TY - GEN
T1 - Airflow Direction Control of Air Conditioners Using Deep Reinforcement Learning
AU - Sakuma, Yuiko
AU - Nishi, Hiroaki
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR19K1, MEXT/JSPS KAKENHI Grant (B) Number JP16H04455 and JP17H01739. The authors would like to thank Panasonic Corporation, Appliances Company for providing advice for this research.
Publisher Copyright:
© 2020 The Society of Instrument and Control Engineers - SICE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3
Y1 - 2020/3
N2 - Achieving a uniform comfort within an indoor housing environment is important for health and productivity while saving the energy consumption of a Heating, Ventilation, and Air Conditioners (HVAC) device. The optimal control of an HVAC system is a well-studied area. While many works explore the optimal temperature set-point, a few works consider effective airflow direction control. This work proposes an airflow direction control method that aims uniform comfort of the indoor environment using a deep reinforcement learning (DRL) approach. We implemented our proposed DRL framework using computational fluid dynamics (CFD) simulation software. Our proposed method was evaluated for comfort and energy consumption. The experimental results show the improvements for our proposed method in comfort by 21.3 % while reducing energy consumption by 34.5 % for the average than the baseline method.
AB - Achieving a uniform comfort within an indoor housing environment is important for health and productivity while saving the energy consumption of a Heating, Ventilation, and Air Conditioners (HVAC) device. The optimal control of an HVAC system is a well-studied area. While many works explore the optimal temperature set-point, a few works consider effective airflow direction control. This work proposes an airflow direction control method that aims uniform comfort of the indoor environment using a deep reinforcement learning (DRL) approach. We implemented our proposed DRL framework using computational fluid dynamics (CFD) simulation software. Our proposed method was evaluated for comfort and energy consumption. The experimental results show the improvements for our proposed method in comfort by 21.3 % while reducing energy consumption by 34.5 % for the average than the baseline method.
KW - Deep reinforcement learning
KW - HVAC control
UR - http://www.scopus.com/inward/record.url?scp=85085255711&partnerID=8YFLogxK
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U2 - 10.23919/SICEISCS48470.2020.9083565
DO - 10.23919/SICEISCS48470.2020.9083565
M3 - Conference contribution
AN - SCOPUS:85085255711
T3 - Proceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020
SP - 61
EP - 68
BT - Proceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 SICE International Symposium on Control Systems, SICE ISCS 2020
Y2 - 3 March 2020 through 5 March 2020
ER -
