東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Deep learning for power system applications = case studies linking artificial intelligence and power systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for power system applications/ by Fangxing Li, Yan Du.
其他題名:	case studies linking artificial intelligence and power systems /
作者:	Li, Fangxing.
其他作者:	Du, Yan.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xii, 101 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Electric power systems. -
電子資源:	https://doi.org/10.1007/978-3-031-45357-1
ISBN:	9783031453571

Deep learning for power system applications = case 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

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

649380
Electric power systems.

LC Class. No.: TK1005

Dewey Class. No.: 621.3104

Deep learning for power system applications = case studies linking artificial intelligence and power systems /
LDR:02437nmm a2200337 a 4500 001 2367748
003 DE-He213
005 20231110151825.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031453571 $q (electronic bk.)
020 $a 9783031453564 $q (paper)
024 7 $a 10.1007/978-3-031-45357-1 $2 doi
035 $a 978-3-031-45357-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1005
072 7 $a THR $2 bicssc
072 7 $a TEC031000 $2 bisacsh
072 7 $a THR $2 thema
082 0 4 $a 621.3104 $2 23
090 $a TK1005 $b .L693 2024
100 1 $a Li, Fangxing. $3 3711144
245 1 0 $a Deep learning for power system applications $h [electronic resource] : $b case studies linking artificial intelligence and power systems / $c by Fangxing Li, Yan Du.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xii, 101 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Power electronics and power systems, $x 2196-3193
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.
650 0 $a Electric power systems. $3 649380
650 0 $a Artificial intelligence $x Engineering applications. $3 1567162
650 1 4 $a Electrical Power Engineering. $3 3592498
650 2 4 $a Energy Grids and Networks. $3 3592502
650 2 4 $a Energy Policy, Economics and Management. $3 1532761
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Du, Yan. $3 1277363
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Power electronics and power systems. $3 2071809
856 4 0 $u https://doi.org/10.1007/978-3-031-45357-1
950 $a Energy (SpringerNature-40367)