The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Review on the research and practice of deep learning and reinforcement learning in smart grids
Open accessAbstract
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.
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Cites
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Cited by
Deep reinforcement learning for cyber security
Nguyen, T. T., & Reddi, V. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3121870 · Cited by 620
A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid
Alazab, M., Khan, S., Krishnan, S. S. R., Pham, Q.-V., Reddy, M. P. K., & Gadekallu, T. R. (2020) · IEEE Access · doi:10.1109/access.2020.2991067 · Cited by 259
Machine Learning in IoT Security: Current Solutions and Future Challenges
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986444 · Cited by 129
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110
Ali, Z., Terriche, Y., Hoang, L. Q. N., Abbas, S. Z., Hassan, M. A., Sadiq, M., Su, C.-L., & Guerrero, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3112383 · Cited by 104
Metadata
- Authors
- China Electric Power Research Institute, Zhang, D., Han, X., Taiyuan University of Technology, Deng, C., & China Electric Power Research Institute
- Venue
- CSEE Journal of Power and Energy Systems
- Publisher
- Power System Technology Press
- Published
- 2018-09-24
- Volume
- 4
- Issue
- 3
- Pages
- 362-370
- ISSN
- 2096-0042 (ISSN-L)
- OpenAlex
- W2892841407
- Cited by
- 561
- References
- 34
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- diamond
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- China Electric Power Research Institute, Zhang, D., Han, X., Taiyuan University of Technology, Deng, C., & China Electric Power Research Institute. (2018). Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE Journal of Power and Energy Systems. https://doi.org/10.17775/cseejpes.2018.00520
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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