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 | 06006 | |
| Number of page(s) | 9 | |
| Section | Generative AI in the Sustainable Built Environments | |
| DOI | https://doi.org/10.1051/e3sconf/202671606006 | |
| Published online | 09 June 2026 | |
- World Meteorological Organization (WMO) (2024) State of the Global Climate 2024. [Google Scholar]
- United Nations Environment Programme (2025) (2025) Emissions Gap Report 2025: Off target - Continued collective inaction puts global temperature goal at risk, 2025. [Google Scholar]
- International Energy Agency (IEA) (2023) World Energy Outlook 2023, 2023. [Google Scholar]
- Maket, I. (2024) Rethinking energy poverty alleviation through financial inclusion: Do institutional quality and climate change risk matter? Util Policy 91: 101820. [Google Scholar]
- Gupta, R., Mathur, J., Garg, V. (2023) Assessment of climate classification methodologies used in building energy efficiency sector. Energy Build 298: 113549. [Google Scholar]
- P. Tootkaboni, Ballarini, I., Corrado, V. (2021) Analysing the future energy performance of residential buildings in the most populated Italian climatic zone: A study of climate change impacts. Energy Reports 7: 8548–8560. [Google Scholar]
- Raissi, M., Perdikaris, P., Karniadakis, G.E. (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378: 686–707. [Google Scholar]
- Stasi, R., Semeraro, S., Ruggiero, F., et al. (2026) Optimizing Energy Efficiency in Historical Buildings Using Model Predictive Control for Building Automation and Control Systems. 523–531. [Google Scholar]
- Chen, Y., Yang, Q., Chen, Z., et al. (2023) Physics- informed neural networks for building thermal modeling and demand response control. Build Environ 234. [Google Scholar]
- Pereira, P., Ramos, N.M.M., Zhao, X., et al. (2022) Digital Twins in Built Environments: An Investigation of the Characteristics, Applications, and Challenges. Buildings 2022, Vol 12, Page 120 12: 120. [CrossRef] [Google Scholar]
- Semeraro, S., Vecchi, F., Stasi, R., et al. (2025) Physics- Informed Neural Networks for predicting indoor temperature and cooling demand in historic buildings. Journal of Building Engineering 115: 114–392. [Google Scholar]
- Urban, J.F., Stefanou, P., Pons, J.A. (2025) Unveiling the optimization process of physics informed neural networks: How accurate and competitive can PINNs be? J Comput Phys 523: 113–656. [Google Scholar]
- Stasi, R., Ruggiero, F., Berardi, U. (2025) From energyintensive buildings to NetPlus targets: An innovative solar exoskeleton for the energy retrofitting of existing buildings. Energy Build 333. [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.

