EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 396 (2023) E3S Web of Conf., 396 (2023) 03008 References
Open Access
Issue
E3S Web of Conf.
Volume 396, 2023
The 11th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC2023)
Article Number 03008
Number of page(s) 8
Section Energy Efficient and Healthy HVAC systems
DOI https://doi.org/10.1051/e3sconf/202339603008
Published online 16 June 2023
  1. J. Kim, S. Schiavon, and G. Brager, “Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control,” Build. Environ., vol. 132, no. January, pp. 114–124, 2018, doi: 10.1016/j.buildenv.2018.01.023. [CrossRef] [Google Scholar]
  2. M. André, R. De Vecchi, and R. Lamberts, “User-centered environmental control: a review of current findings on personal conditioning systems and personal comfort models,” Energy Build., vol. 222, 2020, doi: 10.1016/j.enbuild.2020.110011. [Google Scholar]
  3. IEA EBC, “IEA EBC - Annex 87 - Energy and Indoor Environmental Quality Performance of Personalised Environmental Control Systems,” 2021. https://annex87.iea-ebc.org/. [Google Scholar]
  4. J. Verhaart, R. Li, and W. Zeiler, “User interaction patterns of a personal cooling system: A measurement study,” Sci. Technol. Built Environ., vol. 24, no. 1, pp. 57–72, 2018, doi: 10.1080/23744731.2017.1333365. [CrossRef] [Google Scholar]
  5. K. Katić, R. Li, and W. Zeiler, “Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants’ heating behavior,” Appl. Ergon., vol. 85, 2020, doi: 10.1016/j.apergo.2020.103078. [Google Scholar]
  6. A. Aryal and B. Becerik-Gerber, “Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods,” Build. Environ., vol. 185, no. August, p. 107316, 2020, doi: 10.1016/j.buildenv.2020.107316. [CrossRef] [Google Scholar]
  7. C. Yu et al., “Performances of machine learning algorithms for individual thermal comfort prediction based on data from professional and practical settings,” J. Build. Eng., vol. 61, no. June, p. 105278, 2022, doi: 10.1016/j.jobe.2022.105278. [CrossRef] [Google Scholar]
  8. Q. Matias;, S. Schiavon, T. Federico, K. Joyce, and M. Clayton, “Cohort comfort models - Using occupant’s similarity to predict personal thermal preference with less data,” Build. Environ., 2022, doi: 10.1016/j.buildenv.2022.109685. [Google Scholar]
  9. B. Kazanci, Ongun et al., “Development and initial testing of a Personalized Environmental Control System (PECS),” 2022, doi: doi.org/10.34641/clima.2022.239. [Google Scholar]
  10. ASHRAE, “ASHRAE 55-2017 Thermal Environmental Conditions for Human Occupancy.” 2017. [Google Scholar]
  11. F. Jazizadeh, F. M. Marin, and B. Becerik-Gerber, “A thermal preference scale for personalized comfort profile identification via participatory sensing,” Build. Environ., vol. 68, pp. 140–149, 2013, doi: 10.1016/j.buildenv.2013.06.011. [CrossRef] [Google Scholar]
  12. F. Jazizadeh, A. Ghahramani, B. Becerik-Gerber, T. Kichkaylo, and M. Orosz, “User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings,” Energy Build., vol. 70, pp. 398–410, 2014, doi: 10.1016/j.enbuild.2013.11.066. [CrossRef] [Google Scholar]
  13. J. J. Aguilera, O. B. Kazanci, and J. Toftum, “Thermal adaptation in occupant-driven HVAC control,” J. Build. Eng., vol. 25, no. February, p. 100846, 2019, doi: 10.1016/j.jobe.2019.100846. [CrossRef] [Google Scholar]
  14. J. Shinoda, D.-I. Bogatu, B. Kazanci, Ongun, F. Watanabe, Y. Kaneko, and W. Olesen, Bjarne, “Occupant Feedback and Control Behavior with a Newly Developed Personalized Environmental Control System (PECS),” 2023. [Google Scholar]
  15. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [MathSciNet] [Google Scholar]
  16. T. Herlau, M. N. Schmidt, and M. Mørup, Introduction to Machine Learning and Data Mining. 2021. [Google Scholar]
  17. T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman,” Int. Stat. Rev., vol. 77, no. 3, pp. 482–482, 2009, doi: 10.1111/j.1751-5823.2009.00095_18.x. [CrossRef] [Google Scholar]
  18. C. Dai, H. Zhang, E. Arens, and Z. Lian, “Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions,” Build. Environ., vol. 114, pp. 1–10, 2017, doi: 10.1016/j.buildenv.2016.12.005. [CrossRef] [Google Scholar]
  19. F. Tartarini and S. Schiavon, “pythermalcomfort: A Python package for thermal comfort research.” p. 100578, 2020, doi: https://doi.org/10.1016/j.softx.2020.100578. [Google Scholar]
  20. CEN, “EN ISO 7730:2006 - Ergonomics of the thermal environment - Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria,” 2006. [Google Scholar]
  21. A. Ghahramani, C. Tang, and B. Becerik-Gerber, “An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling,” Build. Environ., vol. 92, pp. 86–96, 2015, doi: 10.1016/j.buildenv.2015.04.017. [CrossRef] [Google Scholar]
  22. J. Kim, Y. Zhou, S. Schiavon, P. Raftery, and G. Brager, “Personal comfort models: Predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning,” Build. Environ., vol. 129, no. December 2017, pp. 96–106, 2018, doi: 10.1016/j.buildenv.2017.12.011. [CrossRef] [Google Scholar]
  23. E. Laftchiev and D. Nikovski, “An IoT system to estimate personal thermal comfort,” 2016 IEEE 3rd World Forum Internet Things, WF-IoT 2016, pp. 672–677, 2017, doi: 10.1109/WF-IoT.2016.7845401. [Google Scholar]
  24. J. Kim, S. Schiavon, and G. Brager, “Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control,” Build. Environ., vol. 132, no. November 2017, pp. 114–124, 2018, doi: 10.1016/j.buildenv.2018.01.023. [Google Scholar]
  25. D.-I. Bogatu, O. B. Kazanci, F. Watanabe, Y. Kaneko, and W. Olesen, Bjarne, “Personal comfort model for automatic control of personal comfort systems,” 2022. [Google Scholar]
  26. P. O. Fanger, Thermal comfort: analysis and applications in environmental engineering. New York, NY, USA: McGraw-Hill, 1970. [Google Scholar]
  27. K. Katić, R. Li, and W. Zeiler, “Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants’ heating behavior,” Appl. Ergon., vol. 85, no. February, 2020, doi: 10.1016/j.apergo.2020.103078. [Google Scholar]
  28. A. Aryal and B. Becerik-Gerber, “Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods,” Build. Environ., vol. 185, no. September, p. 107316, 2020, doi: 10.1016/j.buildenv.2020.107316. [CrossRef] [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.