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Deep learning for power system applicationscase studies linking artificial intelligence and power systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for power system applicationsby Fangxing Li, Yan Du.
Reminder of title:	case studies linking artificial intelligence and power systems /
Author:	Li, Fangxing.
other author:	Du, Yan.
Published:	Cham :Springer International Publishing :2024.
Description:	xii, 101 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power systems.
Online resource:	https://doi.org/10.1007/978-3-031-45357-1
ISBN:	9783031453571$q(electronic bk.)

Deep learning for power system applicationscase studies linking artificial intelligence and power systems /
Li, Fangxing.

Deep learning for power system applicationscase studies linking artificial intelligence and power systems /[electronic resource] :by Fangxing Li, Yan Du. - Cham :Springer International Publishing :2024. - xii, 101 p. :ill. (chiefly col.), digital ;24 cm. - Power electronics and power systems,2196-3193. - Power electronics and power systems..

Introduction-A Brief History of Deep Learning and Its Applications in Power Systems -- Deep Neural Network for Microgrid Management -- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening -- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning -- Summary and Future Works.

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.

ISBN: 9783031453571$q(electronic bk.)

Standard No.: 10.1007/978-3-031-45357-1doiSubjects--Topical Terms:

182985
Electric power systems.

LC Class. No.: TK1005

Dewey Class. No.: 621.3104

Deep learning for power system applicationscase studies linking artificial intelligence and power systems /
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505 0 $a Introduction-A Brief History of Deep Learning and Its Applications in Power Systems -- Deep Neural Network for Microgrid Management -- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening -- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning -- Summary and Future Works.
520 $a 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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