A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple...

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Bibliographic Details
Published in:2023 IEEE Smart World Congress (SWC) pp. 1 - 7
Main Authors: Wang, Marshall, Willes, John, Jiralerspong, Thomas, Moezzi, Matin
Format: Conference Proceeding
Language:English
Published: IEEE 28-08-2023
Subjects:
Aerospace electronics
Costs
Energy Efficiency
HVAC
HVAC control
Q-Learning
Reinforcement Learning
System performance
Task analysis
Training
Tuning
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Summary:Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.
DOI:10.1109/SWC57546.2023.10448598