Optimal Control of Earth-to-Air Heat Exchanger System Using Reinforcement Learning

The purpose of this study is to establish optimal control rules for an earth-to-air heat exchanger (EAHE) using reinforcement learning (RL). We validate the RL control method that achieves two objectives: maintaining the heat load of a fresh air handling unit (FAHU) and suppressing the occurrence of condensation in the EAHE. Compared to scheduled control, RL control increases the annual heat load of the FAHU by approximately 1%, but the time-integrated condensation area ratio is suppressed by approximately 75%. Thus, we confirm that it is possible to achieve the two aforementioned objectives simultaneously using RL control.