| 書(shū)目名稱(chēng) | Deep Learning for Power System Applications | | 副標(biāo)題 | Case Studies Linking | | 編輯 | Fangxing Li,Yan Du | | 視頻video | http://file.papertrans.cn/265/264613/264613.mp4 | | 概述 | Provides a history of AI in power grid operation and planning.Introduces the CNN, DNN, and DRL algorithms and applications in power systems.Includes several representative case studies | | 叢書(shū)名稱(chēng) | Power Electronics and Power Systems | | 圖書(shū)封面 |  | | 描述 | 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.. | | 出版日期 | Book 2024 | | 關(guān)鍵詞 | Deep learning; Deep neural network; Convolutional neural network; Deep reinforcement learning; Deep dete | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-45357-1 | | isbn_softcover | 978-3-031-45359-5 | | isbn_ebook | 978-3-031-45357-1Series ISSN 2196-3185 Series E-ISSN 2196-3193 | | issn_series | 2196-3185 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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