The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.
LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning
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In the citing paper
“LUR is an example of user comfort measurement based on control quality because the measure refers to the ratio of the time the lights are on with the time the lights are used [347].”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as [347]
In the citing paper
“Many systems within buildings-such as lights and heating-can also adjust how they operate based on whether a building or room is occupied, thereby improving both occupant comfort and energy use [614].”
From Tackling climate change with machine learning (2023) · cites this paper as [614]
Citations
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Self-citation
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Cited by
Sorted by citations
Reinforcement learning for demand response: A review of algorithms and modeling techniques
Vázquez-Canteli, J. R., & Nagy, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.11.002 · Cited by 883
Self-citationReinforcement learning for building controls: The opportunities and challenges
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“(2019) [103] Energy & Comfort lighting Value iteration limited state and action, explore all of them Not used Yes Vázquez-Canteli et al.”
BackgroundInfluentialTushar, W., Yuen, C., Saha, T. K., Morstyn, T., Chapman, A. C., Alam, M. J. E., Hanif, S., & Poor, H. V. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.116131 · Cited by 527
“In [94], LightLearn - a reinforcement learning-based lighting control scheme - is developed using occupant preferences that successfully balance occupant comfort and energy consumption.”
A Review of Deep Reinforcement Learning for Smart Building Energy Management
Yu, L., Qin, S., Zhang, M., Shen, C. Q., Jiang, T., & Guan, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078462 · Cited by 556
Applications of reinforcement learning in energy systems
Perera, A. T. D., & Kamalaruban, P. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110618 · Cited by 457
“In these works, while learning the model, the transition dynamics is represented in tabular form [10,76,90,144], RF [98], NN [98], or DNN ensemble [111].”
Background
Metadata
- Authors
- Park, J. Y., Dougherty, T., Fritz, H., & Nagy, Z.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Building and Environment
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 147
- Pages
- 397-414
- ISSN
- 0360-1323 (ISSN-L)
- OpenAlex
- W2896270480
- Cited by
- 238
- References
- 68
- Citations by year
- 2026: 32 · 2025: 26 · 2024: 31 · 2023: 36 · 2022: 38 · 2021: 32 · 2020: 25 · 2019: 17
- FWCI
- 15.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Building and Environment (publishedVersion)
- Funders
- University of Texas at Austin
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- adaptive lighting control, reinforcement learning, commercial building energy efficiency, occupant comfort, occupancy sensors, office lighting
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