Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems

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.

General

Imprint:	Elsevier - Health Sciences Division
Country of origin:	United States
Release date:	November 2023
First published:	2024
Editors:	Yuekuan Zhou • Jinglei Yang • Guo-Qiang Zhang • Peter D. Lund
Dimensions:	229 x 151mm (L x W)
ISBN-13:	978-0-443-13177-6
Categories:	Books
LSN:	0-443-13177-5
Barcode:	9780443131776

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