TY - JOUR
T1 - Adaptive control method of HVAC for uniformizing comfort at Japanese residential living rooms using deep reinforcement learning
AU - Sakuma, Yuiko
AU - Nishi, Hiroaki
N1 - Funding Information:
These research results were obtained from the commissioned research by National Institute of Information and Communications Technology (NICT, Grant Number 22004), Japan. The authors would like to thank Panasonic Corporation Appliances Company for providing advice for this research.
Publisher Copyright:
© 2021 The Institute of Electrical Engineers of Japan.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - There is a great demand for high-quality indoor thermal environments while achieving comfort and saving the energy for heating, ventilation, and air conditioning (HVAC) devices offer a tradeoff. The room shapes, partitioning, and the location of diffusers cause uneven comfort and degrade thermal satisfaction. Therefore, an HVAC control method that provides uniform thermal comfort while saving energy is required. In such cases, providing uniform comfort in a room can be a low-cost and energy-efficient solution. We propose a deep reinforcement learning (DRL)-based HVAC control method to uniformize comfort in a room. The proposed method considers the use of sensors, which are already deployed in existing HVAC devices and is adaptive to rooms with different conditions. The proposed method controls the HVAC device based on the prediction of the room's thermal response using wall heatmaps of the target room. The heatmaps are obtained by using a simple measuring system of an infrared thermopile array sensor. For evaluating the proposed method's adaptivity to rooms with different conditions, a computer flud dynamics (CFD)-based simulation was designed. The coldest period throughout a year was chosen as the evaluation period; uniformizing comfort is the most difficult when the temperature difference between outside and inside a room and the validity of the proposed method for the other periods is implied. The usefulness of the proposed method for the chosen period was shown for the selected period; the proposed method achieved an improvement of 33.9% in thermal comfort performance while on average increasing energy consumption by 6.3%, against that of comparable approaches. Moreover, the comfort performance during learning is confirmed to be similar to those of the existing method.
AB - There is a great demand for high-quality indoor thermal environments while achieving comfort and saving the energy for heating, ventilation, and air conditioning (HVAC) devices offer a tradeoff. The room shapes, partitioning, and the location of diffusers cause uneven comfort and degrade thermal satisfaction. Therefore, an HVAC control method that provides uniform thermal comfort while saving energy is required. In such cases, providing uniform comfort in a room can be a low-cost and energy-efficient solution. We propose a deep reinforcement learning (DRL)-based HVAC control method to uniformize comfort in a room. The proposed method considers the use of sensors, which are already deployed in existing HVAC devices and is adaptive to rooms with different conditions. The proposed method controls the HVAC device based on the prediction of the room's thermal response using wall heatmaps of the target room. The heatmaps are obtained by using a simple measuring system of an infrared thermopile array sensor. For evaluating the proposed method's adaptivity to rooms with different conditions, a computer flud dynamics (CFD)-based simulation was designed. The coldest period throughout a year was chosen as the evaluation period; uniformizing comfort is the most difficult when the temperature difference between outside and inside a room and the validity of the proposed method for the other periods is implied. The usefulness of the proposed method for the chosen period was shown for the selected period; the proposed method achieved an improvement of 33.9% in thermal comfort performance while on average increasing energy consumption by 6.3%, against that of comparable approaches. Moreover, the comfort performance during learning is confirmed to be similar to those of the existing method.
KW - Deep reinforcement learning
KW - Energy management systems
KW - HVAC control
KW - Infrared thermopile array sensors
KW - Smart home
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U2 - 10.1541/ieejeiss.141.373
DO - 10.1541/ieejeiss.141.373
M3 - Article
AN - SCOPUS:85101742900
VL - 141
SP - 373
EP - 382
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 3
ER -