An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

arXiv:2401.05737v2 Announce Type: replace
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 …

abstract air conditioning algorithms arxiv buildings commercial consumption control cs.lg cs.sy driver eess.sy energy evaluation experimental however hvac major reinforcement reinforcement learning solutions studies systems type