Review on the research and practice of deep learning and reinforcement learning in smart grids
Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling techn...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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China electric power research institute
2018-09-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Online Access: | https://ieeexplore.ieee.org/document/8468674 |
| _version_ | 1811270389547925504 |
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| author | Dongxia Zhang Xiaoqing Han Chunyu Deng |
| author_facet | Dongxia Zhang Xiaoqing Han Chunyu Deng |
| author_sort | Dongxia Zhang |
| collection | DOAJ |
| description | Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids. |
| first_indexed | 2024-04-12T21:59:54Z |
| format | Article |
| id | doaj.art-c7d357ca671049218f4173399647fb5f |
| institution | Directory Open Access Journal |
| issn | 2096-0042 2096-0042 |
| language | English |
| last_indexed | 2024-04-12T21:59:54Z |
| publishDate | 2018-09-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj.art-c7d357ca671049218f4173399647fb5f2022-12-22T03:15:10ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422096-00422018-09-014336237010.17775/CSEEJPES.2018.00520Review on the research and practice of deep learning and reinforcement learning in smart gridsDongxia Zhang0Xiaoqing Han1Chunyu Deng2China Electric Power Research Institute, Beijing 100192, ChinaDepartment of Electrical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaSmart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.https://ieeexplore.ieee.org/document/8468674 |
| spellingShingle | Dongxia Zhang Xiaoqing Han Chunyu Deng Review on the research and practice of deep learning and reinforcement learning in smart grids CSEE Journal of Power and Energy Systems |
| title | Review on the research and practice of deep learning and reinforcement learning in smart grids |
| title_full | Review on the research and practice of deep learning and reinforcement learning in smart grids |
| title_fullStr | Review on the research and practice of deep learning and reinforcement learning in smart grids |
| title_full_unstemmed | Review on the research and practice of deep learning and reinforcement learning in smart grids |
| title_short | Review on the research and practice of deep learning and reinforcement learning in smart grids |
| title_sort | review on the research and practice of deep learning and reinforcement learning in smart grids |
| url | https://ieeexplore.ieee.org/document/8468674 |
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