EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 716 (2026) E3S Web Conf., 716 (2026) 08009 References
Open Access
Issue
E3S Web Conf.
Volume 716, 2026
The 12th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC 2026)
Article Number 08009
Number of page(s) 8
Section Visual (Lighting and Daylighting) and Acoustic Quality
DOI https://doi.org/10.1051/e3sconf/202671608009
Published online 09 June 2026
  1. R. Sithravel, R. Ibrahim, M.S. Lye, E.K. Périmai, N. Ibrahim, and N.D. Dahlan, “Morning boost on individuals’ psychophysiological wellbeing indicators with supportive, dynamic lighting in windowless open-plan workplace in Malaysia,” PLOS ONE, vol. 13, no. 11, Nov.2018. [Google Scholar]
  2. R. Chen, M.-C. Tsai, and Y.-S. Tsay, “Effect of Color Temperature and Illuminance on Psychology, Physiology, and Productivity: An Experimental Study,” Energies, vol. 15, p. 4477, Jun. 2022. [Google Scholar]
  3. K. Van Den Wymelenberg, “Patterns of occupant interaction with window blinds: A literature review,” Energy Build., vol. 51, pp. 165–176, 2012. [Google Scholar]
  4. N. Nikookar, A.O. Sawyer, M. Goel, and S. Rockcastle, “Investigating the Impact of Combined Daylight and Electric Light on Human Perception of Indoor Spaces,” Sustainability, vol. 16, no. 9, p. 3691, Apr. 2024. [Google Scholar]
  5. J. Weninger, M. Canazei, and W. Pohl, “Effects of a personalizable workplace lighting concept on acceptance, usability, and cognitive performance,” in Lux Europa, Prague - Czech Republic, 2022. [Google Scholar]
  6. Z. Kong, Q. Liu, X. Li, K. Hou, and Q. Xing, “Indoor lighting effects on subjective impressions and mood states: A critical review,” Build. Environ., vol. 224, no. 2, 2022. [Google Scholar]
  7. O. Ayan and B. Turkay, “IoT-Based Energy Efficiency in Smart Homes by Smart Lighting Solutions,” in 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA), Bourgas, Bulgaria: IEEE, Jun. 2020, pp. 1–5. [Google Scholar]
  8. Y. Li, W. Fang, B. Guo, and H. Qiu, “Diurnal effects of dynamic lighting on alertness, cognition, and mood of mentally fatigued individuals in a daylight deprived environment,” Light. Res. Technol., vol. 56, no. 2, pp. 136–155, 2024. [Google Scholar]
  9. A.S. Dunn et al., “The Impact of Dynamic Lighting on Sleep Timing and Duration for Hospitalised Patients,” J. Sleep Res., Mar. 2025. [Google Scholar]
  10. M. Canazei et al., “Effects of adjustable dynamic bedroom lighting in a maternity ward,” J. Environ. Psychol., vol. 62, pp. 59–66, 2019. [Google Scholar]
  11. M. Belloni, L. Carrino, and E. Meschi, “The impact of working conditions on mental health: Novel evidence from the UK,” Labour Econ., vol. 76, 2022. [Google Scholar]
  12. B. Yu, J. Hu, M. Funk, R.H. Liang, M. Xue, and L. Feijs, “RESonance: Lightweight, Room-Scale Audio-Visual Biofeedback for Immersive Relaxation Training,” IEEE Access, vol. 6, pp. 38336–38347, 2018. [Google Scholar]
  13. B. Yu, J. Hu, M. Funk, and L. Feijs, “DeLight: biofeedback through ambient light for stress intervention and relaxation assistance,” Pers. Ubiquitous Comput., vol. 22, no. 4, 2018. [Google Scholar]
  14. D. Cupkova, E. Kajati, J. Mocnej, P Papcun, J. Koziorek, and I. Zolotova, “Intelligent human-centric lighting for mental wellbeing improvement,” Int. J. Distrib. Sens. Netw., vol. 15, no. 9, Sep. 2019. [Google Scholar]
  15. R. Shahidi, R. Golmohammadi, M. Babamiri, J. Faradmal, and M. Aliabadi, “Effect of warm/cool white lights on visual perception and mood in warm/cool color environments,” EXCLIJ, 2021. [Google Scholar]
  16. A. Kuijsters, J. Redi, B. De Ruyter, P. Seuntiëns, and I. Heynderickx, “Affective ambiences created with lighting for older people,” Light. Res. Technol., vol. 47, no. 7, pp. 859–875, 2015. [Google Scholar]
  17. Y.H. Kim et al., “MudGet: Reproduction of the desired lighting environment using a smart-LED,” J. Comput. Des. Eng., vol. 4, pp. 231–237, 2017. [Google Scholar]
  18. E.K. Hansen, T. Bjorner, E. Xylakis, and M. Pajuste, “An experiment of double dynamic lighting in an office responding to sky and daylight: Perceived effects on comfort, atmosphere and work engagement,” Indoor Built Environ., vol. 31, no. 2, pp. 355–374, 2022. [Google Scholar]
  19. M. Kwon, H. Remoy, A. van den Dobbelsteen, and U. Knaack, “Personal control and environmental user satisfaction in office buildings: Results of case studies in the Netherlands,” Build. Environ., vol. 149, no. October 2018, pp. 428–435, 2019. [Google Scholar]
  20. S. Chraibi, T. Lashina, P Shrubsole, M. Aries, E. Van Loenen, and A. Rosemann, “Satisfying light conditions: A field study on perception of consensus light in Dutch open office environments,” Build. Environ., vol. 105, pp. 116–127, 2016. [Google Scholar]
  21. K. Vashishtha, A. Saad, R. Faieghi, and F. Xi, “Intelligent adaptive lighting algorithm: Integrating reinforcement learning and fuzzy logic for personalized interior lighting,” Eng. Appl. Artif Intell., vol. 133, 2024. [Google Scholar]
  22. A. Almalaq, “Reinforcement Learning in Smart Building Management: A Paradigm Shift towards Intelligent Energy Efficiency and Optimization,” in 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC), Chicago, IL, USA: IEEE, Jun. 2024, pp. 1–6. [Google Scholar]
  23. J.Y. Park, T. Dougherty, H. Fritz, and Z. Nagy, “LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning,” Build. Environ., vol. 147, pp. 397–414, 2019. [CrossRef] [Google Scholar]
  24. Z. Cheng, Q. Zhao, F. Wang, Y Jiang, L. Xia, and J. Ding, “Satisfaction based Q-learning for integrated lighting and blind control,” Energy Build., vol. 127, pp. 43–55, 2016. [Google Scholar]
  25. X. Pan and B. Lee, “An Approach of Reinforcement Learning Based Lighting Control for Demand Response,” 2016. [Google Scholar]
  26. S. Karjalainen, “Should it be automatic or manual-The occupant’s perspective on the design of domestic control systems,” Energy Build., vol. 65, pp. 119–126, 2013. [Google Scholar]
  27. P Zhou et al., “Privacy-Preserving and Residential Context-Aware Online Learning for IoT-Enabled Energy Saving With Big Data Support in Smart Home Environment,” IEEE Internet Things J., vol. 6, no. 5, pp. 7450–7468, 2019. [Google Scholar]
  28. M.S. Altulayan, C. Huang, L. Yao, X. Wang, and S. Kanhere, “Contextual Bandit Learning for Activity-Aware Things-of-Interest Recommendation in an Assisted Living Environment,” in Databases Theory and Applications, vol. 12610, M. Qiao, G. Vossen, S. Wang, and L. Li, Eds., Cham: Springer International Publishing, 2021, pp. 37–49. [Google Scholar]
  29. X. Zhuang and C. Wu, “The effect of interactive feedback on attitude and behavior change in setting air conditioners in the workplace,” Energy Build., vol. 183, pp. 739–748, 2019. [Google Scholar]
  30. S. Saadatifar, A.O. Sawyer, and D. Byrne, “Occupant-Centric Digital Twin: A Case Study on Occupant Engagement in Thermal Comfort Decision-Making,” Architecture, vol. 4, no. 2, pp. 390–415, Jun. 2024. [Google Scholar]
  31. M.Â. Campano, I. Acosta, S. Dommguez, and R. Lopez-Lovillo, “Dynamic analysis of office lighting smart controls management based on user requirements,” Autom. Constr, vol. 133, Jan. 2022. [Google Scholar]
  32. Light and lighting. Lighting of work places. Part 1 Indoor work places. London: British Standards Institution, 2021. [Google Scholar]
  33. L. Li, W. Chu, J. Langford, and R.E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” in Proceedings of the 19th international conference on World wide web, Raleigh North Carolina USA: ACM, Apr. 2010, pp. 661–670. [Google Scholar]
  34. A. Kuijsters, J. Redi, B. De Ruyter, and I. Heynderickx, “Lighting to make you feel better: Improving the mood of elderly people with affective ambiences,” PLOS ONE, vol. 10, 2015. [Google Scholar]
  35. W. Kim, S. Lee, Y. Chang, T. Lee, I. Hwang, and J. Song, “Hivemind: social control-and-use of IoT towards democratization of public spaces,” in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, Virtual Event Wisconsin: ACM, Jun. 2021, pp. 467–482. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.