Submission Deadline: 30 November 2025 View: 59 Submit to Special Issue
Prof. Dr. Jihoon Moon
Email: jmoon25@duksung.ac.kr
Affiliation: Department of Data Science, Duksung Women's University, Seoul, 01369, Korea
Research Interests: neural networks, energy forecasting, AI for smart grids, AI-driven industrial automation
Prof. Dr. Jehyeok Rew
Email: jhrew@duksung.ac.kr
Affiliation: Department of Data Science, Duksung Women's University, Seoul, 01369, Korea
Research Interests: deep learning, explainable AI, deep learning for energy systems, computer vision, bioinformatics, AI-driven healthcare analytics
Prof. Dr. Hyeonwoo Kim
Email: hwkim24@sch.ac.kr
Affiliation: Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31476, Korea.
Research Interests: multimodal deep learning, industrial deep learning applications, digital healthcare, deep learning-driven cybersecurity, intrusion detection, deep learning for medical imaging
Deep learning has emerged as a transformative force in energy systems, redefining how engineers and computational scientists approach energy forecasting, optimization, and decision-making. This Special Issue aims to explore the latest breakthroughs in applying deep learning to energy-related domains, including smart grids, renewable energy integration, energy efficiency, and sustainable infrastructure. Recent advances have significantly improved performance in energy demand prediction, grid reliability, and the development of secure and intelligent energy networks.
This Special Issue will bring together research that advances both theoretical frameworks and practical implementations of deep learning in energy systems. Topics of interest include but are not limited to, deep learning-driven energy forecasting, smart grid optimization, secure AI applications in energy networks, and the integration of multimodal data for enhanced decision support. Contributions that explore generative models, privacy-preserving AI, and trustworthy deep learning systems within the energy sector are also welcome. We invite original research articles and comprehensive reviews that contribute to advancing deep learning applications in energy systems and help address pressing global challenges in sustainable energy.
Suggested Themes:
· Deep learning & neural networks in energy system applications
· Deep learning for energy infrastructure Automation, smart grid management & security
· Deep learning-driven renewable energy forecasting & grid optimization
· Multimodal deep learning & retrieval-augmented generation (RAG) for energy analytics
· Deep learning in energy demand prediction, fault detection & maintenance planning
· Generative deep learning for energy modeling & ethical AI in sustainability
· Deep learning-enabled energy informatics & computational energy genomics
· Intrusion detection, deep learning-enhanced cybersecurity in energy networks & federated learning
· Machine unlearning, privacy-preserving AI & trustworthy deep learning for energy systems
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