Artificial Intelligence in Modern Power and 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: 20 January 2026 | Viewed by 22
Special Issue Editors
Interests: machine learning; artificial neural network; deep learning; reinforcement learning; signal processing; image processing; time series analysis; energy forecasting; smart-grids
Special Issues, Collections and Topics in MDPI journals
Interests: operational energy design; optimal system operation; energy planning; smart grid resilience; cross-sector/vector integration
Interests: power system design; power system control; micro-grids; smart grid; energy performance analysis
Special Issues, Collections and Topics in MDPI journals
Interests: power systems; micro-grids; smart grids; multi-energy hub systems; renewable energy sources; energy communities; energy storage; hydrogen
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to collect original and innovative contributions on the application of artificial intelligence techniques in power and energy systems. We welcome work addressing machine learning (ML), deep learning (DL), reinforcement learning (RL), large language models (LLMs), and other data-driven solutions, which are emerging as powerful tools for modeling, forecasting, optimization, and decision support in modern energy ecosystems.
As the energy sector undergoes rapid transformation driven by the energy transition, renewable integration, electrification, and digitalization, AI is offering new pathways for the construction of more resilient, efficient, sustainable, and intelligent systems capable of handling increasing complexity and uncertainty.
For this Special Issue, topics of interest include, but are not limited to, the following:
- Energy demand and generation forecasting using ML and DL methods;
- RL-based approaches for adaptive control, energy management, and electricity market participation;
- Fault prediction, anomaly detection, predictive maintenance, real-time monitoring, and system diagnostics using ML and DL methods;
- Use of LLMs for enhanced user engagement through natural language interfaces and decision support;
- Generative AI for synthetic data generation, scenario modeling, and system design.
We encourage both theoretical developments and practical case studies that demonstrate real-world applicability and impact.
Dr. Amedeo Buonanno
Dr. Salvatore Fabozzi
Dr. Maria Valenti
Prof. Dr. Giorgio Graditi
Guest Editors
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
- machine learning
- deep learning
- reinforcement learning
- LLM
- generative AI
- smart grid
- energy system
- power system
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