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

被引：0
作者：
Tomoda, Kento [1 ]
Shiraishi, Yasuyuki [1 ]
Saelens, Dirk [2 ,3 ]
机构：
[1] Univ Kitakyushu, Fukuoka, Japan
[2] Katholieke Univ Leuven, Dept Civil Engn, Bldg Phys & Sustainable Design Sect, Leuven, Belgium
[3] EnergyVille, Genk, Belgium
来源：
PROCEEDINGS OF BUILDING SIMULATION 2021: 17TH CONFERENCE OF IBPSA | 2022年 / 17卷
关键词：
D O I：
10.26868/25222708.2021.30448
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：239 / 246
页数：8
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