• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2023-11 (Vol.72 No.11)
  4. 10.5370/KIEE.2023.72.11.1586
XML PDF INFO REF

References

1 
J. Lee, C. Jung, S. Son, 2022, P2H Technical Potential Analysis of Korea for Renewable Energy Acceptance, The Proceedings of the Korean Institute of Electrical Engineers, pp. 187-188Google Search
2 
K. H. Chung, M. G. Park, S. B. Cho, K. S. Cho, 2014, Analysis on the Prerequisites to Deploying Virtual Power Plant (VPP) in Smart Grid Environment, The Proceedings of the Korean Institute of Electrical Engineers, pp. 493-494Google Search
3 
J Ryu, J Kim, 2021, Case Study of Overseas Virtual Power Plant Operation to activate VPP in Korea Electricity Market, The Proceedings of the Korean Energy Society, pp. 133-135Google Search
4 
G. An, Mar 2012, The importance and role of Energy Storage Systems, In The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers, Vol. 26, No. 2, pp. 13-17Google Search
5 
J. Lee, S. Lee, J. Kim, 2020, Optimal operation of virtual power plants using machine learning-based new and renewable energy prediction, pp. 81Google Search
6 
R. S. Sutton, A. G. Barto, 2018, Reinforcement Learning: An Introduction, 2nd ed, The MIT PressGoogle Search
7 
B. Recht, Robotics, A tour of reinforcement learning: The view from continuous control, Annual Review of ControlDOI
8 
M. Roderick, J. MacGlashan, S. Tellex, 2017, Implementing the Deep Q-Network, arXivGoogle Search
9 
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. V. D. Driessche, G. J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, Vol. 529, No. 7587, pp. 484-489Google Search
10 
J. Peters, J. A. Bagnell, 2016, Policy Gradient Methods. In: Sammut, C., Webb, G. (eds), Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA.Google Search
11 
J. Peters, 2008, Natural actor-critic, Neurocomputing, Vol. vol 71. no. 7-9, pp. 1180-1190DOI
12 
K. Kwon, H. Zhu, 2022, Reinforcement Learning-Based Optimal Battery Control Under Cycle-Based Degradation Cost, IEEE Transactions on Smart Grid, Vol. 13, No. 6, pp. 4909-4917DOI
13 
A. Pieter, A. Y. Ng., 2004, Apprenticeship learning via inverse reinforcement learning, In Proceedings of the twenty-first international conference on Machine learningDOI
14 
A. A. Markov, 2006, An Example of Statistical Investigation of the Text Eugene Onegin Concerining the Connection of Samples in Chains, Science in Context, Vol. 19, No. 4, pp. 591-600DOI
15 
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller, 2013, Playing Atari with Deep Reinforcement Learning, arXiv preprint arXivDOI
16 
L. Lin, 1992, Self-improving reactive agents based on reinforcement learning, planning and teaching, Machine Learning, Vol. 8, No. 3, pp. 293-321Google Search
17 
Ministry of Public Administration and Security [Online], Available: https://www.data.go.kr/data/15043275/fileData.doGoogle Search
18 
Data.gov [Online], Available: https://catalog.data.gov/dataset /?tags=energy-consumption.Google Search
19 
ERCOT Market Price [Online], Available: http://www.ercot.com/ mktinfo/pricesGoogle Search
20 
Kyung-bin Kwon, Su-Min Hong, Jae-Haeng Heo, Hosung Jung, 2022, Development of Reinforcement Learning-based Energy Management Agent for HVAC Facilities and ESS, The transactions of The Korean Institute of Electrical Engineers, Vol. 71, No. 10, pp. 1434-1440Google Search
21 
Keras. [Online] Available: https://github.com/fchollet/kerasGoogle Search