Abstract
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 language | English (US) |
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Title of host publication | Proceedings of the 54th Annual Design Automation Conference 2017, DAC 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781450349277 |
DOIs | |
State | Published - Jun 18 2017 |
Event | 54th Annual Design Automation Conference, DAC 2017 - Austin, United States Duration: Jun 18 2017 → Jun 22 2017 |
Publication series
Name | Proceedings - Design Automation Conference |
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Volume | Part 128280 |
ISSN (Print) | 0738-100X |
Other
Other | 54th Annual Design Automation Conference, DAC 2017 |
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Country/Territory | United States |
City | Austin |
Period | 6/18/17 → 6/22/17 |
Funding
The authors gratefully acknowledge the support from the National Science Foundation award CCF-1553757 and the Riverside Public Utilities.
ASJC Scopus subject areas
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modeling and Simulation