An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

• URL: http://arxiv.org/abs/2401.05737v3
• Date: Sat, 31 Aug 2024 15:46:34 GMT
• Title: An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
• Authors: Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero,
• Abstract summary: Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can outperform traditional reactive controllers. This paper provides a critical and reproducible evaluation of several state-of-the-art DRL algorithms for HVAC control.
• Score: 40.71019623757305
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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