Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage.
Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems.
A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity.
1. Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. List of Contributors - p. xiii 8. Foreword - p. xv 9. Preface - p. xvii 10. Acknowledgments - p. xix 11. Chapter 1 Smart buildings and intelligent transportations with artificial intelligence and digitalization technology - p. 1 12. Chapter 2 Machine learning and artificial intelligence-distributed renewable energy sources: technologies, perspectives, and challenges - p. 17 13. Chapter 3 Building occupant behavior and vehicle driving schedules with demand prediction and analysis - p. 31 14. Chapter 4 Integrated energy flexible building and e-mobility with demand-side management and model predictive control - p. 45 15. Chapter 5 Electrification and hydrogenation in integrated building-transportation systems for sustainability - p. 63 16. Chapter 6 Hybrid energy storages in buildings with artificial intelligence - p. 91 17. Chapter 7 Smart grid with energy digitalization - p. 115 18. Chapter 8 Peer-to-peer energy trading with advanced pricing and decision-making mechanisms - p. 133 19. Chapter 9 Blockchain technologies for automatic, secure, and tamper-proof energy trading - p. 159 20. Chapter 10 Energy economy and robustness with mobile energy storage systems - p. 171 21. Chapter 11 Application of Internet of Energy and digitalization in smart grid and sustainability - p. 211 22. Chapter 12 Application of big data and cloud computing for the development of integrated smart building transportation energy systems - p. 223 23. Chapter 13 Social and economic analysis of integrated building transportation energy system - p. 239 24. Index - p. 265
Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage.
Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems.
A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity.
1. Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. List of Contributors - p. xiii 8. Foreword - p. xv 9. Preface - p. xvii 10. Acknowledgments - p. xix 11. Chapter 1 Smart buildings and intelligent transportations with artificial intelligence and digitalization technology - p. 1 12. Chapter 2 Machine learning and artificial intelligence-distributed renewable energy sources: technologies, perspectives, and challenges - p. 17 13. Chapter 3 Building occupant behavior and vehicle driving schedules with demand prediction and analysis - p. 31 14. Chapter 4 Integrated energy flexible building and e-mobility with demand-side management and model predictive control - p. 45 15. Chapter 5 Electrification and hydrogenation in integrated building-transportation systems for sustainability - p. 63 16. Chapter 6 Hybrid energy storages in buildings with artificial intelligence - p. 91 17. Chapter 7 Smart grid with energy digitalization - p. 115 18. Chapter 8 Peer-to-peer energy trading with advanced pricing and decision-making mechanisms - p. 133 19. Chapter 9 Blockchain technologies for automatic, secure, and tamper-proof energy trading - p. 159 20. Chapter 10 Energy economy and robustness with mobile energy storage systems - p. 171 21. Chapter 11 Application of Internet of Energy and digitalization in smart grid and sustainability - p. 211 22. Chapter 12 Application of big data and cloud computing for the development of integrated smart building transportation energy systems - p. 223 23. Chapter 13 Social and economic analysis of integrated building transportation energy system - p. 239 24. Index - p. 265
