類似仮想ビル選択による即時運用可能な空調電力管理システムの強化学習制御

抄録

This paper proposes a novel method for building multi-type air-conditioners, wherein the power management control system can automatically adapt to various air-conditioning environments using reinforcement learning. Our previous study reduced the learning period by pre-training on a virtual building and simulated the dynamic power characteristics of air-conditioners and room temperature. However, advantages of decreasing the learning period diminish when the difference between the virtual and actual buildings is significant. Therefore, our proposed method first performs pre-training on multiple virtual buildings with different environments. Subsequently, it selects the one whose environment is closest to that of the actual building based on the difference in average rewards.