| Description |
1 online resource (xii, 101 pages) : illustrations (chiefly color) |
| Series |
Power electronics and power systems, 2196-3193 |
|
Power electronics and power systems (Springer) 2196-3193
|
| Contents |
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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed November 15, 2023) |
| Subject |
Electric power systems.
|
|
Artificial intelligence -- Engineering applications.
|
|
Deep learning (Machine learning)
|
|
Artificial intelligence -- Engineering applications
|
|
Deep learning (Machine learning)
|
|
Electric power systems
|
| Form |
Electronic book
|
| Author |
Du, Yang (Professor of electrical engineering), author
|
| ISBN |
9783031453571 |
|
3031453573 |
|
