Application of Machine Learning in Modern Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".
Deadline for manuscript submissions: 25 March 2026 | Viewed by 39
Special Issue Editor
Interests: cyber–physical systems modeling; power systems stability and optimization; machine learning and deep learning in smart grids; grid cybersecurity; renewable energy integration; distributed systems; microgrids
Special Issue Information
Dear Colleagues,
This Special Issue, "Application of Machine Learning in Modern Power Systems", brings together state-of-the-art research on how machine learning can address the growing complexity and data-driven nature of modern energy systems. It focuses on topics such as anomaly and fault detection, predictive maintenance, cybersecurity, demand-side management, load forecasting, and enhancing system stability and resilience. The call highlights the role of ML in improving monitoring, operation, and control of smart grids, while also supporting integration with emerging paradigms like the energy Internet. Key challenges emphasized include data quality limitations, model generalization, interpretability in safety-critical contexts, vulnerability of ML models to cyberattacks, and the gap between simulation studies and real-world deployment. By aggregating diverse contributions, this Special Issue serves as a platform for interdisciplinary exchange, enabling researchers to identify open problems, benchmark solutions, and advance the deployment of AI-driven tools for secure, efficient, and resilient power systems.
The collection particularly emphasizes the critical importance of developing robust machine learning frameworks that can operate effectively in the dynamic and uncertain environment of modern power grids. With the increasing penetration of renewable energy sources, distributed generation, and electric vehicles, power systems face unprecedented variability and complexity that traditional analytical methods struggle to handle. The featured research explores innovative approaches, including federated learning for privacy-preserving grid optimization, reinforcement learning for real-time energy management, and hybrid models that combine physics-based knowledge with data-driven insights to enhance reliability and efficiency. These contributions demonstrate how ML techniques can adapt to the stochastic nature of renewable resources while maintaining grid stability and meeting evolving consumer demands.
Dr. Mohamed Sadok Massaoudi
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- power systems
- system stability and resilience
- smart grids
- federated learning
- grid optimization
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