Physics-Aware Machine Learning for Integrated Energy Systems Management

Erscheinungsjahr 2025
ISBN: 978-0-443-32984-5
Verlag: Elsevier Science

Buch, Englisch, 300 Seiten, Format (B × H): 152 mm x 229 mm

Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.

Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management.

Autoren/Hrsg.

Daneshvar, Mohammadreza

Mohammadi-Ivatloo, Behnam

Zare, Kazem

Aghaei, Jamshid

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze

Inhaltsverzeichnis

1. Introduction
2. The Need for Integrated Energy Systems Management
3. Attributes of Integrated Energy Systems in Modern Energy Grids
4. Physical-economic Models for Integrated Energy Systems Management
5. Decision-making Tools for the Optimal Operation and Planning of Integrated Energy Systems
6. Energy Storage Systems for Integrated Energy Systems Management
7. Applicability of Machine Learning Techniques in Managing Integrated Energy Systems
8. Physics-aware Machine Learning for Integrated Energy Systems Management
9. Physics-aware Machine Learning for Improving the Sustainability of Integrated Energy Systems
10. Physics-aware Machine Learning for Cyber-security Assessment of Integrated Energy Systems Management
11. Physics-aware Reinforcement Learning for Integrated Energy Systems Management
12. Physics-aware Feature Learning for Integrated Energy Systems Management
13. Physics-aware Neural Networks for Integrated Energy Systems Management
14. Physics-aware Machine Learning for Integrated Energy Interaction Management

Über Autor(innen)

Mohammadi-Ivatloo, Behnam
Behnam Mohammadi-Ivatloo, Ph.D., is a Professor with the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. He was previously a Senior Research Fellow at Aalborg University, Denmark. Before joining the University of Tabriz, he was a research associate at the Institute for Sustainable Energy, Environment and Economy at the University of Calgary. He obtained MSc and Ph.D. degrees in electrical engineering from the Sharif University of Technology. His main research interests are renewable energies, microgrid systems, and smart grids. He has authored and co-authored more than 200 technical publications in his domain of interest, more than 30 book chapters, and 10 books.

Zare, Kazem
Kazem Zare PhD, SMIEEE received the B.Sc. and M.Sc. degrees in electrical engineering from University of Tabriz, Tabriz, Iran, in 2000 and 2003, respectively, and Ph.D. degree from Tarbiat Modares University, Tehran, Iran, in 2009. Currently, he is an Associate Professor of the Faculty of Electrical and Computer Engineering, University of Tabriz. His research areas include distribution networks operation and planning, power system economics, microgrid and energy management.

Daneshvar, Mohammadreza
Mohammadreza Daneshvar is a Research Associate with the Smart Energy Systems Lab in the Department of Electrical and Computer Engineering at the University of Tabriz. He is the editor of more than 40 journal and conference papers in the field of multi-energy systems, grid modernization, transactive energy, and optimizing the multi-carrier energy grids. He is the author and editor of three books with Springer, Elsevier, and Wiley-IEEE. He serves as an active reviewer with IEEE, Elsevier, Springer, Wiley, Taylor & Francis, and IOS Press, and was ranked among the top 1% of reviewers in Engineering and Cross-Field based on Publons global reviewer database. His research interests include smart grids, transactive energy, energy management, renewable energy sources, multi-carrier energy systems, grid modernization, electrical energy storage systems, microgrids, energy hubs, machine learning and deep learning, blockchain technology, and optimization techniques.