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

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Kuldeep Kurte , Kadir Amasyali , Peter L. Munk , Helia Zandi ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1109/isgt51731.2023.10066358

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.

Related Topics

Heating, Ventilation, And Air Conditioning

Carbon Footprint

Reinforcement Learning

Computer Science

Range (Aeronautics)

Air Conditioning

Ventilation (Architecture)

Environmental Science

Carbon Fibers

Automotive Engineering

Greenhouse Gas

Simulation

Engineering

Artificial Intelligence

Mathematics

Aerospace Engineering

Geometry

Mechanical Engineering

Ecology

Biology

Algorithm

Concepts

HVAC Carbon footprint Reinforcement learning Reduction (mathematics) Computer science Range (aeronautics) Air conditioning Ventilation (architecture) Environmental science Carbon fibers Automotive engineering Greenhouse gas Simulation Engineering Artificial intelligence Mathematics Aerospace engineering Composite number Geometry Mechanical engineering Ecology Biology Algorithm

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/isgt51731.2023.10066358
OA Status: green
Cited By: 5
References: 15
Related Works: 10
OpenAlex ID: https://openalex.org/W4360584158

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OpenAlex ID: https://openalex.org/W4360584158

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DOI: https://doi.org/10.1109/isgt51731.2023.10066358

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

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-01-16

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Authors: Kuldeep Kurte, Kadir Amasyali, Peter L. Munk, Helia Zandi

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Landing page: https://doi.org/10.1109/isgt51731.2023.10066358

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OA status: green

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OA URL: https://www.osti.gov/biblio/1965263

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Concepts: HVAC, Carbon footprint, Reinforcement learning, Reduction (mathematics), Computer science, Range (aeronautics), Air conditioning, Ventilation (architecture), Environmental science, Carbon fibers, Automotive engineering, Greenhouse gas, Simulation, Engineering, Artificial intelligence, Mathematics, Aerospace engineering, Composite number, Geometry, Mechanical engineering, Ecology, Biology, Algorithm

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Cited by: 5

Total citation count in OpenAlex
Citations by year (recent): 2025: 3, 2024: 2

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References (count): 15

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Related works (count): 10

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abstract_inverted_index.this	1, 31
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abstract_inverted_index.with	22
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abstract_inverted_index.(TOU)	59
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abstract_inverted_index.based	12
abstract_inverted_index.found	87
abstract_inverted_index.price	61, 72, 122, 126
abstract_inverted_index.range	94, 116
abstract_inverted_index.shift	45, 67
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abstract_inverted_index.using	33
abstract_inverted_index.where	40, 62
abstract_inverted_index.(HVAC)	21
abstract_inverted_index.6%-16%	97
abstract_inverted_index.carbon	27, 89, 111, 141
abstract_inverted_index.demand	47, 69
abstract_inverted_index.during	119
abstract_inverted_index.paper,	2
abstract_inverted_index.period	52, 73, 123
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abstract_inverted_index.control	14
abstract_inverted_index.optimal	83
abstract_inverted_index.present	4
abstract_inverted_index.results	130
abstract_inverted_index.signal.	106
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abstract_inverted_index.Heating,	16
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abstract_inverted_index.approach	137
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institutions_distinct_count	4
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citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False