Can reinforcement learning move beyond hype in building control?
-
-
REFERENCES
[1] U.S. Department of Energy. (2021). A National Roadmap for Grid-Interactive Efficient Buildings. DOE. [2] MimsFrick N. and Schwartz L. (2019). Time-Sensitive Value of Efficiency. Lawrence Berkeley National Lab. [3] Ding C., Zhang X., Li Y., et al. (2024). Data-driven control strategies for complex energy systems under uncertainty. Nat. Commun. 15:5916. DOI:10.1016/j.apenergy.2021.118148 [4] Nagy Z., Henze G., Dey S., et al. (2023). Ten questions concerning reinforcement learning for building energy management. Build. Environ. 241:110435. DOI:10.1016/j.buildenv.2023.110435 [5] Yu H., Tam V.W.Y. and Xu X. (2024). A systematic review of reinforcement learning application in building energy-related occupant behavior simulation. Energy Build. 312:113825. DOI:10.1016/j.enbuild.2024.114189 -
ABOUT THIS ARTICLE
Cite this article:
Saad Ul Haq M., Ji W., Geng Y., et al. (2026). Can reinforcement learning move beyond hype in building control? The Innovation Energy 3:100150. https://doi.org/10.59717/j.xinn-energy.2026.100150
Welcome!
To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.
Export File
Citation
Saad Ul Haq M., Ji W., Geng Y., et al. (2026). Can reinforcement learning move beyond hype in building control? The Innovation Energy 3:100150. https://doi.org/10.59717/j.xinn-energy.2026.100150
Format
Content
DownLoad:
-
Figure 1.
Bridging RL concepts and deployment reality in grid-interactive buildings
