LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning
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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.
“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]
Many building systems can adjust operation based on occupancy, improving occupant comfort and energy use.
“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]
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“These uncertainties can influence the performance of RL algorithms which is strongly related to the data quality [45,46].”
BackgroundOccupant-Facade interaction: a review and classification scheme
Luna-Navarro, A., Loonen, R., Juaristi, M., Monge-Barrio, A., Attia, S., & Overend, M. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106880 · Cited by 74 · see citing works
“Ia Control Action • Give occupants personal control [161] • Allow users to override automated strategies • Increases user acceptance of logic [15], [162][163] • Provides data on overrides which can be used to train Logic [1] Potential detrimental effect on energy performance of automated systems [164] Manually control…”
BackgroundInfluentialTen questions concerning human-building interaction research for improving the quality of life
Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Berges, M. E., Billington, S. L., Borić-Lubecke, O., Ghahramani, A., Heydarian, A., Jazizadeh, F., et al. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109681 · Cited by 75 · see citing works
“Examples include personalization of thermal comfort [59–61]and visual comfort [62,63], personalized air delivery systems [64,65], and personalized sleep quality systems [66].”
BackgroundShuvo, S. S., & Yılmaz, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3158814 · Cited by 68 · see citing works
“Park et al. [30] provide theory and implementation for adaptive and occupant-centered lighting optimization in an office setup.”
BackgroundA review on the current usage of machine learning tools for daylighting design and control
Ngarambe, J., Adilkhanova, I., Uwiragiye, B., & Yun, G. Y. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109507 · Cited by 65 · see citing works
Artificial Intelligence in the NHS: Climate and Emissions✰,✰✰
Bloomfield, P. S., Clutton-Brock, P., Pencheon, E., Magnusson, J., & Karpathakis, K. (2021) · The Journal of Climate Change and Health · doi:10.1016/j.joclim.2021.100056 · Cited by 71 · see citing works
Coraci, D., Brandi, S., & Capozzoli, A. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117303 · Cited by 63 · see citing works
Moadab, N. H., Olsson, T., Fischl, G., & Aries, M. M. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111009 · Cited by 62 · see citing works
Multi-criteria design methods in façade engineering: State-of-the-art and future trends
Bianchi, S., Andriotis, C. P., Klein, T., & Overend, M. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111184 · Cited by 61 · see citing works
Kim, H., Kang, H., Choi, H., Jung, D., & Hong, T. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104938 · Cited by 63 · see citing works
Mahmoudzadeh, P., Afacan, Y., & Adi, M. N. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107823 · Cited by 59 · see citing works
A Review on energy consumption optimization techniques in IoT based smart building environments
Shah, A. S., Nasir, H., Fayyaz, M., Lajis, A., & Shah, A. (2019) · The International Islamic University Malaysia Repository (The International Islamic University Malaysia) · Cited by 58 · see citing works
Rampini, L., & Cecconi, F. R. (2022) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2022.043 · Cited by 64 · see citing works
Heidari, A., Marechal, F. M. A., & Khovalyg, D. M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119206 · Cited by 57 · see citing works
Li, P., Lu, Y., Yan, D., Xiao, J., & Wu, H. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103776 · Cited by 56 · see citing works
Wang, X., Kang, X., An, J., Chen, H., & Yan, D. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113696 · Cited by 55 · see citing works
Operation scheduling in a solar thermal system: A reinforcement learning-based framework
Correa-Jullian, C., Droguett, E. L., & Cardemil, J. M. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114943 · Cited by 55 · see citing works
Kar, P., Shareef, A., Kumar, A., Harn, K. T., Kalluri, B., & Panda, S. K. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.01.035 · Cited by 53 · see citing works
Brandi, S., Gallo, A., & Capozzoli, A. (2022) · Energy Reports · doi:10.1016/j.egyr.2021.12.058 · Cited by 52 · see citing works
Wang, J., Jiang, L., Yu, H., Feng, Z., Castaño-Rosa, R., & Cao, S. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114165 · Cited by 52 · see citing works
Reinforcement Learning in Construction Engineering and Management: A Review
Asghari, V., Wang, Y., Biglari, A. J., Hsu, S., & Tang, P. (2022) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002386 · Cited by 49 · see citing works
Yu, H., Tam, V. W. Y., & Xu, X. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114189 · Cited by 42 · see citing works
Long-term experimental evaluation and comparison of advanced controls for HVAC systems
Wang, X., & Dong, B. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123706 · Cited by 41 · see citing works
Platform design for lifelog-based smart lighting control
Cho, Y.-J., Seo, J., Lee, H., Choi, S., Choi, A.-S., Sung, M., & Hur, Y. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107267 · Cited by 44 · see citing works
An agent-based simulation of occupancy schedule in office buildings
Norouziasl, S., Jafari, A., & Wang, C. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107352 · Cited by 40 · see citing works
The reinforcement learning method for occupant behavior in building control: A review
Han, M., Zhao, J., Zhang, X., Shen, J., & Li, Y. (2020) · Energy and Built Environment · doi:10.1016/j.enbenv.2020.08.005 · Cited by 41 · see citing works
Heidari, A., Marechal, F. M. A., & Khovalyg, D. M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118833 · Cited by 39 · see citing works
Mora, H., Peral, J., Ferrández, A., Gil, D., & Szymański, J. (2019) · IEEE Access · doi:10.1109/access.2019.2914613 · Cited by 38 · see citing works
Nweye, K., & Nagy, Z. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118980 · Cited by 37 · see citing works
Self-citationTamas, R., Ouf, M. M., & O’Brien, W. (2019) · Architectural Science Review · doi:10.1080/00038628.2019.1695573 · Cited by 35 · see citing works
“Occupant-centric control strategies can address these issues as they rely onmachine learning algorithms to derive useful information about occupancy patterns and preferences to continuously optimize control operations, with the ultimate goal of improving both occupant comfort and energy efficiency (Park et al. 2019).”
BackgroundMa, G., & Pan, X. (2021) · Sustainability · doi:10.3390/su13147602 · Cited by 34 · see citing works
“During the operation of smart buildings, some studies have emphasized personalized comfort [19].”
BackgroundChallenges and opportunities of machine learning control in building operations
Zhang, L., Chen, Z., Zhang, X., Pertzborn, A. J., & Jin, X. (2023) · Building Simulation · doi:10.1007/s12273-023-0984-6 · Cited by 33 · see citing works
“A few studies use real buildings to validate the controller trained by simulation (Kazmi et al. 2018; Zhang and Lam 2018; Park et al. 2019a; Han et al. 2020), which has many advantages.”
BackgroundMachine Learning Applications for Smart Building Energy Utilization: A Survey
Huotari, M., Malhi, A. K., & Främling, K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10054-7 · Cited by 33 · see citing works
Wang, W., Hong, T., Xu, N., Xu, X., Chen, J., & Shan, X. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106280 · Cited by 33 · see citing works
“applied LightLearn method that learns the individual occupant behaviors with reinforcement learning algorithm to build occupant centered control based lighting system for energy saving [33].”
MethodA simulation-based method to investigate occupant-centric controls
Ouf, M. M., Park, J. Y., & Gunay, B. (2020) · Building Simulation · doi:10.1007/s12273-020-0726-y · Cited by 33 · see citing works
“Many of the early studies mainly focused on concept papers and small-scale experiments that lasted for only short periods (typically a few days to three months) (Park et al. 2019b).”
BackgroundMethodInfluentialSelf-citationA review of current research on occupant-centric control for improving comfort and energy efficiency
Yuan, Y., Song, C., Gao, L., Zeng, K., & Chen, Y. (2024) · Building Simulation · doi:10.1007/s12273-024-1170-1 · Cited by 32 · see citing works
“Over the past decade, research into OB has been vigorous (He et al. 2019; Park et al. 2019b), with considerable studies on OB modeling and model development (Yan et al. 2017; Chen et al. 2023; Deng et al. 2023).”
Reinforcement Learning: Theory and Applications in HEMS
Al-Ani, O., & Das, S. (2022) · Energies · doi:10.3390/en15176392 · Cited by 34 · see citing works
“EV, ES, and RG [122,123] Mixed/NA [124] Other Residential [125,126] Other/Mixed”
Rethinking the limitations of research on occupants’ window-opening behavior: A review
Liu, Y., Chong, W. T., Yau, Y. H., Chang, L., Cui, T., Yu, H., Cui, Y., & Pan, S. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112552 · Cited by 32 · see citing works
Park, J. Y., & Nagy, Z. (2020) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3396851.3402364 · Cited by 31 · see citing works
“For the building control problems, an RL mechanism is particularly suitable due to its model-free learning for complex building systems and interactive nature to consider human behavior actively [3, 4].”
BackgroundSelf-citationOCCUPIED: Long-term field experiment results from an occupant-centric control in an office building
Jiang, Z., O’Neill, Z. D., & Dong, B. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113435 · Cited by 30 · see citing works
Concepts of user-centred lighting controls for office applications: A systematic literature review
Hammes, S., Geisler‐Moroder, D., Hauer, M., Weninger, J., Obleitner, M., Miller, J., Plörer, D., van Karsbergen, V., Weitlaner, R., & Pfluger, R. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111321 · Cited by 34 · see citing works
A hierarchical multi-purpose roller shade controller to enhance indoor comfort and energy efficiency
Tabadkani, A., Haddadi, M., Rizi, R. A., & Tabadkani, E. (2023) · Building Simulation · doi:10.1007/s12273-023-1003-7 · Cited by 30 · see citing works
“…by 99% for the occupant across the year which is significantly greater than previous studies such as (Iwata et al. 2017; Kunwar et al. 2019a; Park et al. 2019a) since the artificial lighting was a trade-off between visual comfort (minimizing the discomfort glare and provision of partial…”
BackgroundMethodHawila, A. A.-W., Diallo, T. M. O., & Collignan, B. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110525 · Cited by 28 · see citing works
Norouziasl, S., & Jafari, A. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106590 · Cited by 28 · see citing works
Assessments of users' interactions with energy-efficient solutions: A systematic review
Ekim, Z. E., Mattsson, P., & Bernardo, R. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110522 · Cited by 30 · see citing works
Day-ahead prediction of plug-in loads using a long short-term memory neural network
Markovic, R., Azar, E., Annaqeeb, M. K., Frisch, J., & van Treeck, C. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110667 · Cited by 26 · see citing works
Occupant-centered indoor environmental quality management: Physiological response measuring methods
Kong, M., An, J., Jung, D., & Hong, T. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110661 · Cited by 27 · see citing works
Franzoso, A., Fambri, G., & Badami, M. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.120095 · Cited by 27 · see citing works
Hahn, J., Heiler, S., Kane, M. B., Park, S., & Jensch, W. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.838859 · Cited by 26 · see citing works
Zhu, H., Lian, X., Liu, Y., Zhang, Y., & Li, Z. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112720 · Cited by 26 · see citing works
Heidari, A., & Khovalyg, D. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106310 · Cited by 25 · see citing works
Chen, J., An, J., Yan, D., & Zhou, X. (2024) · Building Simulation · doi:10.1007/s12273-024-1209-3 · Cited by 24 · see citing works
“Park et al. (2019) introduced LightLearn, a lighting controller based on reinforcement learning and centered on room occupancy.”
Luna-Navarro, A., Fidler, P. R. A., Law, A., Torres, S., & Overend, M. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107656 · Cited by 24 · see citing works
Lee, M. J., & Zhang, R. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5632 · Cited by 24 · see citing works
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · Complexity · doi:10.1155/2022/4989344 · Cited by 24 · see citing works
“Park et al. [12] used a light sensor and actuators such as Switchmate.”
InfluentialA reinforcement learning approach for thermostat setpoint preference learning
Elehwany, H., Ouf, M. M., Gunay, B., Cotrufo, N., & Venne, J.-S. (2023) · Building Simulation · doi:10.1007/s12273-023-1056-7 · Cited by 22 · see citing works
“This led to the rise of occupant-centric controls (OCC), where the system utilizes data from occupants, and indoor and outdoor environments to predict occupancy patterns or occupant preferences (Park et al. 2019b; O’Brien et al. 2020).”
A workflow for evaluating occupant-centric controls using building simulation
Hobson, B. W., Huchuk, B., Gunay, B., & O’Brien, W. (2021) · Journal of Building Performance Simulation · doi:10.1080/19401493.2021.1894485 · Cited by 22 · see citing works
“Park et al. (2019b) provide a comprehensive list of these implementations to date, which may serve as a starting point for evaluating the performance gap in future work.”
BackgroundMethodInfluentialPotential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach
Suman, S., Etemad, A., & Rivest, F. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3127483 · Cited by 21 · see citing works
“In [20], value iteration, a reward-based algorithm, was used to learn user preferences about lighting, conditional on the number of occupants, time of day, and surrounding illumination.”
Li, N., Luo, X., Xing, M., Liu, J., Gao, Y., Zhou, T., Zhang, M., Li, J., & Liu, Y. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131142 · Cited by 21 · see citing works
Kharvari, F., & Rostami-Moez, M. (2021) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2021.02.001 · Cited by 20 · see citing works
Trending machine learning models in cyber‐physical building environment: A survey
Hasan, Z., & Roy, N. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1422 · Cited by 20 · see citing works
“Park, Dougherty, et al. (2019) proposed an occupant centered controller system named LightLearn .”
BackgroundCarbonare, N., Pflug, T., Bongs, C., & Wagner, A. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108380 · Cited by 19 · see citing works
Jang, S., Baik, Y. K., & Kim, S. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111322 · Cited by 19 · see citing works
Heidari, A., Girardin, L., Dorsaz, C., & Marechal, F. M. A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124815 · Cited by 19 · see citing works
Moshari, A., Javanroodi, K., & Nik, V. M. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126997 · Cited by 18 · see citing works
- 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 · 2018: 1
- 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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