Clean Energy and Sustainability

Guide for Authors Submit to CES

AI–Driven Smart Energy Systems for Sustainable and Low-Carbon Application

Deadline for manuscript submissions: 30 April 2027.

Guest Editor (1)

Elkhatib Kamal
Dr. Elkhatib Kamal 
Laboratoire des sciences du numérique de Nantes (LS2N), École Centrale de Nantes, Nantes 44300, France
Interests: Artificial Intelligence for Energy Systems; Smart Grid Technologies; Power System Optimization and Control; Renewable Energy Integration; Energy Storage Systems; Machine Learning Applications in Power Systems; Energy Data Analytics; Distributed Energy Resource Management; Digital Twin for Energy Systems; Cyber-Physical Energy Infrastructure
Website: https://www.webofscience.com/wos/author/record/PNI-8210-2026

Co-Guest Editors (5)

Abdelouhab AITOUCHE
Prof. Dr. Abdelouhab AITOUCHE 
Automotive Engineering and Transportation Engineering, University of Sciences and Technology of Lille, Lille 59650, France
Interests: Diagnostic; FTC; automation; SoS; nonlinear control; FC; Renewable energy; IWT;
Website: https://scholar.google.com/citations?user=7WRRP14AAAAJ&hl=fr
Mohan Dolatabadi
Dr. Mohan Dolatabadi 
Optimization and Mathematical Modeling, Università degli Studi di Salerno, Fisciano SA 84084, Italy
Interests: Numerical Optimization
Website: https://scholar.google.com/citations?user=-IFuNVUAAAAJ&hl=en
Reza Ghorbani
Prof. Dr. Reza Ghorbani 
Renewable Energy Design Laboratory, Mechanical Engineering, University of Hawaii at Manoa, Hawaii 96822, USA
Interests: Renewable Energy; Dynamics; Control
Website: https://scholar.google.com/citations?user=eyvnf6cAAAAJ&hl=en
Mohamed Kouki
Dr. Mohamed Kouki 
Laboratoire Génie de Production-LGP, University of Toulouse, UTTOP, Tarbes 65000, France
Interests: Power Systems, Control, Modal Analysis, Energy Management, Microgrids
Website: https://scholar.google.com/citations?user=8_AnHl0AAAAJ&hl=fr
Ahmed Ragab
Prof. Dr. Ahmed Ragab 
Lead AI Scientist, Natural Resources Canada – CanmetENERGY, Polytechnique Montréal, Montréal QC H3T 1J4, Canada
Interests: Artificial Intelligence & Machine Learning, Data & Decision Fusion, Causality Analysis, Supervisory
Website:

Special Issue Information

The transition toward sustainable and low-carbon energy systems is accelerating worldwide, driven by the urgent need to reduce greenhouse gas emissions while ensuring reliable and efficient energy supply. Artificial intelligence (AI) is playing an increasingly transformative role in enabling intelligent monitoring, forecasting, optimization, and control across modern energy infrastructures. By supporting the integration of distributed renewable energy resources, enhancing operational flexibility, and improving demand-side management, AI-driven smart energy systems are emerging as key enablers of resilient and sustainable energy transitions.

This Special Issue aims to provide a platform for high-quality research contributions addressing recent advances in AI-enabled smart energy technologies and their applications in sustainable and low-carbon energy systems. Particular emphasis is placed on interdisciplinary approaches combining machine learning, data analytics, digital platforms, and intelligent optimization techniques with renewable energy integration, smart grids, and advanced energy management systems across urban, industrial, and distributed environments.

Topics of interest include, but are not limited to:
•    AI-enabled monitoring, forecasting, and control of smart grids 
•    Intelligent integration of distributed renewable energy resources 
•    Machine learning methods for energy demand prediction and response strategies 
•    Data-driven optimization of energy storage and hybrid energy systems 
•    Digital twins and cyber-physical platforms for sustainable energy infrastructures 
•    Multi-energy systems and sector coupling supported by AI technologies 
•    Intelligent decision-support tools for sustainable energy planning 
•    Real-world applications and implementation case studies of AI-based smart energy systems
 
Original research articles, review papers, and application-oriented case studies are welcome. This Special Issue seeks to highlight innovative AI-driven solutions that accelerate the deployment of efficient, resilient, and low-carbon energy systems supporting global sustainability goals.

Published Papers (0 Papers)

TOP