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Applications of Deep Machine Learning in Future Energy Systems by Mohammad-Hassan Khooban (Department of Engineering - Cyper-Physical Systems, Aarhus University, Aarhus N, Denmark) at Abbey's Bookshop,
Applications of Deep Machine Learning in Future Energy Systems
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Mohammad-Hassan Khooban (Department of Engineering - Cyper-Physical Systems, Aarhus University, Aarhus N, Denmark)

Applications of Deep Machine Learning in Future Energy Systems

Mohammad-Hassan Khooban (Department of Engineering - Cyper-Physical Systems, Aarhus University, Aarhus N, Denmark)

$326.95

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English
Elsevier - Health Sciences Division
21 August 2024
Power generation & distribution; Machine learning
Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply.

An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy.
Edited by:  
Imprint:   Elsevier - Health Sciences Division
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 152mm, 
Weight:   450g
ISBN:   9780443214325
ISBN 10:   0443214328
Pages:   334
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

Dr. Mohammad-Hassan Khooban is an Assistant Professor in the Department of Engineering and the Director of the Power Circuits and Systems Research Group at Aarhus University in Denmark. He has authored or co-authored more than 220 publications in peer-reviewed journals (mostly IEEE) and international conferences, written three book chapters, and holds one patent. He has been involved in six national and international projects. He was identified in 2019, 2020, and 2021 by Stanford University as one of the world’s top 2% researchers in engineering. He was also ranked 16th in the list of top 30 Electronics and Electrical Engineering Scientists in Denmark in 2022. His research interests include the application of advanced control, and optimization of artificial intelligence-inspired techniques in power electronics and systems.

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