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Comfort-aware HVAC Aggregation Method based on Deep Reinforcement Learning

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Takao OnoyeAuthors Info & Claims

BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 290 - 291

https://doi.org/10.1145/3600100.3626265

Published: 15 November 2023 Publication History

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Abstract

Extracting demand-side flexibility is important to utilize renewable energy as much as possible with keeping the grid stable. Heating, ventilation, and air conditioning (HVAC) system takes a necessary role in our comfort life and consumes more than 40% energy consumption of typical office buildings. HVAC systems are known as potential resources to extract demand-side flexibility because of easy performance control. This paper proposes a comfort-aware HVAC aggregation method based on deep reinforcement learning to both maintain a comfortable temperature and extract flexibility. Experiments show the proposed method satisfies the given demand-response requirements and thermal comfort.

References

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Donald Azuatalam, Wee-Lih Lee, Frits de Nijs, and Ariel Liebman. 2020. Reinforcement learning for whole-building HVAC control and demand response. Energy and AI 2 (11 2020), 100020.

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Peter Palensky and Dietmar Dietrich. 2011. Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics 7, 3 (8 2011), 381–388.

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Ashraf Radaideh, Ayman Al-Quraan, Hussein Al-Masri, and Zaid Albataineh. 2021. Rolling horizon control architecture for distributed agents of thermostatically controlled loads enabling long-term grid-level ancillary services. International Journal of Electrical Power & Energy Systems 127 (5 2021), 106630.

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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arxiv:1707.06347 [cs.LG]

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Dafang Zhao, Daichi Watari, Yuki Ozawa, Ittetsu Taniguchi, Toshihiro Suzuki, Yoshiyuki Shimoda, and Takao Onoye. 2022. A Thermal Comfort and Peak Power Demand Aware VRF Heating/Cooling Management Framework: Simulation and On-site Experiment. Journal of Information Processing 30 (7 2022), 476–485.

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Cited By

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Malfait HZhao DNishikawa HTaniguchi ISuzuki TOnoye T(2024)Comfort-Aware HVAC Aggregation for Enhancing Demand-Side Flexibility: Insights from an On-Site ExperimentProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698703(227-228)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3698703
Sasaki RNonaka SZhao DNishikawa HTaniguchi IOnoye T(2024)Thermal Comfort-aware Aggregation via Multi-HVAC Systems for Demand-side FlexibilityProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661975(480-481)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661975

Index Terms

Comfort-aware HVAC Aggregation Method based on Deep Reinforcement Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
      1. Planning under uncertainty
2. Hardware
  1. Power and energy
    1. Energy distribution
      1. Smart grid

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Published In

BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2023

567 pages

ISBN:9798400702303

DOI:10.1145/3600100

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2023

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Short-paper
Research
Refereed limited

Funding Sources

DAIKIN Industries, Ltd.
JSPS KAKENHI

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BuildSys '23

BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 15 - 16, 2023

Istanbul, Turkey

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Malfait HZhao DNishikawa HTaniguchi ISuzuki TOnoye T(2024)Comfort-Aware HVAC Aggregation for Enhancing Demand-Side Flexibility: Insights from an On-Site ExperimentProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698703(227-228)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3698703
Sasaki RNonaka SZhao DNishikawa HTaniguchi IOnoye T(2024)Thermal Comfort-aware Aggregation via Multi-HVAC Systems for Demand-side FlexibilityProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661975(480-481)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661975

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Thermal Comfort-aware Aggregation via Multi-HVAC Systems for Demand-side Flexibility

Comfort-Aware HVAC Aggregation for Enhancing Demand-Side Flexibility: Insights from an On-Site Experiment

Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems

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