Airflow Direction Control of Air Conditioners Using Deep Reinforcement Learning

Yuiko Sakuma, Hiroaki Nishi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ61-68
ページ数8
ISBN(電子版)9784907764647
DOI
出版ステータスPublished - 2020 3
イベント2020 SICE International Symposium on Control Systems, SICE ISCS 2020 - Tokushima, Japan
継続期間: 2020 3 32020 3 5

出版物シリーズ

名前Proceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020

Conference

Conference2020 SICE International Symposium on Control Systems, SICE ISCS 2020
CountryJapan
CityTokushima
Period20/3/320/3/5

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Systems Engineering
  • Computational Mathematics
  • Control and Optimization
  • Modelling and Simulation
  • Artificial Intelligence
  • Aerospace Engineering

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