Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow
Author
Abstract
Suggested Citation
DOI: 10.31219/osf.io/g8p4f
Download full text from publisher
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Saiz, Albert & Salazar-Miranda, Arianna, 2023. "Understanding Urban Economies, Land Use, and Social Dynamics in the City: Big Data and Measurement," IZA Discussion Papers 16501, Institute of Labor Economics (IZA).
- Miguel Ángel Rodríguez López & Diego Rodríguez Rodríguez, 2024. "La aplicación de datos masivos en economía de la energía: una revisión," Working Papers 2024-08, FEDEA.
- Yi Bao & Zhou Huang & Han Wang & Ganmin Yin & Xiao Zhou & Yong Gao, 2023. "High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 350-361, February.
- Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
- Mayer, Kevin & Haas, Lukas & Huang, Tianyuan & Bernabé-Moreno, Juan & Rajagopal, Ram & Fischer, Martin, 2023. "Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data," Applied Energy, Elsevier, vol. 333(C).
- Diana M Nova Díaz & Aritz Adin & Eduardo Sánchez Iriso, 2024. "QALYs in adults with cerebral palsy: Mapping from the San Martin Scale onto the EQ-5D-5L instrument," Working Papers 2024-07, FEDEA.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-06-20 (Big Data)
- NEP-ENE-2022-06-20 (Energy Economics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:g8p4f. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.