Miguel Molina-Solana

The building and construction sector —and particularly Heating, Ventilation, and Air Conditioning (HVAC) systems— pose a significant challenge to climate change mitigation given its high impact on global energy consumption and CO_2 emissions. Recent studies have addressed efficient HVAC control by employing different Deep Reinforcement Learning (DRL) algorithms, but in general they are are explicitly designed for specific setups and do not provide a standardized comparison of their performance. This paper aims to fill this gap by developing a critical and reproducible evaluation of state-of-the-art DRL algorithms applied to HVAC control in various simulated buildings and configurations. To this end, we considered several aspects of energy consumption and occupants’ comfort: and took into account the performance of agents, their robustness under different conditions, their capability to adapt from simpler to more complex environments, and the trade-off between optimization goals. We used the sinergym software, a simulation framework capable of running experiments involving multiple models and configurations. The obtained results confirm the potential of DRL algorithms, such as SAC and TD3, compared to reactive control methods, and reveal interesting insights about their generalization and learning capabilities.

@article{Manjavacas2023,
  author = {Manjavacas, Antonio and Campoy-Nieves, Alejandro and Jiménez-Raboso, Javier and Molina-Solana, Miguel and Gómez-Romero, Juan},
  title = {An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control},
  journal = {Artificial Intelligence Review},
  year = {SUBMITTED},
  volume = {(under review)},
  doi = {},
  comment = {},
  timestamp = {37}
}