{"product_id":"advances-in-digitalization-and-machine-learning-for-integrated-buildingtransportation-energy-systems-9780443131776","title":"Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eAdvancements in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems explores the combined impact of buildings and transportation systems on energy demand and use, focusing on AI and machine learning approaches. It covers topics such as renewable energy sources, hybrid energy storages, smart grids, and prosumer-based P2P energy trading, and concludes with discussions on blockchain technologies, IoT, and big data and cloud computing. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 01 November 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Elsevier - Health Sciences Division\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eAdvancements in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems delves into the intricate interplay between buildings and transportation systems, exploring their profound impact on energy demand and utilization. With a steadfast emphasis on leveraging artificial intelligence (AI) and machine learning techniques, the book comprehensively examines every aspect of the energy lifecycle, encompassing sources, grids, demand, storage, and usage. The journey commences with an introductory chapter on smart buildings and intelligent transportation systems, laying the foundation for AI and its transformative applications in renewable energy sources. It also highlights the latest technological breakthroughs in the field.\u003cbr\u003e\u003cbr\u003eSubsequent chapters delve into diverse topics such as the behavior of building occupants and the scheduling of vehicle trips, with a focus on predicting and analyzing demand. Hybrid energy storage systems in buildings are explored, leveraging AI to optimize energy utilization. The smart grid, characterized by energy digitalization, is examined, as are the prospects of prosumer-based peer-to-peer energy trading.\u003cbr\u003e\u003cbr\u003eThe book concludes with insightful discussions on blockchain technologies, the Internet of Things (IoT) in smart grid operation, and the immense potential of big data and cloud computing in integrated smart building-transportation energy systems. This comprehensive title serves as a valuable resource for students, researchers, and engineers seeking to gain a deep understanding, design, and implement flexible energy systems capable of meeting the escalating demand for electricity.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 450g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 229 x 151 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9780443131776\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Paperback \/ softback","offer_id":44770981773562,"sku":"9780443131776","price":138.38,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1699637648261_book.jpg?v=1699978930","url":"https:\/\/shulphink.com\/products\/advances-in-digitalization-and-machine-learning-for-integrated-buildingtransportation-energy-systems-9780443131776","provider":"Shulph Ink","version":"1.0","type":"link"}