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Titlebook: Deep Learning for Power System Applications; Case Studies Linking Fangxing Li,Yan Du Book 2024 The Editor(s) (if applicable) and The Author

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樓主: cerebellum
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發(fā)表于 2025-3-23 09:47:12 | 只看該作者
Desistance from Sexual Offendingons in the area of power systems are also discussed to provide the readers with a general perception of the potential of deep learning in solving complicated real-world problems, both theoretically and practically.
12#
發(fā)表于 2025-3-23 17:45:47 | 只看該作者
Palgrave Studies in Risk, Crime and Societyforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system, and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator (DSO), the target is to decrease t
13#
發(fā)表于 2025-3-23 21:26:39 | 只看該作者
Desistance from Sexual Offendingosed method is a combination of a deep convolutional neural network (CNN) and a depth-first search (DFS) algorithm. First, deep CNN is constructed as a security assessment tool to evaluate the system security status based on observable information. Second, a scenario tree is built to represent the p
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發(fā)表于 2025-3-24 00:18:46 | 只看該作者
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發(fā)表于 2025-3-24 06:11:34 | 只看該作者
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發(fā)表于 2025-3-24 06:35:21 | 只看該作者
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發(fā)表于 2025-3-24 14:06:36 | 只看該作者
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發(fā)表于 2025-3-24 18:32:55 | 只看該作者
Summary and Future Works,This chapter gives a brief summary of the research works from Chaps. ., ., . and also discusses the potential future directions for applying deep learning in the field of power systems, including the most up-to-date deep learning techniques such as physics-informed deep learning, transfer learning, and meta-learning.
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發(fā)表于 2025-3-24 21:11:07 | 只看該作者
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發(fā)表于 2025-3-25 01:45:23 | 只看該作者
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