Topic Editors

Department of Power Engineering, Faculty of Electrical Engineering, Gheorghe Asachi Technical University of Iași, 700050 Iasi, Romania
Department of Energy “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

Mathematical Fundamentals of Machine Learning in Power Systems

Abstract submission deadline
31 March 2028
Manuscript submission deadline
31 May 2028
Viewed by
312

Topic Information

Dear Colleagues,

Machine learning is increasingly employed in power systems to address complex challenges related to optimization, control, forecasting, and decision-making. However, the reliable deployment of data-driven methods in electrical power systems requires a solid understanding of their underlying mathematical principles, particularly in relation to system constraints, physical consistency, and operational robustness.

This Topic focuses on the mathematical fundamentals of machine learning in power systems, emphasizing models and algorithms grounded in optimization theory, statistical learning, and numerical analysis. Contributions are encouraged that investigate the mathematical structure, stability, convergence, and interpretability of machine learning techniques applied to power generation, transmission, and distribution systems.

Relevant applications include smart grids, renewable energy integration, electric vehicle coordination, voltage and loss optimization, demand response, and energy management systems. Particular attention is given to hybrid approaches that combine physical modeling with data-driven learning, as well as to explainable and uncertainty-aware machine learning methods. By bringing together researchers from applied mathematics, machine learning, and power engineering, this Topic aims to advance trustworthy, transparent, and mathematically sound machine learning solutions for modern power systems.

Dr. Bogdan Neagu
Dr. Andrea Mazza
Topic Editors

Keywords

  • machine learning
  • power systems
  • mathematical modeling
  • optimization theory
  • numerical methods
  • statistical learning
  • hybrid modeling

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
2.1 4.5 2008 19.2 Days CHF 1800 Submit
Applied Sciences
applsci
2.5 5.5 2011 16 Days CHF 2400 Submit
Axioms
axioms
1.6 - 2012 21.7 Days CHF 2400 Submit
Big Data and Cognitive Computing
BDCC
4.4 9.8 2017 23.1 Days CHF 1800 Submit
Encyclopedia
encyclopedia
- - 2021 26.8 Days CHF 1200 Submit
Mathematics
mathematics
2.2 4.6 2013 17.3 Days CHF 2600 Submit
Sci
sci
- 5.2 2019 26.7 Days CHF 1400 Submit
Symmetry
symmetry
2.2 5.3 2009 15.8 Days CHF 2400 Submit

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Published Papers

This Topic is now open for submission.
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