Optimisation and Management of Energy Systems Through Artificial Intelligence
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: 31 July 2026 | Viewed by 19
Special Issue Editor
Special Issue Information
Dear Colleagues,
Energy systems have faced significant challenges due to increased loads, from homes with electrical appliances to electric vehicles. This situation requires efficient systems that maximise production and rationalise usage. With the widespread integration of renewable energy sources, the growth of distributed generation, the implementation of smart grids, and the need for real-time operational decisions, traditional analytical methods are no longer sufficient to ensure efficiency, reliability, and resilience. In this context, Artificial Intelligence (AI) has emerged as a tool that, together with data provided by the electrical system, can extract meaningful patterns, predict system behaviour, and support decision-making at multiple levels.
Advances in machine learning, deep learning, optimisation algorithms, and intelligent control have opened new avenues for improving energy forecasting, load management, fault detection, predictive maintenance, resource allocation, and system planning. These approaches enable more adaptable and robust operational strategies, reduce costs, improve energy efficiency, and contribute to the sustainability of energy infrastructures.
This Special Issue aims to present and disseminate the latest research, as well as practical solutions that use AI for the optimisation and management of energy systems.
Topics of interest for publication include, but are not limited to, the following:
- AI-based energy forecasting (load, generation, prices, renewable variability);
- Optimisation of energy systems and smart grids;
- Intelligent energy management strategies for industrial and residential sectors;
- Predictive maintenance and fault diagnosis using AI;
- Integration and optimisation of renewable energy sources;
- Intelligent control of distributed energy resources;
- AI-supported grid stability assessment and resilience enhancement;
- Data-driven decision support systems for energy planning;
- Reinforcement learning for real-time control and optimisation;
- Multi-objective optimisation in energy systems;
- Digital twins and simulation frameworks enhanced by AI;
- Cybersecurity in AI-driven energy systems;
- Physical Asset Management in energy systems;
- Life Cycle assessment supported by generative AI.
Dr. Balduíno Mateus
Guest Editor
Manuscript Submission Information
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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.
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Keywords
- artificial intelligence
- machine learning
- deep learning
- energy systems optimisation
- smart grids
- energy management
- load forecasting
- renewable energy integration
- predictive maintenance
- fault detection
- fault diagnosis
- distributed energy resources
- intelligent control
- energy planning
- data-driven decision making
- reinforcement learning
- reliability
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