An experimental evaluation of deep reinforcement learning algorithms for HVAC control
File(s)
Published version
Author(s)
Manjavacas, Antonio
Campoy-Nieves, Alejandro
Jimenez-Raboso, Javier
Molina-Solana, Miguel
Gomez-Romero, Juan
Type
Journal Article
Abstract
Heating, ventilation, and air conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can outperform traditional reactive controllers. However, DRL-based solutions are generally designed for ad hoc setups and lack standardization for comparison. To fill this gap, this paper provides a critical and reproducible evaluation, in terms of comfort and energy consumption, of several state-of-the-art DRL algorithms for HVAC control. The study examines the controllers’ robustness, adaptability, and trade-off between optimization goals by using the Sinergym framework. The results obtained confirm the potential of DRL algorithms, such as SAC and TD3, in complex scenarios and reveal several challenges related to generalization and incremental learning.
Date Issued
2024-06-13
Date Acceptance
2024-05-28
Citation
Artificial Intelligence Review, 2024, 57 (7)
ISSN
0269-2821
Publisher
Springer
Journal / Book Title
Artificial Intelligence Review
Volume
57
Issue
7
Copyright Statement
© The Author(s) 2024 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Subjects
Building energy optimization
BUILDINGS
Computer Science
Computer Science, Artificial Intelligence
HVAC
MODEL
PREDICTIVE CONTROL
Reinforcement learning
Science & Technology
SIMULATION
Sinergym
SYSTEMS
Technology
THERMAL COMFORT
Publication Status
Published
Article Number
173
Date Publish Online
2024-06-13