Abstract
While both model predictive control (MPC) and deep reinforcement learning control (DRL) have shown significant energy cost savings for building systems, there is a lack of in-depth quantitative study on the comparison between the two. One major obstacle is the lack of a holistic evaluation environment for the building community and the control community to integrate their expertise in studying both model-based and learning-based control methods. To address this challenge, this paper presents a scalable containerized software framework for building control performance comparisons, with a special focus on enabling both optimal model-based control and deep learning-based control. The framework provides a standardized building environment for the control community to benchmark different advanced control strategies, and a flexible software architecture for the building community to standardize their own customized building environments. A preliminary performance comparison of MPC and DRL on a single zone building is performed in the case study. Both MPC and DRL can outperform the rule-based baseline controllers in terms of reducing energy cost and maintaining thermal discomfort. DRL can outperform MPC after a long training time with a predefined reward policy.
Original language | English (US) |
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Title of host publication | BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments |
Publisher | Association for Computing Machinery, Inc |
Pages | 276-280 |
Number of pages | 5 |
ISBN (Electronic) | 9781450391146 |
DOIs | |
State | Published - Nov 17 2021 |
Event | 8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021 - Virtual, Online, Portugal Duration: Nov 17 2021 → Nov 18 2021 |
Publication series
Name | BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments |
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Conference
Conference | 8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021 |
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Country/Territory | Portugal |
City | Virtual, Online |
Period | 11/17/21 → 11/18/21 |
Funding
The research reported in this paper was supported by the Building Technologies Office at the U.S. Department of Energy through the Emerging Technologies program under award number DE-EE0009150.
Keywords
- OpenAI-Gym
- building energy and control system
- deep reinforcement learning
- model predictive control
ASJC Scopus subject areas
- Information Systems
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Building and Construction
- Computer Networks and Communications
- Architecture