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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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Items 101–200 of 237
Doukari, O., Seck, E. H. B., Greenwood, D. J., Feng, H., & Kassem, M. (2022) · Buildings · doi:10.3390/buildings12030362 · Cited by 18
“Building behaviour simulations are not only based on BIM model and real-time data but also the building historical use, users’ behaviour when interacting with the building and their feedback and preferences towards energy advice given by the smart system through automatic energy settings or notifications over BIM dash…”
MethodAbuimara, T., Hobson, B. W., Gunay, B., & O’Brien, W. (2022) · Building Services Engineering Research and Technology · doi:10.1177/01436244211069655 · Cited by 18
Park, J. Y., Nweye, K., Mbata, E., & Nagy, Z. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109040 · Cited by 18
Self-citationAsghari, V., Biglari, A. J., & Hsu, S. (2022) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-4883 · Cited by 19
An ontology to represent synthetic building occupant characteristics and behavior
Putra, H. C., Hong, T., & Andrews, C. J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103621 · Cited by 18
“Advancement in lighting controllers, LightLearn, has been proposed to consider occupant behavior and preferences on the illuminance level [37], [52]”
BackgroundFritz, H., Bastami, S., Lin, C., Nweye, K., To, T. V., Chen, L., Le, D., Ibarra, A., Zhang, W., Park, J. Y., et al. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109432 · Cited by 17
Self-citationA Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption
Krishnan, D., Kelly, S., & Kim, Y. (2022) · Energies · doi:10.3390/en15031219 · Cited by 18
Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand
Weerasinghe, A. S., Rasheed, E. O., & Rotimi, J. O. B. (2023) · Sustainability · doi:10.3390/su15032305 · Cited by 17
Tabadkani, A., Dehnavi, A. N., Mostafavi, F., & Naeini, H. G. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106118 · Cited by 16
“Based on Reinforcement learning, Park et al. [47] introduced an occupant-centered lighting controller, which was experimented in five offices during eight weeks.”
MethodAli, A. S., Riley, C., Acton, E., Ali, A., Heidarinejad, M., & Stephens, B. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111344 · Cited by 16
“Increasingly, we now see highly customized control applications using such development platforms, such as controlling heat transfer for informing better heat exchanger designs [10] and demonstrating adaptive occupant-centered lighting control based on reinforcement learning [11].”
BackgroundA Systematic Review of Sensing Technology in Human-Building Interaction Research
Ji, W., Yang, L., Liu, Z., & Feng, S. (2023) · Buildings · doi:10.3390/buildings13030691 · Cited by 15
A repository of occupant-centric control case studies: Survey development and database overview
Lorenz, C.-L., André, M., Abele, O., Gunay, B., Hahn, J., Hensen, P., Nagy, Z., Ouf, M. M., Park, J. Y., Yaduvanshi, N. S., et al. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113649 · Cited by 15
Self-citationAbdolhosseini, M., & Abdollahi, R. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3353917 · Cited by 15
“For example, in [19], to adjust the operation of the lighting control system, statistical information should be received from users, and after analysis, it should be used to train the control system.”
BackgroundMethodHuynh, T. N. Y., Nguyen, A. T., Ahn, Y., Oo, B. L., & Lim, B. T. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116045 · Cited by 16
Reinforcement Learning-Enabled Adaptive Control for Climate-Responsive Kinetic Building Facades
Li, Z., Tian, J., Ji, G., Cheng, T. J., Loftness, V., & Xu, H. (2025) · Buildings · doi:10.3390/buildings15162977 · Cited by 13
Wang, Q., & Dong, J. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111811 · Cited by 16
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Lovillo, R. M. L., Domínguez-Amarillo, S., Sendra, J. J., & Acosta, I. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105704 · Cited by 12
Yaduvanshi, N. S., & Park, J. Y. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107629 · Cited by 12
Self-citationReinforcement learning for occupant behavior modeling in public buildings: Why, what and how?
Yu, H., & Xu, X. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110491 · Cited by 12
Hammes, S., Geisler‐Moroder, D., Weninger, J., Zech, P., & Pfluger, R. (2024) · Developments in the Built Environment · doi:10.1016/j.dibe.2024.100526 · Cited by 12
A Data-Driven Personalized Lighting Recommender System
Zarindast, A., & Wood, J. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.706117 · Cited by 12
“Park et al. (2019) introduced a reinforcement learning based control system for of fi ce location that is based on occupancy.”
BackgroundThe Application of Reinforcement Learning to Pumps—A Systematic Literature Review
Aribisala, A. A., Ghori, U. A. S., & Cavalcante, C. A. V. (2025) · Machines · doi:10.3390/machines13060480 · Cited by 11
“Because DR systems require human feedback to accurately simulate the RL’s reward function, more research is needed to develop models that imitate human activity or to deploy RL in a real-world setting where occupants can provide feedback to help the RL agent to follow the best policies [154].”
Mandarić, K., Kešelj, A., & Ježić, G. (2024) · Sensors · doi:10.3390/s24061764 · Cited by 10
“Park et al. [34] describe a reinforcement learning-based system for lighting that balances two factors, human comfort and energy savings, with the goal of achieving energy savings without neglecting and sacrificing user comfort.”
BackgroundEvaluating the impact sequences of operation have on the implementation of occupant-centric controls
Hobson, B. W., & Gunay, B. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112121 · Cited by 10
Toward Personalization of User Preferences in Partially Observable Smart Home Environments
Suman, S., Rivest, F., & Etemad, A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3178065 · Cited by 10
“With the development of the Internet of Things, it has become much easier to control and automate various devices, such as thermostats, speakers, voice assistants [2], [18], [21], and many other devices with the overall aim of improving the experience of occupants in the home.”
Franzoso, A., Fambri, G., & Badami, M. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.120963 · Cited by 10
Yuan, Y., Song, C., Zeng, K., Gao, L., Huang, Y. L., & Chen, Y. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.111925 · Cited by 8
Designing a Fuzzy Q-Learning Power Energy System Using Reinforcement Learning
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Yin, L., & Xiong, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123348 · Cited by 8
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Karyono, K., Abdullah, B. M., Cotgrave, A., & Brás, A. (2020) · Buildings · doi:10.3390/buildings10090168 · Cited by 9
“One of the AI methods is an Artificial Neural Network (ANN) to simplify the model of parameter tuning [29] and the use of reinforcement learning to gain knowledge on the schedule-based and occupancy-based control scenarios and use it to control lighting [30].”
MethodSensing systems for smart building occupant-centric operation
Chu, Y., & Cetin, K. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-817784-6.00025-4 · Cited by 8
Sensorless Air Flow Control in an HVAC System through Deep Learning
Son, J., & Kim, H. (2019) · Applied Sciences · doi:10.3390/app9163293 · Cited by 8
“[12] applied reinforcement learning to a general room detection method to improve its performance.”
MethodKim, D. H., Jeon, S. H., & Sung, J.-S. (2024) · Sustainability · doi:10.3390/su16125054 · Cited by 7
Chen, Z., Xing, T., Wang, Y., Zhuang, Y., Zheng, M., Zhao, Q., & Jia, Q. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125851 · Cited by 7
Mohsenpour, M., & Xing, Y. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127392 · Cited by 7
Bhandari, N., Faheem, M., Tadepalli, S., & Gopalakrishnan, P. (2023) · Energy and Built Environment · doi:10.1016/j.enbenv.2023.10.002 · Cited by 7
Predictability quantification of occupant presence in high-rise residential apartment buildings
Kim, S. H., & Park, C. S. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112494 · Cited by 7
Development of algorithms for building energy efficiency
Serale, G., Fiorentini, M., & Noussan, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819946-6.00011-4 · Cited by 8
Net-Zero Home Race: A Board Game to Engage and Educate Students for Sustainable Building Retrofits
Yaduvanshi, N. S., Payne, T., & Park, J. Y. (2025) · Journal of Architectural Engineering · doi:10.1061/jaeied.aeeng-2062 · Cited by 6
Self-citationChen, P., Wang, L., Wu, Y., Liang, Z., Yu, J., & Chen, T. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113575 · Cited by 10
Park, J. Y., Mbata, E., & Nagy, Z. (2019) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3360322.3360869 · Cited by 6
“In addition, occupancy information is also necessary to develop occupant-centered controllers, which aim at balancing occupant comfort and energy efficiency [5].”
BackgroundMethodSelf-citationIntelligent Personalized Lighting Control System for Residents
Zhang, J., Chen, Z., Wang, A., Li, Z., & Wan, W. (2023) · Sustainability · doi:10.3390/su152115355 · Cited by 6
Park, J. Y., & Nagy, Z. (2019) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784482445.013 · Cited by 6
Self-citationZhang, S., Su, H., Wu, M., & Yang, B. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112954 · Cited by 8
A Multi-Agent Approach to Energy Optimisation for Demand-Response Ready Buildings
Kem, O., & Ksontini, F. (2020) · Springer eBooks · doi:10.1007/978-3-030-42726-9_4 · Cited by 5
On the simulation of occupant-centric control for building operations
Ouf, M. M., Park, J. Y., & Gunay, B. (2021) · Journal of Building Performance Simulation · doi:10.1080/19401493.2021.2001622 · Cited by 5
“Furthermore, inadequately short test periods and, sometimes, the lack of adequate submetering prevent reliable measurement and verification of the effectiveness of OCC solutions (Park et al. 2019).”
BackgroundMethodInfluentialSelf-citationAsghari, V., Jahanbiglari, A., & Hsu, S. (2025) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2607 · Cited by 4
Zhu, H., Lian, X., Li, Z., & He, B. (2022) · Buildings · doi:10.3390/buildings12122043 · Cited by 4
“2019 USA Office lighting Whether the room is occupied, switch position, indoor light levels, period of day The total energy use, light-utilization ratio, unmet comfort hours, light–comfort ratio, the daily relative change of each value [11] 1”
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Reinforcement Learning Methodologies for Controlling Occupant Comfort in Buildings
Han, M., May, R., & Zhang, X. (2021) · Sustainable development goals series · doi:10.1007/978-981-16-2778-1_9 · Cited by 5
Occupancy inference using infrastructure elements in indoor environment: a multi-sensor data fusion
Trivedi, D., Badarla, V., & Bhandari, R. B. (2023) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-023-00130-z · Cited by 5
“In this technology, occupancy is detected through the light-emitting diode (LED) (Yang et al. 2018) and lighting infrastructure (Park et al. 2019).”
BackgroundArchitecting net zero: from drawings to bytes
Sajjadian, S. M. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110094 · Cited by 4
Topology-aware hypergraph reinforcement learning for indoor occupant-centric HVAC control
Zhong, D., Xing, T., Sun, K., Zhang, Z., Zhao, Q., & Kang, J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116219 · Cited by 4
A dialectical system framework for building occupant energy behavior
Yang, M., Yu, H., & Xu, X. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115649 · Cited by 3
Ten questions concerning participant engagement in building energy research
Outcault, S. M., Johns, D., Morgan, G. T., Park, J. Y., Black, J. R., Searl, E., Stopps, H., Touchie, M. F., & Zaniboni, L. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114452 · Cited by 3
Self-citationPredictability and periodicity of occupant lighting control in high-rise residential buildings
Kim, J. H., Cho, S., & Park, C. S. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113489 · Cited by 3
The Internet of Things-Enabled Smart City: An In-Depth Review of Its Domains and Applications
Meydani, A., Ramezani, A., & Meidani, A. (2023) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke60553.2023.10326308 · Cited by 3
“An RL agent, named LightLearn, for managing the brightness of office spaces is presented in [58].”
BackgroundA DQN-Based Coordination Method of HVAC and Energy Storage for Building Energy Management
Wang, S., Chen, X., Bu, L., Bo, W., Yu, K., & He, D. (2023) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei259745.2023.10512933 · Cited by 3
A Smart Adaptive Lighting System for a Multifunctional Room
Karyono, K., Abdullah, B. M., Cotgrave, A., & Brás, A. (2019) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2019.00184 · Cited by 3
A data-driven personalized smart lighting recommender systemPreprint
Zarindast, A., Wood, J., & Sharma, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02164 · Cited by 3
“Park et al. [31] introduced a reinforcement learning based control system for office location that is based on occupancy.”
BackgroundLiterature review - Energy saving potential of user-centered integrated lighting solutions
Altomonte, S., Naves, C., Ciampi, G., Garcia-Hansen, V., Gentile, N., Gkaintatzi-Masouti, M., Lee, E. S., MASKARENJ, M., Osterhaus, W., Obradovic, B., et al. (2021) · doi:10.18777/ieashc-task61-2021-0013 · Cited by 3
Ning, C., Shen, D., Dong, Y., Wang, Y., & Duan, P. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112683 · Cited by 2
Liang, Z., Bian, D., Xu, C., Zhang, X., Shi, D., & Wang, Z. (2021) · North American Power Symposium (NAPS) · doi:10.1109/naps50074.2021.9449817 · Cited by 2
“An occupant centered controller for lighting in CBs based on RL is proposed in [4].”
MethodEvaluation of a new user-centric sensor-based lighting control approach for circulation areas
Karaman-Madan, Ö., & Pekeriçli, M. K. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2042/1/012123 · Cited by 2
Design of Sequences of Operation for Occupant-Centric Controls
Gunay, B., Hobson, B. W., Ouf, M. M., Nagy, Z., & Miller, C. C. (2023) · Routledge eBooks · doi:10.1201/9781003176985-10 · Cited by 2
Self-citationSu, K., Wang, C., & Xiang, J. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111587 · Cited by 2
Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users
Bilésimo, T. L., & Ghisi, E. (2024) · Sustainability · doi:10.3390/su16104258 · Cited by 2
Hossain, A., Mostafa, S. M. G., Zilani, M. A. K., Talukder, O. I., Pranta, M. R., & Fahim, J. A. (2025) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce64574.2025.11013236 · Cited by 2
“To turn on the lights, a light sensor must detect illumination that is less than a predetermined threshold [18, 19].”
Demand Response Optimization for Mircogrid Clusters with Deep Reinforcement Learning
Sinha, A., Sawant, M., Kochar, H., Abhija, A., Seth, R., Sornagattu, P. R., & Vyas, O. P. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9579612 · Cited by 2
“Further examination is needed to join actual human input, either preparing substitute models addressing human interference or carrying out RL in a testing environment where user can give their feedback and RL can adjust likewise [12].”
BackgroundReinforcement learning control for indoor comfort: a survey
May, R., Zhang, X., Wu, J., & Han, M. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/609/6/062011 · Cited by 2
Haddam, N., Boulakia, B. C., & Barth, D. (2020) · International Conference on Smart Grid and Smart Cities (ICSGSC) · doi:10.1109/icsgsc50906.2020.9248550 · Cited by 2
“above, the system can be seen as the learning agent and the user as (part of) the environment [41] [25].”
BackgroundMethodToward Responsible Design of Low-Carbon Buildings: From Concept to Engineering
Kalluri, B., Chockalingam, S., Alibrandi, U., Asmone, A. S., & Manthapuri, S. (2022) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0001216 · Cited by 2
Xue, K. (2020) · Educational Data Mining · Cited by 2
Fuzzy Reinforcement Learning Multi-agent System for Comfort and Energy Management in Buildings
Kofinas, P., Dounis, A. I., & Korkidis, P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2380-6_26 · Cited by 1
Zulramadhanie, Z., Windarta, J., & Cahyadi, C. (2024) · Jurnal Teknologi Terapan G-Tech · doi:10.33379/gtech.v8i3.4446 · Cited by 1
Zhang, H. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02679-z · Cited by 1
A Comparison Between Indoor Daylight Prediction Methods: Daysim Simulation and LSTM Models
Testasecca, T., Ribino, P., Potenza, G., Baglivo, C., Beccali, M., & Bonomolo, M. (2025) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic/icpseurope64998.2025.11169147 · Cited by 1
Designing Efficient Hybrid Controls for High-performance buildings with Multiple Energy Systems
Bak, J., Liu, X., Alkhalil, M., James, K., Mohaidat, S., Alsaleem, F., Hempel, M., Sharif-Kashani, H., & Alahmad, M. A. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3140/5/052009 · Cited by 1
“The majority of RL research has focused on managing individual energy systems such as HVAC [1], distributed energy resources (DER) [2], and lighting [3], etc., with system-specific control actions and problem formulations.”
Elehwany, H., Markus, A. A., Gunay, B., Ouf, M. M., Cotrufo, N., Venne, J.-S., & Wen, J. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117030 · Cited by 1
Islam, M. S., Norouziasl, S., & Jafari, A. (2026) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486443.037 · Cited by 1
Chen, E. X., Malkawi, A. M., Samuelson, H. W., Shen, G., & Li, N. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117337 · Cited by 2
Bardy, B. G. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1800319 · Cited by 1
Yun, J. Y., Moon, J. W., Bae, K. W., Kim, J. K., Lee, H. W., Kim, H. I., Sung, J.-H., & Kim, M. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114673 · Cited by 2
Scalable physics-informed multi-agent reinforcement learning for building energy system control
Wang, X., Ren, Z., Li, N., & Dong, B. (2026) · Advances in Applied Energy · doi:10.1016/j.adapen.2026.100285 · Cited by 1
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“In the lighting domain, LightLearn [28] demonstrated an occupant-centered RL controller that adapts to individual preferences to balance comfort and energy.”
Whole-process AI in building applications: A framework of integrating AI with domain knowledge
Yan, D., Dong, B., O’Neill, Z. D., Chen, Z., Wang, X., Jiang, Z., Song, R., Kang, X., & Zhou, X. (2026) · Building Simulation · doi:10.1007/s12273-026-1487-z · Cited by 1
“Park et al. (2019) introduced an occupant-centric controller for lighting based on reinforcement learning.”
A Multilevel Approach for Smart Buildings Management
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Adhikari, R., Gautam, Y., Jebelli, H., & Sitzabee, W. E. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485262.020 · Cited by 1
The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review
Sarmas, E., Marinakis, V., & Doukas, H. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-85209-1_2 · Cited by 1
Zheng, Y., Xiao, X., Cai, Y., Gao, Z., Ye, Y., Li, H., & Wang, Z. (2025) · IET Renewable Power Generation · doi:10.1049/rpg2.70035 · Cited by 1
Kem, O., & Ksontini, F. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3352545 · Cited by 1
“Some are based on artificial neural network [3, 9], multi-agent system [7], distributed model predictive [11], ant colony optimisation [5], and reinforcement learning [10], to name a few.”
BackgroundOn the possibilities of applying machine learning in lighting control systems
Рослякова, С. В., Брагина, Т. В., Землянова, Е. И., Короткова, Д. В., & Меркулова, П. А. (2021) · Research Result Information Technologies · doi:10.18413/2518-1092-2021-6-4-0-7 · Cited by 1
Shuvo, S. S., & Yılmaz, Y. (2021) · doi:10.36227/techrxiv.16574711.v1 · Cited by 1
Shuvo, S. S., & Yılmaz, Y. (2021) · doi:10.36227/techrxiv.16574711 · Cited by 1
Multi-Behavior of Occupants: Modeling and Interdependency QuantificationPreprint
Kim, S.-H., & Park, C. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4600054 · Cited by 1
Adaptive circadian rhythm a cognitive approach through dynamic light management
Maganty, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819443-0.00007-6
Office Occupancy Detection based on Power Meters and BLE Beaconing
Pratama, A. R. (2020) · doi:10.33612/diss.147276967
“Previous works have shown that occupancy detection based on the localization systems with identification leads to users’ comfort and energy saving, both lighting and Heating, Ventilation, and Air Conditioning (HVAC) systems [84, 144, 90, 14].”
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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