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.

1. Introduction to smart buildings and intelligent transportations with Artificial Intelligent and Digitalization Technology2. ML and AI Distributed Renewable Energy sources, technologies, perspectives, and challenges3. Building occupants' behavior and vehicle driving schedule with demand prediction and analysis4. Spatiotemporal energy sharing with new energy vehicles and Human-Machine Interaction5. Integrated Energy Flexible Building and E-Mobility with Demand-side management and model predictive control6. Electrification and hydrogenation in integrated building-transportation systems for sustainability7. Hybrid Energy storages in buildings with AI8. Smart grid with energy digitalization9. AI-powered 'source-grid-load-storage' optimization in multi-energy systems10. Prosumer-based P2P energy trading with advanced energy pricing mechanism11. Blockchain technologies for secure and tamper-proof in energy trading12. Energy Resilience, Robustness and Reliability in smart district energy systems13. Application of internet of energy things and digitalization in smart grid operation14. Application of Big Data and cloud computing for Integrated Smart Building-Transportation Energy Systems development15. Social and economic analysis of integrated Building-Transportation Energy Systems