Presentation 2015-06-18
Robust Policy Optimization for Demand Response to Utilize Photovoltaics Power
Sachiyo Arai, Talatoshi Watanabe,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In order to utilize solar photovoltaic, it is important to resolve instability of its demand. We have proposed a reinforcement learning agent to manage the microgrid to be independent of main grid. The agent consists of five modules; solar photovoltaic, constant generator, power demand, home co-generation system and storage unit. In our previously proposed agent, which was reinforced with the monthly data, it turns out that agent hard to adapt to changes in the dynamics of monthly data. In this paper, we proposed the improved agent model, which has an ability to adapt to changes in the dynamics of monthly data.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Demand response / Reinforcement learning
Paper # AI2015-4
Date of Issue 2015-06-11 (AI)

Conference Information
Committee AI
Conference Date 2015/6/18(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Toshiharu Sugawara(Waseda Univ.)
Vice Chair Tsunenori Mine(Kyushu Univ.) / Daisuke Katagami(Tokyo Polytechnic Univ.)
Secretary Tsunenori Mine(Kyoto Univ.) / Daisuke Katagami(Shizuoka Univ.)
Assistant Yuichi Sei(Univ. of Electro-Comm.)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Robust Policy Optimization for Demand Response to Utilize Photovoltaics Power
Sub Title (in English) Effects of State Representation
Keyword(1) Demand response
Keyword(2) Reinforcement learning
1st Author's Name Sachiyo Arai
1st Author's Affiliation Chiba University(Chiba Univ.)
2nd Author's Name Talatoshi Watanabe
2nd Author's Affiliation Chiba University(Chiba Univ.)
Date 2015-06-18
Paper # AI2015-4
Volume (vol) vol.115
Number (no) AI-97
Page pp.pp.19-24(AI),
#Pages 6
Date of Issue 2015-06-11 (AI)