Solar energy
Combines 3D building footprints with meteorological data to estimate building-integrated PV potential in 120 cities worldwide; facade potential averages 68.2% of rooftop potential.
· Nexus
Solar energy
Integrates AIoT, GIS and meteorological data into a facade BIPV potential model that accounts for 3D occlusion; facade element parsing and shading turn out to be key to accurate estimates.
· Advances in Applied Energy
Solar energy
A deep learning framework that encodes spatiotemporal data to predict shadow casting and rooftop PV potential (annual MAPE 2.85%, shading mIoU 89.23%), about 158 times faster than simulation.
· Applied Energy
XU Jian, Zhiling Guo, Qing Yu, DONG Kechuan, TAN Hongjun, Haoran Zhang, Jinyue Yan (2025). Spatiotemporal feature encoded deep learning method for rooftop PV potential assessment. Applied Energy, 394, 126171.
Qing Yu, DONG Kechuan, Zhiling Guo, XU Jian, Jiaxing Li, TAN Hongjun, Yanxiu Jin, Jian Yuan, Haoran Zhang, Junwei Liu, Qi Chen, Jinyue Yan (2025). Global estimation of building-integrated facade and rooftop photovoltaic potential by integrating 3D building footprint and spatio-temporal datasets. Nexus, 2(2), 100060.
DONG Kechuan, Qing Yu, Zhiling Guo, XU Jian, TAN Hongjun, Haoran Zhang, Jinyue Yan (2025). Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy. Advances in Applied Energy, 17, 100212.
