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
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
Items 201–237 of 237
Creativity and innovation in building automation systems
Habibi, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822129-7.00003-6
A Novel Reinforcement Learning Method for Improving Occupant Comfort via Window Opening and Closing
May, R., Han, M., & Zhang, X. (2021) · Sustainable development goals series · doi:10.1007/978-981-16-2778-1_10 · Cited by 2
Fang, Z., Lu, Q., Xie, X., Parlikad, A. K., Schooling, J. M., & Pitt, M. (2022) · doi:10.1680/dtbe.65802.161
Liang, S., Lu, Y. H., Lin, Y., & Xiao, Y. (2023) · Environmental science and engineering · doi:10.1007/978-981-19-9822-5_200
Iluminatul centrat pe ocupant (OCL) pentru bunăstare: o revizuire
Simtinică, M.-C., Husch, M., & Caluianu, S. (2023) · Revista Romana de Inginerie Civila/Romanian Journal of Civil Engineering · doi:10.37789/rjce.2023.14.4.1
Enhancing Energy Efficiency through Smart Detection and Analysis in Indoor SpacesPreprint
Gala, D., Khetan, S., & Mehendale, N. D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4657093
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2023) · International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA) · doi:10.1109/icicyta60173.2023.10428882 · Cited by 1
“Park et al. [8] uses several metrics to measure user comfort.”
BackgroundExperimental Evaluation of Advanced Controls for Hvac Systems with Physics-Informed Neural NetworksPreprint
Wang, X., & Dong, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4766710
Interacting Multi-Behavior Occupant Model for Indoor Environment in Residential Apartment BuildingsPreprint
Kim, S., & Park, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4811411
Influence of Subjective Factors on Window Use in Maternity Hospitals in Spring
Xiao, M., Deng, W., Ma, H., Wu, J., Zhou, T., Zhu, J., Wang, Y., & Pan, S. (2024) · Sustainability · doi:10.3390/su16229808
Approche ontologique du jumeau numérique pour la maintenance énergétique des bâtiments
Calixte, M., Rahhal, A., & Leclercq, P. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202420301002
Wang, Y., Lu, M. Z., Zhang, S., Zhu, J., & Zhang, L. (2025) · Research Square · doi:10.21203/rs.3.rs-7548356/v1
Zhao, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8750-3_30
Beiglary, S., & Wang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5800167
Baik, Y. K., & Kim, S. (2025) · 한국생활환경학회지 · doi:10.21086/ksles.2025.12.32.6.685
MOON, J. W., Kim, A. Y., & Kim, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6115835
Personalized Environmental Control Systems for lighting and daylighting: a systematic reviewPreprint
Khanie, M. S., Luna-Navarro, A., de la Barra, P., Martinez-Alcaraz, P., Barrett, E. M., Assaad, D. A., Chinazzo, G., Deng, Z., Dong, B., Vásquez, N. G., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6248840
MOON, J. W., Kim, A. Y., & Kim, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6451166
MOON, J. W., Kim, A. Y., & Kim, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6451167
A deep reinforcement learning framework for multi-dimensional adaptability in building operations
Schaumann, D., Yan, Z., Haiman, E., & Kalay, Y. E. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.115993
Martinez-Alcaraz, P., de la Barra, P., Andriotis, C. P., Knaack, U., & Luna-Navarro, A. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114588
MOON, J. W., Kim, A. Y., & Kim, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6537185
Boudourides, M. A., & Savvidou, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6543076
Computer vision to advance the sensing and control of net-zero transition in buildings
Wang, J., Han, R., & Huang, L. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34164-9.00006-x
Hashaykeh, H., Alsobeh, A., Shatnawi, A., & Darwish, O. (2026) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc69565.2026.11542294
An AI-Driven Affective Lighting System for Stress Reduction and Satisfaction in Office Environments
Nikookar, N., & Sawyer, A. O. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671608009
A Markov chain-based method for interactive individual occupancy status forecastingPreprint
Shen, G., Malkawi, A. M., & Li, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7050860
A Gaussian Process-based method for interactive individual occupant comfort modelingPreprint
Shen, G., Malkawi, A. M., & Li, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7050861
Contrastive Preference Learning From User Overrides for Personalized Home Energy Management
Du, X. Z., Luo, F., Lan, J., Zhou, W. G., & Wen, J. (2026) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2026.3711278
“At the other end of the spectrum, (4) implicit behavioral signals infer preferences from users’ natu-ral interactions; for example, [12] learns personalized lighting setpoints from occupant switch actions via RL, [11] uses Bayesian inference to learn thermal comfort–cost trade-o ff s from thermostat…”
Ten questions concerning the future of intelligent buildings
Ghaffarianhoseini, A., Omrany, H., GhaffarianHoseini, A., Cooper, I., Daemei, A. B., Rahimian, F. P., Chang, S., Powell, K. M., El-Haouzi, H. B., AlWaer, H., et al. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115189
Casino, S. C., Chu, Y., & Grinham, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7477833
Oh, S., Park, J. Y., Jeong, Y.-S., & Kim, Y.-K. (2026) · Energy · doi:10.1016/j.energy.2026.142465
Self-citationEliminating Cold Start in Smart Home Lighting Control via Digital Twin-Trained Q-Learning
Sladcik, T., & Habiballa, H. (2026) · Automation · doi:10.3390/automation7050148
“In recent years, modern systems have been heavily oriented toward combining not only machine and reinforcement learning, but also fuzzy logic [38].”
Liu, J., & Wang, H. (2026) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1109/aann71145.2026.11709206
“Reinforcement-learning lighting research optimizes comfort or lighting quality, mainly in offices or non-mobility settings [12] , [13] , while building-control studies highlight the sim-to-real barrier [14] .”
Ribino, P., Mannone, M., Paragliola, G., & Napoli, C. D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-40774-0_39
Zareshahamati, M., del Blanco, F., & Barrera, M. D. M. B. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118391
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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