Physics-Aware Machine Learning for Integrated Energy Systems Management: Advances in Intelligent Energy Systems
Editat de Mohammadreza Daneshvar, Behnam Mohammadi-Ivatloo, Kazem Zare, Jamshid Aghaeien Limba Engleză Paperback – aug 2025
Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, Physics-Aware Machine Learning for Integrated Energy Systems Management is a holistic introduction to an exciting new approach in energy systems management.
- Outlines the challenges, opportunities, and applications in utilising physics-aware machine learning to support renewable energy integration to the modern grid
- Covers a wide variety of techniques, from the 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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Specificații
ISBN-13: 9780443329845
ISBN-10: 0443329842
Pagini: 300
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Advances in Intelligent Energy Systems
ISBN-10: 0443329842
Pagini: 300
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Advances in Intelligent Energy Systems
Cuprins
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
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