CLEB: A Continual Learning Energy Bidding Framework For An Energy Market Bidding Application-Reference-Cited by-同舟云学术

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CLEB: A Continual Learning Energy Bidding Framework For An Energy Market Bidding Application

Published:2023-12-16 Issue: Volume: Page:46-53
ISSN:
Container-title:2023 6th Artificial Intelligence and Cloud Computing Conference (AICCC)
language:
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Author:

Nguyen Tri D.T.¹^ORCID,Hua Cam-Hao¹^ORCID,Rehman Ubaid Ur¹^ORCID,Hussain Musarrat¹^ORCID,Lee Sungyoung¹^ORCID

Affiliation:

1. Kyung Hee University (Global Campus), Republic of Korea

Funder

Institute of Information & communications Technology Planning & Evaluation

Grand Information Technology Research Center

ITRC(Information Technology Research Center)

Institute of Information & communications Technology Planning & Evaluation (IITP)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3639592.3639599

Reference23 articles.

1. Zhang X. Zhu S. He J. Yang B. and Guan X. 2019. Credit rating based real-time energy trading in microgrids. Applied energy 236 pp.985-996.

2. ENTRUST: Energy trading under uncertainty in smart grid systems

3. Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response

4. Noor S. Yang W. Guo M. van Dam K.H. and Wang X. 2018. Energy demand side management within micro-grid networks enhanced by blockchain. Applied energy 228 pp.1385-1398.

5. A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing

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