Guest Editor(s)
Dr. Emrah ASLAN
Email: emrahaslan@artuklu.edu.tr
Affiliation: Department of Computer Engineering, Faculty of Engineering and Architecture, Mardin Artuklu University, Mardin, Turkey
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Research Interests: artificial intelligence in energy systems, machine learning and deep learning, predictive maintenance, fault diagnosis, condition monitoring, remaining useful life prediction, smart grids, renewable energy systems, energy data analytics, explainable AI applications for sustainable and reliable energy infrastructures
Dr. Yıldırım ÖZÜPAK
Email: yildirim.ozupak@dicle.edu.tr
Affiliation: Department of Electricity and Energy, Silvan Vocational School, Dicle University, Diyarbakır, Turkey
Homepage:
Research Interests: power electronics, power systems analysis, power converters, power systems simulation, renewable energy technologies, power systems modelling, transformers, machine learning
Summary
The global energy sector is undergoing a profound transformation driven by the rapid integration of renewable energy resources, smart grid technologies, distributed energy systems, and digitalization. At the same time, increasing energy demand, climate change concerns, and the need for sustainable energy management require innovative solutions that improve efficiency, reliability, resilience, and operational performance.
Artificial intelligence (AI), machine learning (ML), deep learning (DL), and data-driven analytics have emerged as powerful tools for addressing these challenges. These technologies enable advanced forecasting, predictive maintenance, fault diagnosis, energy optimization, condition monitoring, asset management, and intelligent decision-making across modern energy infrastructures.
This Special Issue aims to provide a platform for researchers, engineers, and industry practitioners to present cutting-edge developments, methodologies, and applications of AI-driven technologies in energy systems. Contributions focusing on both theoretical advances and practical implementations are highly encouraged.
Suggested themes include, but are not limited to:
· Artificial intelligence for smart grids
· Machine learning in renewable energy systems
· Predictive maintenance and fault diagnosis
· Condition monitoring of energy assets
· Energy forecasting and demand prediction
· Digital twins for energy applications
· Explainable AI in energy systems
· Energy optimization and management
· Battery health monitoring and prognostics
· Intelligent power system operation and control
· Sustainable and resilient energy infrastructures
Keywords
artificial intelligence, machine learning, deep learning, smart grids, renewable energy, predictive maintenance, fault diagnosis, energy forecasting, explainable AI, sustainable energy systems
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