03698nam a22006135i 4500 978-3-031-45357-1 DE-He213 20251030110831.0 cr nn 008mamaa 231110s2024 sz | s |||| 0|eng d 9783031453571 978-3-031-45357-1 10.1007/978-3-031-45357-1 doi TK1001-1841 THR bicssc TEC031020 bisacsh THR thema Li, Fangxing. author. aut http://id.loc.gov/vocabulary/relators/aut 67120 Deep Learning for Power System Applications [electronic resource] : Case Studies Linking Artificial Intelligence and Power Systems / by Fangxing Li, Yan Du. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XII, 101 p. 32 illus., 30 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Power Electronics and Power Systems, 2196-3193 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. Electric power production. 65675 Electric power distribution. 66068 Energy policy. 61992 Energy and state. 66460 Artificial intelligence. 58441 Machine learning. 55059 Electrical Power Engineering. 65867 Energy Grids and Networks. 66069 Energy Policy, Economics and Management. 66461 Artificial Intelligence. 58441 Machine Learning. 55059 Du, Yan. author. aut http://id.loc.gov/vocabulary/relators/aut 67121 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031453564 Printed edition: 9783031453588 Printed edition: 9783031453595 Power Electronics and Power Systems, 2196-3193 67122 https://doi.org/10.1007/978-3-031-45357-1 ZDB-2-ENE ZDB-2-SXEN 56532 56532