Applications of Deep Machine Learning in Future Energy Systems

Khooban, Mohammad-hassan (Editor)

ISBN 10: 0443214328 / ISBN 13: 9780443214325

Verlag: Elsevier Science Ltd, 2024

Beschreibung

334 pages. 9.00x5.94x2.36 inches. In Stock. Bestandsnummer des Verkäufers __0443214328

Bibliografische Details

Titel: Applications of Deep Machine Learning in ...

Verlag: Elsevier Science Ltd

Erscheinungsdatum: 2024

Einband: Paperback

Zustand: Brand New

Über diesen Titel

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

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.