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

Conference ·
 [1];  [1];  [2];  [1]
  1. ORNL
  2. National Renewable Energy Laboratory (NREL)
+ Show Author Affiliations

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.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1965263
Resource Relation:
Conference: The 2023 North American Innovative Smart Grid Technologies Conference (ISGT) - Washington, D.C, District of Columbia, United States of America - 1/16/2023 3:00:00 PM-1/19/2023 3:00:00 PM
Country of Publication:
United States
Language:
English

References (13)

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conference November 2020
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Deep Reinforcement Learning for Building HVAC Control
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conference June 2017
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Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning journal January 2021

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