Reliable Non-Parametric Techniques for Energy System Operation and Control - Couverture souple

Zhang

Couverture souple

ISBN 10 : 0443364923 ISBN 13 : 9780443364921

Editeur : Elsevier, 2025

Synopsis

Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods, a new Volume in the Advances in Intelligent Energy Systems, is a comprehensive guide to modern smart methods in energy system operation and control. This book covers fundamental concepts and applications in both deterministic and uncertain environments. It addresses the challenge of accuracy in imbalanced datasets and the limitations of measurements. The book delves into advanced topics such as safe reinforcement learning for energy system control, including training-efficient intrinsic-motivated reinforcement learning, and physical layer-based control, and more.

Other chapters cover barrier function-based control and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, this book stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.

A propos du livre

À propos des auteurs

Hongcai Zhang is currently an Assistant Professor with the State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering at the University of Macau, China. Prior to this, he was a postdoctoral scholar with the University of California, USA, from 2018-2019. His current research interests include Internet of Things for smart energy, optimal operation and optimization of power and transportation systems, and grid integration of distributed energy resources. He has published over 70 JCR Q1/Q2 journal papers with 3 identified as ESI highly cited papers, and is an Associate Editor for IEEE Transactions on Power Systems and the Journal of Modern Power Systems and Clean Energy.

Yonghua Song is currently a Chair Professor and the Director of the State Key Laboratory in the Internet of Things for Smart City, both at the University of Macau, China. He is also the Vice President of Chinese Electrotechnical Society, an international member of Academia Europaea, a fellow of the Royal Academy of Engineering (UK), and an IEEE fellow. He has long been engaged in developing renewable electrical power systems and smart energy research. He has published over 200 scientific journal papers and authored/edited 10 books. He has won the second prize of the State Scientific and Technological Progress Award and the prize for Scientific and Technological Progress from the Ho Leung Ho Lee Foundation, China.

Ge Chen is currently a Postdoctoral Research Associate with Purdue University, USA. His research interests include the Internet of Things for smart energy, optimal operation, and data-driven optimization under uncertainty.

Peipei Yu is currently a Ph.D. candidate in electrical and computer engineering at the University of Macau, China. Her research interests include learning-based control, ancillary services for demand response, and integrated energy systems. She has published 7 JCR Q1/Q2 journal papers.

Détails de l'édition

• Éditeur: Elsevier
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 0443364923
• ISBN 13: 9780443364921
• Reliure: Broché
• Nombre de pages: 310
• Coordonnées du fabricant: ELSEVIER; https://www.elsevier.com/es-es; ; Mooswaldallee 6; Freiburg; 79108; Allemagne