Li fangxing (8 Ergebnisse)

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.Provides a history of AI in power grid operation and planning;Introduces deep learning algorithms and applications in power systems;Includes several representative case studies.…

Taschenbuch. Zustand: Neu. Deep Learning for Power System Applications | Case Studies Linking Artificial Intelligence and Power Systems | Fangxing Li (u. a.) | Taschenbuch | Power Electronics and Power Systems | xii | Englisch | 2024 | Springer | EAN 9783031453595 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Hardcover. Zustand: Brand New. 113 pages. 9.26x6.10x0.31 inches. In Stock.

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Zustand: New. Breaks down novel methodologies and tools from deep learning to generative adversarial networksSupports readers in implementing practical steps towards resilient renewable energyPresents practical guidance for readers on the challe.

Taschenbuch. Zustand: Neu. Neuware - Enhancing Resilience in Power Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks.Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future.…