Advanced Machine Learning and Intelligent Optimization in Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 10 February 2026 | Viewed by 5
Special Issue Editor
Interests: machine learning; optimization; modelling; energy complex system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of energy systems, from renewable generation to intelligent grids and electrified transportation, is being accelerated by advances in artificial intelligence and optimization techniques. Integrating advanced machine learning with intelligent optimization enables unprecedented capabilities in forecasting, decision-making, system control, and resource allocation, while enhancing resilience, efficiency, and sustainability.
This Special Issue aims to present and disseminate the latest advances in the theory, algorithms, applications, and case studies of AI-driven and optimization-based solutions for modern energy systems. We seek contributions that bridge the gap between data-driven intelligence and domain-specific engineering, fostering innovations that are both technically rigorous and practically impactful.
Topics of interest include, but are not limited to, the following:
- Machine learning and deep learning for renewable energy forecasting, grid stability, and asset management;
- Intelligent optimization for energy scheduling, dispatch, and control;
- Hybrid AI-optimization frameworks for energy system planning and operation;
- Data-driven predictive maintenance and fault diagnosis in power systems and equipment;
- Reinforcement learning for real-time energy management and control;
- Intelligent energy storage management and integration with distributed generation;
- Optimization and AI applications in electric mobility and charging infrastructure;
- Multi-objective and stochastic optimization for complex energy networks;
- AI-enabled energy market modeling and trading strategies;
- Cybersecurity and anomaly detection in intelligent energy systems.
Dr. Ali Jamali
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- advanced machine learning
- artificial intelligence in energy systems
- intelligent optimization
- renewable energy forecasting
- energy management systems
- smart grids
- deep learning for energy applications
- reinforcement learning in power systems
- predictive maintenance in energy networks
- data-driven energy optimization
- energy storage optimization
- electric vehicle charging optimization
- distributed energy resources
- multi-objective optimization
- stochastic optimization in energy systems
- fault detection and diagnosis
- intelligent control for energy systems
- grid resilience and stability
- AI-enabled energy market analysis
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