demand-side energy management using AI algorithms
· AI-enhanced microgrid and distributed energy resource (DER) optimization
· Predictive Maintenance and Fault Diagnosis
· AI-driven predictive maintenance for energy infrastructure
· Deep learning for anomaly detection in power plants and grids
· AI-based failure diagnostics in renewable energy assets
· AI-Driven Energy Efficiency and Sustainability
· AI applications in industrial energy efficiency and process optimization
· AI-enhanced building energy management and HVAC optimization
· AI for carbon footprint reduction in energy-intensive industries
· Emerging AI Technologies in Energy Systems
· AI-powered digital twins for energy system modeling and control
· Edge computing and AI for decentralized energy networks
· Explainable AI (XAI) for energy decision-making and policy planning

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