Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings

Kuldeep Kurte; Kadir Amasyali; Jeffrey Munk; Helia Zandi

doi:10.1109/ISGT51731.2023.10066358

Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings

Kuldeep Kurte, Kadir Amasyali, Jeffrey Munk, Helia Zandi

Computational Systems Engineering and Cy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of ≈ 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of ≈23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

Original language	English
Title of host publication	2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665453554
DOIs	https://doi.org/10.1109/ISGT51731.2023.10066358
State	Published - 2023
Event	2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 - Washington, United States Duration: Jan 16 2023 → Jan 19 2023

Publication series

Name	2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023

Conference

Conference	2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
Country/Territory	United States
City	Washington
Period	01/16/23 → 01/19/23

Funding

ACKNOWLEDGMENT This work was funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Building Technology Office under contract number DE-AC05-00OR22725.

Keywords

HVAC control
carbon emission
deep reinforcement learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISGT51731.2023.10066358

Cite this

Kurte, K., Amasyali, K., Munk, J., & Zandi, H. (2023). Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings. In 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 (2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGT51731.2023.10066358

Kurte, Kuldeep ; Amasyali, Kadir ; Munk, Jeffrey et al. / Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings. 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023).

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title = "Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings",

abstract = "In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of ≈ 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of ≈23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.",

keywords = "HVAC control, carbon emission, deep reinforcement learning",

author = "Kuldeep Kurte and Kadir Amasyali and Jeffrey Munk and Helia Zandi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 ; Conference date: 16-01-2023 Through 19-01-2023",

year = "2023",

doi = "10.1109/ISGT51731.2023.10066358",

language = "English",

series = "2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023",

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Kurte, K, Amasyali, K, Munk, J & Zandi, H 2023, Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings. in 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023. 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023, Washington, United States, 01/16/23. https://doi.org/10.1109/ISGT51731.2023.10066358

Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings. / Kurte, Kuldeep; Amasyali, Kadir; Munk, Jeffrey et al.
2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kurte, Kuldeep

AU - Amasyali, Kadir

AU - Munk, Jeffrey

AU - Zandi, Helia

PY - 2023

Y1 - 2023

N2 - In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of ≈ 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of ≈23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

AB - In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of ≈ 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of ≈23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

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Kurte K, Amasyali K, Munk J, Zandi H. Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings. In 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023). doi: 10.1109/ISGT51731.2023.10066358

Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings

Abstract

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Keywords

UN SDGs

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