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Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants.
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Exploring Deep Reinforcement Learning for Holistic Smart Building Control
In recent years, the focus has been on enhancing user comfort in commercial buildings while cutting energy costs. Efforts have mainly centered on improving HVAC systems, the central control system. However, it’s evident that HVAC alone can’t ensure occupant comfort. Lighting, blinds, and windows, often overlooked, also impact energy use and comfort. This paper introduces a holistic approach to managing the delicate balance between energy efficiency and occupant comfort in commercial buildings. We presentOCTOPUS, a system employing a deep reinforcement learning (DRL) framework using data-driven techniques to optimize control sequences for all building subsystems, including HVAC, lighting, blinds, and windows.OCTOPUS’s DRL architecture features a unique reward function facilitating the exploration of tradeoffs between energy usage and user comfort, effectively addressing the high-dimensional control problem resulting from interactions among these four building subsystems. To meet data training requirements, we emphasize the importance of calibrated simulations that closely replicate target-building operational conditions. We trainOCTOPUSusing 10-year weather data and a calibrated building model in the EnergyPlus simulator. Extensive simulations demonstrate thatOCTOPUSachieves substantial energy savings, outperforming state-of-the-art rule-based and DRL-based methods by 14.26% and 8.1%, respectively, in a LEED Gold Certified building while maintaining desired human comfort levels.
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- Award ID(s):
- 2239458
- PAR ID:
- 10536238
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Sensor Networks
- Volume:
- 20
- Issue:
- 3
- ISSN:
- 1550-4859
- Page Range / eLocation ID:
- 1 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3656043
