Deep Reinforcement Learning for Building HVAC Control

Tianshu Wei, Yanzhi Wang, Qi Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

309 Scopus citations


Buildings account for nearly 40% of the total energy consumption in the United States, about half of which is used by the HVAC (heating, ventilation, and air conditioning) system. Intelligent scheduling of building HVAC systems has the potential to significantly reduce the energy cost. However, the traditional rule-based and model-based strategies are often inefficient in practice, due to the complexity in building thermal dynamics and heterogeneous environment disturbances. In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the building HVAC systems. We evaluate the performance of our DRL algorithm through simulations using the widely-adopted EnergyPlus tool. Experiments demonstrate that our DRL-based algorithm is more effective in energy cost reduction compared with the traditional rule-based approach, while maintaining the room temperature within desired range.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450349277
StatePublished - Jun 18 2017
Event54th Annual Design Automation Conference, DAC 2017 - Austin, United States
Duration: Jun 18 2017Jun 22 2017

Publication series

NameProceedings - Design Automation Conference
VolumePart 128280
ISSN (Print)0738-100X


Other54th Annual Design Automation Conference, DAC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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