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 06005
Number of page(s) 6
Section Generative AI in the Sustainable Built Environments
DOI https://doi.org/10.1051/e3sconf/202671606005
Published online 09 June 2026
  1. Flagner, S.; Schiavon, S.; Kok, N.; Fuerst, F.; Licina, D.; Loder, A.; Rahman, S.A.; Scheer, F.A.J.L.; Wang, L.; Weeldreyer, G.; et al. Ten Questions Concerning the Economics of Indoor Environmental Quality in Buildings. Build Environ 2025, 282, 113227, doi:10.1016/j.buildenv.2025.113227. [Google Scholar]
  2. Barrett, P.; Zhang, Y.; Moffat, J.; Kobbacy, K. A. Holistic, Multi-Level Analysis Identifying the Impact of Classroom Design on Pupils’ Learning. Build Environ 2013, 59, 678–689, doi:10.1016/j.buildenv.2012.09.016. [Google Scholar]
  3. Miri, M.; Faubel, C.; Demarquet Alban, U.; Martinez-Molina, A. Impact of Indoor Environmental Quality on Students’ Attention and Relaxation Levels During Lecture-Based Instruction. Buildings 2025, 15, 2813, doi:10.3390/buildings15162813. [Google Scholar]
  4. Zhao, Z.; Bagkeris, E.; Mumovic, D. The Combined Impacts of Indoor Temperature and Total Volatile Organic Compounds on Cognitive Performance of University Students: A Controlled Exposure Study. Science of the Total Environment 2025, 966, doi:10.1016/j.scitotenv.2025.178652. [Google Scholar]
  5. Mendell, M.J.; Heath, G.A. Do Indoor Pollutants and Thermal Conditions in Schools Influence Student Performance? A Critical Review of the Literature. Indoor Air 2005, 15, 27–52. [Google Scholar]
  6. Satish, U.; Mendell, M.J.; Shekhar, K.; Hotchi, T.; Sullivan, D.; Streufert, S.; Fisk, W.J. Is CO2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2 Concentrations on Human Decision-Making Performance. Environ Health Perspect 2012, 120, 1671–1677, doi:10.1289/ehp.1104789. [CrossRef] [PubMed] [Google Scholar]
  7. Sharma, V.; Dave, T.; Wani, F.A.; Mathur, J.; Mathur, S. Exploring the Influence of Indoor Temperature on Thermal Comfort and Performance. Sci Technol Built Environ 2025, 31, 466–483, doi: 10.1080/23744731.2024.2444822. [Google Scholar]
  8. Faubel, C.; Arvanitidis, A.I.; Iskandar, L.; Martinez- Molina, A.; Alamaniotis, M. Comparative Analysis of Artificial Intelligence Models for Real-Time and Future Forecasting of Environmental Conditions: A Wood-Frame Historic Building Case Study. Journal of Building Engineering 2024, 98, 111474, doi: 10.1016/jjobe.2024.111474. [CrossRef] [Google Scholar]
  9. Arvanitidis, A.I.; Faubel, C.; Martinez-Molina, A.; Alamaniotis, M. Comparative Analysis of Artificial Intelligence Models for HVAC System Optimization in UNESCO Heritage Buildings. In Proceedings of the 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA); IEEE, July 17 2024; pp. 1–6. [Google Scholar]
  10. Lee, M.J.; Zhang, R. Multimodal Data Fusion and Deep Learning for Occupant-Centric Indoor Environmental Quality Classification. Journal of Computing in Civil Engineering 2025, 39, doi:10.1061/JCCEE5.CPENG-6249. [Google Scholar]
  11. ANSI/ASHRAE Standard 55; Thermal Environmental Conditions for Human Occupancy; ASHRAE: Peachtree Corners, GA, USA, 2023; [Google Scholar]
  12. Gabeur, V.; Sun, C.; Alahari, K.; Schmid, C. MultiModal Transformer for Video Retrieval. In Proceedings of the Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IV; Springer-Verlag: Berlin, Heidelberg, 2020; pp. 214–229. [Google Scholar]
  13. Kalajdjieski, J.; Zdravevski, E.; Corizzo, R.; Lameski, P.; Kalajdziski, S.; Pires, I.M.; Garcia, N.M.; Trajkovik, V. Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote Sens (Basel) 2020, 12, 4142, doi:10.3390/rs12244142. [Google Scholar]
  14. Sabiri, Y.; Houmaidi, W.; Bougrine, A.; Billah, S.E.M. Optimizing Indoor Environmental Quality in Smart Buildings Using Deep Learning. 2025. [Google Scholar]
  15. Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. 2021, doi:https://doi.org/10.48550/arXiv.2010.11929. [Google Scholar]

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