Artificial Intelligence Techniques for a Scalable Energy Transition: Advanced Methods, Digital Technologies, Decision Support Tools, and Applications - Softcover

9783030427283: Artificial Intelligence Techniques for a Scalable Energy Transition: Advanced Methods, Digital Technologies, Decision Support Tools, and Applications

Softcover

ISBN 10: 3030427285 ISBN 13: 9783030427283

Verlag: Springer, 2021

This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).

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�ber die Autorin bzw. den Autor

Moamar Sayed-Mouchaweh received his Master degree from the University of Technology of Compiegne-France in 1999. Then, he received his PhD degree from the University of Reims-France in December 2002. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research center in Sciences and Technology of the Information and the Communication (CReSTIC). In December 2008, he obtained the Habilitation to Direct Researches (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines “Ecole Nationale Sup�rieure des Mines de Douai” at the Department of Computer Science and Automatic Control (Informatique & Automatique). He edited the Springer book ‘Learning in Non-Stationary Environments: Methods and Applications ‘, in April 2012 and wrote two Brief Springer books in Electrical and Computer Engineering: ‘Discrete Event Systems: Diagnosis and Diagnosability’, and ‘Learning from Data Streams in Dynamic Environments’. He was a guest editor of several special issues of international journals. He was IPC Chair of the 12th IEEE International Conference on Machine Learning and Applications (ICMLA'13), the Conference Chair and IPC Chair of IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS2015), and the IPC Chair of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA'16). He is working as a member of the Editorial Board of Elsevier Journal “Applied Soft Computing” and Springer Journals “Evolving systems” and “Intelligent Industrial Systems”.

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<div>This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).<br></div><div><p></p><ul><li>Uses examples and applications to facilitate the understanding of AI techniques for scalable energy transitions</li><li>Includes examples, problems, and techniques in order to increase transparency and understanding of the methodological concepts</li><li>Dedicated to researchers, practitioners, and operators working with industrial systems</li></ul><p></p></div>

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VerlagSpringer
Erscheinungsdatum2021
ISBN 10 3030427285
ISBN 13 9783030427283
EinbandTapa blanda
SpracheEnglisch
Auflage1
Anzahl der Seiten392
HerausgeberSayed-Mouchaweh Moamar

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Artificial Intelligence Techniques for a Scalable Energy Transition : Advanced Methods, Digital Technologies, Decision Support Tools, and Applications

Moamar Sayed-Mouchaweh

Verlag: Springer International Publishing, 2021

ISBN 10: 3030427285 ISBN 13: 9783030427283

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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.). Artikel-Nr. 9783030427283

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