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Añadir al carritoCondición: New. pp. 116 1st ed. 2024 edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Springer International Publishing, 2023
ISBN 10: 3031453565 ISBN 13: 9783031453564
Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.
Idioma: Inglés
Publicado por Springer International Publishing, 2023
ISBN 10: 3031453565 ISBN 13: 9783031453564
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Añadir al carritoBuch. Condición: 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.
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Idioma: Inglés
Publicado por Springer International Publishing Nov 2023, 2023
ISBN 10: 3031453565 ISBN 13: 9783031453564
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 117,69
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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.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. 116 pp. Englisch.
Idioma: Inglés
Publicado por Springer, Springer International Publishing Nov 2023, 2023
ISBN 10: 3031453565 ISBN 13: 9783031453564
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 117,69
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Añadir al carritoBuch. Condición: 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.