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 | 03019 | |
| Number of page(s) | 5 | |
| Section | Thermal Comfort | |
| DOI | https://doi.org/10.1051/e3sconf/202671603019 | |
| Published online | 09 June 2026 | |
- L. Van Thillo, S. Verbeke, A. Audenaert, The potential of building automation and control systems to lower the energy demand in residential buildings: A review of their performance and influencing parameters. Renewable and Sustainable Energy Reviews 158, 112099 (2022). [Google Scholar]
- H. Huang, B.R. Hughes, Review of HVAC forecasting and control strategies for improved building performance. Building and Environment, 113797 (2025). [Google Scholar]
- Z.K. Zadeh, M. Ouf, B. Gunay, B. Delcroix, G.L. Martin, A. Daoud, Development of prediction models for thermostat override behavior in direct load control events. Energy and Buildings 301, 113707 (2023). [Google Scholar]
- D.F. Espejel-Blanco, J.A. Hoyo-Montano, J. Arau, G. Valencia-Palomo, A. Garcia-Barrientos, H.R. Hernandez-De-Leon, J.L. Camas-Anzueto, HVAC control system using predicted mean vote index for energy savings in buildings. Buildings 12(1), 38 (2022). [Google Scholar]
- S. Shahzad, J. Brennan, D. Theodossopoulos, J.K. Calautit, B.R. Hughes, Does a neutral thermal sensation determine thermal comfort? Build. Serv. Eng. Res. Technol. 39(2), 183-195 (2018). [Google Scholar]
- J. Kim, Y. Zhou, S. Schiavon, P. Raftery, G. Brager, Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning. Building and Environment 129, 96-106 (2018). [Google Scholar]
- Z. Deng, Q. Chen, Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort. Energy and Buildings 174, 587-602 (2018). [Google Scholar]
- J.Y. Park, M.M. Ouf, B. Gunay, Y. Peng, W. O'Brien, M.B. Kjærgaard, Z. Nagy, A critical review of field implementations of occupant-centric building controls. Building and Environment 165, 106351 (2019). [Google Scholar]
- Z. Wang, J. Wang, Y. He, Y. Liu, B. Lin, T. Hong, Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning. Journal of Building Engineering 29, 101120 (2020). [Google Scholar]
- A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information. Physical Review E 69(6), 066138 (2004). [Google Scholar]
- T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623-2631 (2019). [Google Scholar]
- M. Schweiker, A. Wagner, The effect of occupancy on perceived control, neutral temperature, and behavioral patterns. Energy and Buildings 117, 246259 (2016). [Google Scholar]
- N.D. Sintov, L.V. White, H. Walpole, Thermostat wars? The roles of gender and thermal comfort negotiations in household energy use behavior. PLoS One 14(11), e0224198 (2019). [Google Scholar]
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