找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
查看: 19044|回復(fù): 36
樓主
發(fā)表于 2025-3-21 17:11:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
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videohttp://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Titlebook: Deep Learning for Power System Applications; Case Studies Linking Fangxing Li,Yan Du Book 2024 The Editor(s) (if applicable) and The Author
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
doihttps://doi.org/10.1007/978-3-031-45357-1
isbn_softcover978-3-031-45359-5
isbn_ebook978-3-031-45357-1Series ISSN 2196-3185 Series E-ISSN 2196-3193
issn_series 2196-3185
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書(shū)目名稱(chēng)Deep Learning for Power System Applications影響因子(影響力)




書(shū)目名稱(chēng)Deep Learning for Power System Applications影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Deep Learning for Power System Applications網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Deep Learning for Power System Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Deep Learning for Power System Applications被引頻次




書(shū)目名稱(chēng)Deep Learning for Power System Applications被引頻次學(xué)科排名




書(shū)目名稱(chēng)Deep Learning for Power System Applications年度引用




書(shū)目名稱(chēng)Deep Learning for Power System Applications年度引用學(xué)科排名




書(shū)目名稱(chēng)Deep Learning for Power System Applications讀者反饋




書(shū)目名稱(chēng)Deep Learning for Power System Applications讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶(hù)組沒(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 20:41:35 | 只看該作者
Book 2024 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..
板凳
發(fā)表于 2025-3-22 01:08:20 | 只看該作者
Book 2024resentative 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
地板
發(fā)表于 2025-3-22 06:03:41 | 只看該作者
5#
發(fā)表于 2025-3-22 11:55:44 | 只看該作者
Desistance from Sexual Offendingpplying the proposed deep CNN and the DFS algorithm on standard test cases verify their accuracy and that their computational efficiency is thousands of times faster than the model-based traditional approach, which implies the great potential of the proposed algorithm for online applications.
6#
發(fā)表于 2025-3-22 16:23:03 | 只看該作者
Deep Neural Network for Microgrid Management,, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.
7#
發(fā)表于 2025-3-22 18:19:26 | 只看該作者
8#
發(fā)表于 2025-3-22 22:22:31 | 只看該作者
9#
發(fā)表于 2025-3-23 02:19:57 | 只看該作者
10#
發(fā)表于 2025-3-23 07:31:38 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-19 07:21
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
金川县| 成武县| 浦县| 东山县| 扶绥县| 上高县| 财经| 江安县| 永济市| 民权县| 哈巴河县| 巨野县| 怀化市| 富平县| 吴忠市| 靖宇县| 延寿县| 阿拉善左旗| 徐水县| 通化县| 天水市| 景泰县| 无棣县| 南昌市| 兴安盟| 石景山区| 濮阳县| 灵山县| 塘沽区| 嘉禾县| 南和县| 上林县| 涿州市| 诸暨市| 会东县| 龙胜| 鹰潭市| 南充市| 和林格尔县| 垫江县| 铜山县|