Ten questions concerning reinforcement learning for building energy management

Nagy, Zoltan; Henze, Gregor; Dey, Sourav; Arroyo, Javier; Helsen, Lieve; Zhang, Xiangyu; Chen, Bingqing; Amasyali, Kadir; Kurte, Kuldeep; Zamzam, Ahmed; Zandi, Helia; Drgoňa, Ján; Quintana, Matias; McCullogh, Steven; Park, June Young; Li, Han; Hong, Tianzhen; Brandi, Silvio; Pinto, Giuseppe; Capozzoli, Alfonso; Vrabie, Draguna; Bergés, Mario; Nweye, Kingsley; Marzullo, Thibault; Bernstein, Andrey

doi:10.1016/j.buildenv.2023.110435

Title: Ten questions concerning reinforcement learning for building energy management

Journal Article · Sun May 28 00:00:00 EDT 2023 · Building and Environment

DOI:https://doi.org/10.1016/j.buildenv.2023.110435· OSTI ID:1986487

^[1]; Henze, Gregor ^[2]; Dey, Sourav ^[3]; Arroyo, Javier ^[4]; Helsen, Lieve ^[5]; Zhang, Xiangyu ^[6]; Chen, Bingqing ^[7];

^[8]; Kurte, Kuldeep ^[8]; Zamzam, Ahmed ^[6];

^[8]; Drgoňa, Ján ^[9]; Quintana, Matias ^[10]; McCullogh, Steven ^[11]; Park, June Young ^[11]; Li, Han ^[12]; Hong, Tianzhen ^[12]; Brandi, Silvio ^[13]; Pinto, Giuseppe ^[14]; Capozzoli, Alfonso ^[13] more »

University of Texas, Austin, TX (United States)
University of Colorado, Boulder, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
University of Colorado, Boulder, CO (United States)
Katholieke University Leuven (Belgium)
Katholieke University. Leuven (Belgium)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Bosch Research North America, Pittsburgh, PA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
National University of Singapore (Singapore); Eidgenoessische Technische Hochschule (ETH) (Singapore). Singapore-ETH Centre
University of Texas, Arlington, TX (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Polytechnic University of Turin (Italy)
PassiveLogic, Salt Lake City, UT (United States); Polytechnic University of Turin (Italy)
Carnegie Mellon University, Pittsburgh, PA (United States)

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. Here, 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.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC05-76RL01830; AC02-05CH11231; AC36-08GO28308; AC05-00OR22725

OSTI ID:: 1986487

Alternate ID(s):: OSTI ID: 1984043; OSTI ID: 1986509; OSTI ID: 2000373

Report Number(s):: PNNL-SA-186171; NREL/JA-5D00-84137

Journal Information:: Building and Environment, Vol. 241; ISSN 0360-1323

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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