Physics-Aware Machine Learning for Integrated Energy Systems Management
Published on 21. August 2025
300 pages
978-0-443-32985-2 (ISBN)
Description
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.
- Outlines the challenges, opportunities, and applications for utilizing physics-aware machine learning to support renewable energy integration to the modern grid
- Covers a wide variety of techniques, from fundamental principles to security concerns
- Represents the latest offering in the cutting-edge series, Advances in Intelligent Energy Systems, which introduces these essential multidisciplinary skills to modern energy engineers
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Additional editions
Mohammadreza Daneshvar | Behnam Mohammadi-Ivatloo | Kazem Zare
Physics-Aware Machine Learning for Integrated Energy Systems Management
Book
08/2025
Elsevier
€170.50
Shipment within 15-20 days
Content
1. Introduction2. The Need for Integrated Energy Systems Management3. Attributes of Integrated Energy Systems in Modern Energy Grids4. Physical-economic Models for Integrated Energy Systems Management5. Decision-making Tools for the Optimal Operation and Planning of Integrated Energy Systems6. Energy Storage Systems for Integrated Energy Systems Management7. Applicability of Machine Learning Techniques in Managing Integrated Energy Systems8. Physics-aware Machine Learning for Integrated Energy Systems Management9. Physics-aware Machine Learning for Improving the Sustainability of Integrated Energy Systems10. Physics-aware Machine Learning for Cyber-security Assessment of Integrated Energy Systems Management11. Physics-aware Reinforcement Learning for Integrated Energy Systems Management12. Physics-aware Feature Learning for Integrated Energy Systems Management13. Physics-aware Neural Networks for Integrated Energy Systems Management14. Physics-aware Machine Learning for Integrated Energy Interaction Management
