Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The transactions of The Korean Institute of Electrical Engineers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
