Clean Energy Technologies

Special Issue on "AI-Based Optimization and Control in Clean Energy Systems"

A special issue of Clean Energy Technologies (CET) (E-ISSN: 2755-8983)
Deadline for manuscript submission: 11 December 2026

Lead Editor: 

Assoc. Prof. Elkhatib Kamal
Affiliation: Laboratoire des sciences du numérique de Nantes (LS2N), École Centrale de Nantes, Nantes, France
E-mail: elkhatib.ibrahim@ec-nantes.fr
Orcid: https://orcid.org/0000-0001-8952-5521
Research Interests: Artificial intelligence for clean energy; Energy system optimization and control; Smart grids and microgrids; Renewable energy integration and forecasting; etc.

 

Guest Editors:

Prof. Reza Ghorbani
Affiliation: Renewable Energy Design Laboratory, Mechanical Engineering, University of Hawaii at Manoa, Hawaii, USA
E-mail: rezag@hawaii.edu

 

Prof. Abdelouhab AITOUCHE
Affiliation: Automotive Engineering and Transportation Engineering, University of Sciences and Technology of Lille, Lille, France
E-mail: abdel.aitouche@junia.com

 

Prof. Ahmed Ragab
Affiliation: Lead AI Scientist, Natural Resources Canada - Canmet ENERGY, Polytechnique Montréal, Montréal, Canada
E-mail: ahmed.ragab@polymtl.ca

 

Assoc. Prof. Mohamed Kouki
Affiliation: Laboratoire Génie de Production-LGP, University of Toulouse, UTTOP, Tarbes, France
E-mail: mohamed.kouki@uttop.fr

 

Assoc. Prof. Lyes SAAD SAOUD
Affiliation: Department of Computing, Information, and Mathematical Sciences and Technologies, Chicago State University, Chicago, USA
E-mail: lsaadsao@csu.edu

 

Special Issue Information:

Dear Colleagues,

The rapid global transition toward sustainable and low-carbon energy systems has intensified the need for advanced methodologies to manage increasing system complexity. The large-scale integration of renewable energy sources, decentralization of power generation, electrification of end-use sectors, and ambitious decarbonization targets present significant operational and control challenges. Conventional optimization and control approaches often fall short in addressing the uncertainty, variability, and data-intensive nature of modern clean energy systems. In this context, Artificial Intelligence (AI) has emerged as a transformative enabler for improving efficiency, resilience, and sustainability.

This Special Issue aims to explore cutting-edge AI-based optimization and control strategies that support the development and deployment of clean energy technologies. By leveraging machine learning, deep learning, reinforcement learning, and data-driven optimization techniques, AI can enhance renewable energy integration, enable intelligent energy management, improve system flexibility, and facilitate real-time decision-making under dynamic conditions. These capabilities are critical for reducing environmental impact while maintaining reliability and economic performance.

Aligned with the mission of Clean Energy Technologies, this Special Issue seeks to provide a platform for interdisciplinary contributions that bridge theory and practice. It welcomes innovative research, applied studies, and case-driven insights demonstrating how AI-driven solutions accelerate the adoption of clean, efficient, and low-carbon energy systems across different scales—from distributed resources and microgrids to large interconnected networks.

The Special Issue particularly encourages contributions that highlight practical implementations, scalability, and measurable sustainability benefits, as well as collaborations between academia, industry, and policymakers.

Topics of interest include, but are not limited to:

  • AI-driven optimization for renewable energy integration and hybrid energy systems
  • Intelligent control of smart grids and low-carbon energy networks
  • Energy management systems for sustainable and distributed energy resources
  • AI and IoT applications for real-time energy monitoring and control
  • Optimization of energy storage systems for clean energy applications
  • Demand response and energy efficiency in low-carbon systems
  • Predictive maintenance for clean energy infrastructure
  • Case studies demonstrating deployment of AI in clean energy technologies
  • Digitalization and data-driven approaches for sustainable energy transitions

Keywords:

  • Artificial intelligence in energy systems
  • Clean energy technologies
  • Renewable energy integration
  • Smart grids and microgrids
  • Energy system optimization
  • Intelligent control systems
  • Machine learning for power systems
  • Energy storage optimization
  • Sustainable energy management
  • Data-driven energy systems