%0 Journal Article %K Open AI Gym %A Zoltan Nagy %A Gregor P Henze %A Sourav Dey %A Javier Arroyo %A Lieve Helsen %A Xiangyu Zhang %A Bingqing Chen %A Kadir Amasyali %A Kuldeep Kurte %A Ahmed Zamzam %A Helia Zandi %A Ján Drgoňa %A Matias Quintana %A Steven McCullogh %A June Young Park %A Han Li %A Tianzhen Hong %A Silvio Brandi %A Giuseppe Pinto %A Alfonso Capozzoli %A Draguna Vrabie %A Mario Bergés %A Kingsley Nweye %A Thibault Marzullo %A Andrey Bernstein %B Building and Environment %D 2023 %G eng %P 110435 %R 10.1016/j.buildenv.2023.110435 %T Ten questions concerning reinforcement learning for building energy management %U https://linkinghub.elsevier.com/retrieve/pii/S0360132323004626 %8 05/2023 %! Building and Environment %X

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. In this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.