역사 내 미세먼지 농도 조절을 위한 강화학습 기반의 송풍기·공조기 제어 에이전트 구축

초록

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

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