Physics-Aware Machine Learning for Integrated Energy Systems Management

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

Buch, Englisch, 458 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

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 Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• 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
Dr. Behnam Mohammadi-Ivatloo, PhD, is a Professor of sector coupling in energy systems at LUT University, Lappeenranta, Finland. He has a mix of high-level experience in research, teaching, administration and voluntary jobs at the national and international levels. He was PI or CO-PI in more than 20 externally funded research projects including grants from EU Horiozn and Business Finland. He is a Senior Member of IEEE since 2017 and a Member of the Governing Board of Iran Energy Association since 2013, where he was elected as President in 2019. He is Editor of IEEE Transactions on Power Systems and IEEE Transactions of Transportation Electrifications. His main areas of interest are integrated energy systems, sector coupling, renewable energies, energy storage systems, microgrids, and smart grids.

Daneshvar, Mohammadreza
Mohammadreza Daneshvar, PhD, is an Assistant Professor, founder and head of the Laboratory of Multi-Carrier Energy Networks Modernization at the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Prior to that, he was a postdoctoral research fellow in the field of modern multi-energy networks at the Smart Energy Systems Lab of the University of Tabriz for two years. He obtained his MSc and PhD degrees in Electrical Power Engineering from the University of Tabriz, all with honors. He has (co)authored more than 50 technical journal and conference articles, 10 books, 28 book chapters, and 10 national and international research projects in the field. Dr. Daneshvar is a member of the Editorial Board of the Energy and Built Environment Journal and the Early Career Editorial Board of the Sustainable Cities and Society Journal. He also served as the guest editor for the Sustainable Cities and Society, and Sustainable Energy Technologies and Assessments journals. Moreover, he serves as an active reviewer with more than 120 top journals, 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, Integrated Multi-Energy Systems, Grid Modernization, Electrical Energy Storage Systems, Sustainable Cities and Society, Microgrids, Energy Hubs, Machine Learning and Deep Learning, Digital Twin, and Optimization Techniques and AI.

Zare, Kazem
Dr. 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 a 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.