Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 홍승호 | - |
dc.date.accessioned | 2020-01-10T02:24:17Z | - |
dc.date.available | 2020-01-10T02:24:17Z | - |
dc.date.issued | 2017-12 | - |
dc.identifier.citation | 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Page. 1822-1823 | en_US |
dc.identifier.isbn | 978-1-5386-2652-8 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/8561092 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/121656 | - |
dc.description.abstract | This paper proposes a price-based demand response(DR) algorithm for energy management in a hierarchical electricity market. The pricing problem is formulated as a reinforcement learning (RL) model. Using RL, the service provider (SP) can adaptively decide the retail electricity price during the on-line learning process. | en_US |
dc.description.sponsorship | This work was supported by the Gyeonggi Regional Research Center (GRRC) program of Gyeonggi Province under Grant GRRC Hanyang 2017-B02 (Development of Industrial Communication-IOT Gateway). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Demand response | en_US |
dc.subject | reinforcement learning | en_US |
dc.subject | smart grid | en_US |
dc.title | A Perspective on Reinforcement Learning in Price-based Demand Response for Smart Grid | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/CSCI.2017.327 | - |
dc.relation.page | 1-2 | - |
dc.contributor.googleauthor | Lu, Renzhi | - |
dc.contributor.googleauthor | Hong, Seung Ho | - |
dc.contributor.googleauthor | Zhang, Xiongfeng | - |
dc.contributor.googleauthor | Ye, Xun | - |
dc.contributor.googleauthor | Song, Won Seok | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | shhong | - |
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