TY - GEN
T1 - Deep Reinforcement Learning for Building HVAC Control
AU - Wei, Tianshu
AU - Wang, Yanzhi
AU - Zhu, Qi
N1 - Funding Information:
The authors gratefully acknowledge the support from the National Science Foundation award CCF-1553757 and the Riverside Public Utilities.
Publisher Copyright:
© 2017 ACM.
PY - 2017/6/18
Y1 - 2017/6/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85023647422&partnerID=8YFLogxK
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U2 - 10.1145/3061639.3062224
DO - 10.1145/3061639.3062224
M3 - Conference contribution
AN - SCOPUS:85023647422
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th Annual Design Automation Conference, DAC 2017
Y2 - 18 June 2017 through 22 June 2017
ER -