Special lssues

Deep Learning: Advances, Challenges, and Trending Solutions in Renewable Energy Systems

Submission Deadline: 31 December 2021 (closed)

Guest Editors

Dr. Manogaran MADHIARASAN, Indian Institute of Technology, Roorkee, Uttarakhand, India. Email: mmadhiarasan.cse@sric.iitr.ac.in, mmadhiarasan89@gmail.com

Dr. Mohamed LOUZAZNI, Chouaib Doukkali University, El Jadida, Morocco. louzazni@msn.com

Dr. Mohamed Arezki MELLAL, M’Hamed Bouagar University, Boumerdes, Algeria. mellal.mohamed@gmail.com


Aims: Over the last decade, Deep Learning and Renewable Energy Systems (RES) is growing fast, reasonably achieving substantial progress in various applications. While it has attained outstanding success in real-world applications with a holistic Deep Learning approach, many vital issues typically dwell in Deep Learning to concern legitimate theory and technical aspects. This formidable challenge paves deep insight into Deep Learning to remodel the framework, prompting new demand to advance rapidly in Deep Learning to achieve better accuracy concerns to present-day looming problems in renewable energy systems and smart grids. 


Scope: This special issue aims to present the recent remarkable novel contributions, notable advances and challenges in Deep Learning. This special issue generously provides a fascinating opportunity to the academician, researchers, and industry delegates to exchange their novel idea, intelligent deep learning models solving the potential limitations of the present technology in renewable energy systems.


This special issue attracts papers in a broad area. The list of topics of interest includes, but is not limited to:

•       Innovative Deep Learning Frameworks for RES.

•       Energy, Modeling, and Forecasting.

•       New Theory and Intelligent Hybrid Models of Deep Learning in RES.

•       Deep and Reinforcement Learning in Smart Grid.

•       Intelligent Real-time Algorithms and Optimizers.

•       Nature-Inspired Methods and Algorithms in Deep Learning.

•       Fuzzy approach in Deep Learning in RES.

•       Emerging Applications of Deep Learning and Optimization in RES.

•       High-Performance Computing in RES.

•       Decision Support Systems for Smart grid.

•       Hardware Implementations for Deep Learning in RES.

•       Industry and Research Case Study on Experience, Lessons Learned and Experimental Reports in RES.


Renewable Energy Systems, Smart Grid, Deep Learning, Energy Management Systems, Decision Support Systems, Optimization, Intelligent Hybrid Models, Modeling and Simulation.

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