Hybrid reinforcement learning for occupant-centric building control: a review and deployment framework for co-optimizing energy, comfort, and indoor air quality

Mohsenpour, M ORCID: https://orcid.org/0009-0006-7539-267X and Xing, Y ORCID: https://orcid.org/0000-0002-5374-7269, 2026. Hybrid reinforcement learning for occupant-centric building control: a review and deployment framework for co-optimizing energy, comfort, and indoor air quality. Applied Energy, 408: 127392. ISSN 0306-2619

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Abstract

The operational phase of buildings represents a major share of global energy consumption, underscoring its importance in achieving sustainability goals. Reinforcement learning, with its ability to manage both continuous and discrete control tasks, shows strong performance for enhancing building systems' efficiency. Existing reviews on reinforcement learning applications in building energy systems primarily focus on heating, ventilation, and air conditioning systems and often overlook critical distinctions between system-centric and occupant-centric control strategies, as well as the role of data acquisition for training. To address these gaps, this study conducts systematic review of reinforcement learning and hybrid reinforcement learning approaches to answer the question of how reinforcement learning methods improve the performance of heating, ventilation, and air conditioning systems, lighting systems, and window systems in terms of energy efficiency, thermal comfort, and indoor air quality. This study summarizes the states, actions, rewards, and performance of reinforcement learning methods. Through a critical analysis of more than seventy papers, this review distinguishes between system-centric and occupant-centric control models in terms of publication trends, design frameworks, and simulation and co-simulation tools. This review also goes beyond the simulation stage and investigates reinforcement learning challenges and methods, training strategies, and data-collection techniques for real-world deployment. In addition, this study proposes a novel co-adaptive reinforcement learning framework for further research on real-world deployment, considering occupants as the core of the design stage. Finally, this study identifies and discusses ten future research directions, outlining current limitations and opportunities for advancing reinforcement learning in building system control.

Item Type:	Journal article
Publication Title:	Applied Energy
Creators:	Mohsenpour, M. and Xing, Y.
Publisher:	Elsevier BV
Date:	April 2026
Volume:	408
ISSN:	0306-2619
Identifiers:	Number Type 10.1016/j.apenergy.2026.127392 DOI 2558275 Other
Rights:	© 2026 The Authors. This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Architecture, Design and the Built Environment
Record created by:	Jeremy Silvester
Date Added:	16 Jan 2026 09:35
Last Modified:	16 Jan 2026 09:35
URI:	https://irep.ntu.ac.uk/id/eprint/55056

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