Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow
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DOI: 10.31219/osf.io/g8p4f
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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).
- 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.
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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)
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