Yuekuan Zhou - Böcker

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 lifecycle, 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. 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.

Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactionsDiscusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable developmentHighlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models

Latent Heat Storage for Energy-Efficient Buildings and Zero-Carbon Communities is an up-to-date reference guide on the rapid progress the field is experiencing while keeping up with much-required transformative initiatives to improve global buildings' energy performance. To lay the foundations, the book covers the fundamentals of phase-change materials, including their thermo-physical properties, nanoscale thermal transport mechanisms, encapsulation techniques, and heat transfer enhancement methods. It goes on to explore the integration of latent heat storage solutions in building envelopes or solar photovoltaics and heating/cooling systems, using mathematical models and performance analysis to discuss thermal management aspects for net-zero energy buildings and zero-carbon community concepts. The volume then delves into practical application of the most advanced, artificial intelligence-powered tools for system modeling and simulation, demand-side management control strategies, and metaheuristic optimization approaches. It concludes with an overview of technical challenges and emerging research areas which evidence academic and industry communities’ persistent interest in the future directions of topics interconnected with the sustainable development of the built environment.Provides comprehensive coverage of latent heat storage from an interdisciplinary perspective, making the content relevant to readers from diverse backgroundsOffers real-world insights, case studies, and implementation strategies, providing valuable, actionable recommendationsIncludes targets for energy-saving and decarbonization within each content section

Advanced Energy Management: Digitalization and AI for the Internet of Energy explores recent advancements in distributed renewable systems, advanced controls, and energy management of nonlinear energy behaviors. The book addresses energy resilience amid extreme climates and events, presenting new applications for energy-efficient, low-carbon, and reliable cities. It details the role of AI in renewable energy systems, including power dispatch, fast response, dynamic aging, and techno-economic performance. This introduction provides a comprehensive overview of AI applications in renewable energy systems. The book delves into the interconnection between climate change and multi-energy systems, big data's role in sustainable energy supply, and much more.It also covers demand-side management, grid-response controls in Integrated Energy Management Systems (IEMSs), and Energy Management Systems (EMSs) in integrated energy systems and power grids with AI. Additionally, the book reviews peer-to-peer and blockchain-based energy sharing, dynamic pricing, decision-making in distributed energy markets, city-scale energy resilience, and robustness with distributed energy systems. Ethical, regulatory, and policy considerations of AI in energy management and detailed Sustainability and Environmental Impact Analyses are discussed.Presents the latest developments and future trends in advanced energy managementExplains the fundamentals of how AI can facilitate a low-carbon transition through sustainable energy and storageIncludes case studies and real-world applications of advanced energy management with AI and digitalization