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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Chapter 1. Attributes of integrated energy systems in modern energy grids - p. 1 8. Chapter 2. Physical-economic models for integrated energy systems management - p. 27 9. Chapter 3. Decision-making tools for optimal operation and planning of integrated energy systems - p. 53 10. Chapter 4. Energy storage in integrated energy systems: an efficient way to overcome the flexibility deficiency - p. 79 11. Chapter 5. Towards efficient energy management using intelligent prediction models: a comparative study on energy consumption data - p. 105 12. Chapter 6. Application of machine learning techniques in managing integrated energy systems - p. 125 13. Chapter 7. Physics-aware machine learning for energy management of integrated energy systems - p. 145 14. Chapter 8. Machine learning-based methods for carbon emissions management in integrated energy systems - p. 169 15. Chapter 9. Physics-aware machine learning for sustainable development of integrated energy systems - p. 189 16. Chapter 10. Physics-informed neural networks for optimal integration of energy sources in future sustainable power systems - p. 211 17. Chapter 11. Data-driven energy scheduling of interconnected local energy networks flexibility: introducing an XGBoost-assisted cyber-security method - p. 241 18. Chapter 12. Physics-aware reinforcement learning for integrated energy systems management - p. 275 19. Chapter 13. Model-free reinforcement learning for integrated energy control of hybrid road vehicles - p. 299 20. Chapter 14. Physics-aware feature learning for integrated energy systems management - p. 333 21. Chapter 15. Physics-aware neural networks for integrated energy systems management - p. 381 22. Index - p. 421
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Chapter 1. Attributes of integrated energy systems in modern energy grids - p. 1 8. Chapter 2. Physical-economic models for integrated energy systems management - p. 27 9. Chapter 3. Decision-making tools for optimal operation and planning of integrated energy systems - p. 53 10. Chapter 4. Energy storage in integrated energy systems: an efficient way to overcome the flexibility deficiency - p. 79 11. Chapter 5. Towards efficient energy management using intelligent prediction models: a comparative study on energy consumption data - p. 105 12. Chapter 6. Application of machine learning techniques in managing integrated energy systems - p. 125 13. Chapter 7. Physics-aware machine learning for energy management of integrated energy systems - p. 145 14. Chapter 8. Machine learning-based methods for carbon emissions management in integrated energy systems - p. 169 15. Chapter 9. Physics-aware machine learning for sustainable development of integrated energy systems - p. 189 16. Chapter 10. Physics-informed neural networks for optimal integration of energy sources in future sustainable power systems - p. 211 17. Chapter 11. Data-driven energy scheduling of interconnected local energy networks flexibility: introducing an XGBoost-assisted cyber-security method - p. 241 18. Chapter 12. Physics-aware reinforcement learning for integrated energy systems management - p. 275 19. Chapter 13. Model-free reinforcement learning for integrated energy control of hybrid road vehicles - p. 299 20. Chapter 14. Physics-aware feature learning for integrated energy systems management - p. 333 21. Chapter 15. Physics-aware neural networks for integrated energy systems management - p. 381 22. Index - p. 421
