Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system

Zhiang Zhang, Khee Poh Lam

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

46 Citations (Scopus)

Abstract

Deep reinforcement learning (DRL) has become a popular optimal control method in recent years. This is mainly because DRL has the potential to solve the optimal control problems with complex process dynamics, such as the optimal control for heating, ventilation, and air-conditioning (HVAC) systems. However, DRL control for HVAC systems has not been well studied. There is limited research on the real-life implementation and evaluation of this method. This study implements and deploys a DRL control method for a radiant heating system in a real-life office building for energy efficiency. A physics-based model for the heating system is first created and then calibrated using the measured building operation data. After that, the model is used as a simulator to train the DRL agent. The trained agent is then deployed in the actual heating system, and a smartphone App is used to let the occupants submit their thermal preferences to the DRL agent. It is found the DRL control method can save 16.6% to 18.2% heating demand compared to the old rule-based control logic over the three-month deployment period. However, several limitations of this study are found, such as the low participation rate of the App-based thermal preference feedback system, inefficient DRL training, and the requirement for a large amount of building data.

Original languageEnglish
Title of host publicationBuildSys 2018 - Proceedings of the 5th Conference on Systems for Built Environments
EditorsGowri Sankar Ramachandran, Nipun Batra
PublisherAssociation for Computing Machinery, Inc
Pages148-157
Number of pages10
ISBN (Electronic)9781450359511
DOIs
Publication statusPublished - 7 Nov 2018
Externally publishedYes
Event5th ACM International Conference on Systems for Built Environments, BuildSys 2018 - Shenzen, China
Duration: 7 Nov 20188 Nov 2018

Publication series

NameBuildSys 2018 - Proceedings of the 5th Conference on Systems for Built Environments

Conference

Conference5th ACM International Conference on Systems for Built Environments, BuildSys 2018
Country/TerritoryChina
CityShenzen
Period7/11/188/11/18

Keywords

  • Deep reinforcement learning
  • Energy efficiency
  • HVAC control

ASJC Scopus subject areas

  • Architecture
  • Computer Networks and Communications
  • Building and Construction
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Information Systems

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