@inproceedings{d5877832282541148ad340ad2359c38d,
title = "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities",
abstract = "This paper presents a novel 3D-GIS and deep learning integrated approach for high-accuracy rooftop solar energy potential characterization. Rooftop solar potential distribution is evaluated based on 3D-GIS-based irradiance modeling to consider adjacent building shading effects, and also based on available area identified by a deep learning technique. The case study results showed that the individual building solar potential reductions varied from 13.4% to 74.5%. Further analysis showed that simple addition of shading-induced reductions and availability-induced reductions tends to overestimate the actual reduction by up to 16%. This study reveals the mechanisms why such effects should be jointly considered. {\textcopyright} 2023 IBPSA. All rights reserved.",
keywords = "Rooftop solar energy, high-density city, building shading effect, geographic information system, deep learning",
author = "Haoshan Ren and Yongjun Sun and Yeling Zhang",
year = "2023",
month = sep,
doi = "10.26868/25222708.2023.1735",
language = "English",
isbn = "978-1-7750520-3-6",
series = "Building Simulation Conference Proceedings",
publisher = "International Building Performance Association (IBPSA)",
pages = "1298--1305",
booktitle = "Proceedings of Building Simulation 2023",
note = "18th International Building Performance Simulation Association Conference on Building Simulation (BS 2023), 18th IBPSA Conference on Building Simulation ; Conference date: 04-09-2023 Through 06-09-2023",
}