TY  - BOOK
AU  - Li,Fangxing
AU  - Du,Yan
ED  - SpringerLink (Online service)
TI  - Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems 
T2  - Power Electronics and Power Systems,
SN  - 9783031453571
AV  - TK1001-1841 
PY  - 2024///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Electric power production
KW  - Electric power distribution
KW  - Energy policy
KW  - Energy and state
KW  - Artificial intelligence
KW  - Machine learning
KW  - Electrical Power Engineering
KW  - Energy Grids and Networks
KW  - Energy Policy, Economics and Management
KW  - Artificial Intelligence
KW  - Machine Learning
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-45357-1
ER  -