ISBN: 9783031453595 - deep learning for power system applications: case studies linking artificial intelligence and power systems (power electronics and power systems) (5 Ergebnisse)

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.…

Zustand: new. Questo è un articolo print on demand.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt 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. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.…