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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 [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]
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“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
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“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].”
BackgroundState-of-the-art on research and applications of machine learning in the building life cycle
Hong, T., Wang, Z., Luo, X., & Zhang, W. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.109831 · Cited by 334
“…for building control RC model and regression [97], [111], [112], [113] RC model and Generic Algorithm [114] Lighting control RL: value based [115] Window control RL: value based [116] Thermal Energy Storage control Non-linear programming [117] RL: value based [118] RL: actor critic [119],…”
BackgroundO’Brien, W., Wagner, A., Schweiker, M., Mahdavi, A., Day, J. K., Kjærgaard, M. B., Carlucci, S., Dong, B., Tahmasebi, F., Yan, D., et al. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106738 · Cited by 345
“In contrast, Nagy, Yong and Schlueter [107] updated learned user preferences for indoor illuminance only when there was a light switch-on event.”
BackgroundInfluentialSelf-citationAn overview of machine learning applications for smart buildings
Alanne, K., & Sierla, S. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103445 · Cited by 313
“The majority of the works reviewed in this article fail to account for human behaviour; however, Park et al. (2019) incorporate the occupant ’ s manipulation of the light switch into the training of the reinforcement learning agent and Cho et al. (2020) extensively track the activity, movements,…”
BackgroundA critical review of field implementations of occupant-centric building controls
Park, J. Y., Ouf, M. M., Gunay, B., Peng, Y., O’Brien, W., Kjærgaard, M. B., & Nagy, Z. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106351 · Cited by 292
Self-citationModeling occupant behavior in buildings
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
A review of reinforcement learning methodologies for controlling occupant comfort in buildings
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Maddalena, E. T., Lian, Y., & Jones, C. N. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104211 · Cited by 169
“An additional class of methods that has being explored in the field of building control is reinforcement learning [Henze and Schoenmann, 2003, Yu and Dexter, 2010, de Gracia et al., 2015, Park et al., 2019].”
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
Shah, A. S., Nasir, H., Fayaz, M., Lajis, A., & Shah, A. (2019) · Information · doi:10.3390/info10030108 · Cited by 169
“Park et al., in [58] introduced a reinforcement learning based occupant centered controller for the lighting.”
BackgroundChen, B., Cai, Z., & Berges, M. E. (2019) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3360322.3360849 · Cited by 138
“It should be noted, though, that in the broader scope of building systems there are real-world applications of RL in systems other than HVAC, including water heaters [21] and lighting systems [36].”
BackgroundTen questions concerning reinforcement learning for building energy management
Nagy, Z., Henze, G. P., Dey, S., Arroyo, J., Helsen, L., Zhang, X., Chen, B., Amasyali, K., Kurte, K. R., Zamzam, A. S., et al. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110435 · Cited by 144
Self-citationHumans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models
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Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“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].”
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A Global Building Occupant Behavior Database
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Self-citationA Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment
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“The applications of the human-centric approach vary from the building controls of lighting (Park et al., 2019a) and HVAC (Gunay et al., 2018) as well as larger infrastructure systems (Dargin and Mostafavi, 2020).”
Self-citationLiu, X., & Gou, Z. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111197 · Cited by 86
Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Berges, M. E., Billington, S. L., Borić-Lubecke, O., Ghahramani, A., Heydarian, A., Hölscher, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25047-y · Cited by 84
Ten questions concerning occupant-centric control and operations
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Self-citationReview and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications
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Occupant-centered real-time control of indoor temperature using deep learning algorithms
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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
“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
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“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].”
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“Park et al. [30] provide theory and implementation for adaptive and occupant-centered lighting optimization in an office setup.”
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Operation scheduling in a solar thermal system: A reinforcement learning-based framework
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An agent-based simulation of occupancy schedule in office buildings
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The reinforcement learning method for occupant behavior in building control: A review
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“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
“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
“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
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
“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
“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
“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
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“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
Park, J. Y., & Nagy, Z. (2020) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3396851.3402364 · Cited by 31
“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
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
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
“…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 31
Norouziasl, S., & Jafari, A. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106590 · Cited by 28
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
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
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
Franzoso, A., Fambri, G., & Badami, M. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.120095 · Cited by 27
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Chen, J., An, J., Yan, D., & Zhou, X. (2024) · Building Simulation · doi:10.1007/s12273-024-1209-3 · Cited by 24
“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
Lee, M. J., & Zhang, R. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5632 · Cited by 24
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · Complexity · doi:10.1155/2022/4989344 · Cited by 27
“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
“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
“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
“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
Kharvari, F., & Rostami-Moez, M. (2021) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2021.02.001 · Cited by 20
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
“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
Jang, S., Baik, Y. K., & Kim, S. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111322 · Cited by 19
Heidari, A., Girardin, L., Dorsaz, C., & Marechal, F. M. A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124815 · Cited by 22
Moshari, A., Javanroodi, K., & Nik, V. M. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126997 · Cited by 18
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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