Reinforcement Learning-based HVAC Control Agent for Optimal Control of Particulate Matter in Railway Stations
This study developed a reinforcement learning-based energy management agent that controls the concentration of fine dust by controlling the power consumption of energy facilities such as air conditioners and blowers in stations. To apply reinforcement learning, the problem was first defined based on the Markov decision-making process, and a model was developed to predict the concentration of fine dust in history using data correlated with fine dust. Based on the linear compensation function created based on this, the Deep Q-Network (DQN) method was applied to obtain the optimal policy based on the artificial neural network. In the case study, it was confirmed that convergence to the optimal policy was achieved through the learning process, and it was confirmed that the learned agent lowers the fine dust concentration by increasing the power consumption of the air conditioner when the fine dust concentration in the station rises above a certain level.
역사 내 미세먼지 농도 조절을 위한 강화학습 기반의 공조설비 제어 에이전트 구축
Reinforcement Learning-based HVAC Control Agent for Optimal Control of Particulate Matter in Railway Stations
This study developed a reinforcement learning-based energy management agent that controls the concentration of fine dust by controlling the power consumption of energy facilities such as air conditioners and blowers in stations. To apply reinforcement learning, the problem was first defined based on the Markov decision-making process, and a model was developed to predict the concentration of fine dust in history using data correlated with fine dust. Based on the linear compensation function created based on this, the Deep Q-Network (DQN) method was applied to obtain the optimal policy based on the artificial neural network. In the case study, it was confirmed that convergence to the optimal policy was achieved through the learning process, and it was confirmed that the learned agent lowers the fine dust concentration by increasing the power consumption of the air conditioner when the fine dust concentration in the station rises above a certain level.
저널 정보
대한전기학회
전기학회논문지 제70권 제10호
ISSN 1975-8359[print] / ISSN 2287-4364[online]
2021. 10
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저자
권경빈 (RaonFriends)
홍수민 (RaonFriends))
허재행 (RaonFriends)
정호성 (Korea Railroad Research Institute)
박종영 (Korea Railroad Research Institute)