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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cites
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Dynamic Daylight Performance Metrics for Sustainable Building Design
Reinhart, C. F., Mardaljevic, J., & Rogers, Z. (2006) · LEUKOS The Journal of the Illuminating Engineering Society of North America · doi:10.1582/leukos.2006.03.01.001 · Cited by 812
Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds
Reinhart, C. F., & Walkenhorst, O. (2001) · Energy and Buildings · doi:10.1016/s0378-7788(01)00058-5 · Cited by 736
Lightswitch-2002: a model for manual and automated control of electric lighting and blinds
Reinhart, C. F. (2004) · Solar Energy · doi:10.1016/j.solener.2004.04.003 · Cited by 633
Ten questions concerning occupant behavior in buildings: The big picture
Hong, T., Yan, D., D’Oca, S., & Chen, C. (2016) · Building and Environment · doi:10.1016/j.buildenv.2016.12.006 · Cited by 588
Kim, J., Schiavon, S., & Brager, G. S. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.01.023 · Cited by 524
IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings
Yan, D., Hong, T., Dong, B., Mahdavi, A., D’Oca, S., Gaetani, I., & Feng, X. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.09.084 · Cited by 509
The Neural Network House: An Environment that Adapts to its Inhabitants
Mozer, M. C. (1998) · Cited by 500
Kim, J., Zhou, Y., Schiavon, S., Raftery, P., & Brager, G. S. (2017) · Building and Environment · doi:10.1016/j.buildenv.2017.12.011 · Cited by 492
The human dimensions of energy use in buildings: A review
D’Oca, S., Hong, T., & Langevin, J. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.019 · Cited by 394
Flagged first, then by citations
2 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- V. Loftness, V. Hartkopf, B. Gurtekin, Linking energy to health and productivity in the built environment, in: 2003 Greenbuild Conference.
- Watkins, 1992, Q-learning, Mach. Learn., 8, 279
- J. Y. Park, T. Dougherty, Z. Nagy, A bluetooth based occupancy detection for buildings, in: 2018 Building Performance Analysis Conference and SimBuild.
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
Wang, Z., & Hong, T. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115036 · Cited by 590
“(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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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