Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures

Qingyu Li, Sebastian Krapf, Lichao Mou, Yilei Shi and Xiao Xiang Zhu

Applied Energy, 2024, vol. 374, issue C, No S0306261924012224

Abstract: Solar energy is an environmentally friendly energy source. Identifying suitable rooftops for solar panel installation contributes to not only sustainable energy plans but also carbon neutrality goals. Aerial imagery, bolstered by its growing availability, is a cost-effective data source for rooftop solar potential assessment at large scale. Existing studies generally do not take roof superstructures into account when determining how many solar panels can be installed. This procedure will lead to an overestimation of solar potential. Only several works have considered this issue, but none have devised a network that can simultaneously learn roof orientations and roof superstructures. Therefore, we devise SolarNet+, a novel framework to improve the precision of rooftop solar potential estimation. After implementing SolarNet+ on a benchmark dataset, we find that SolarNet+ outperforms other state-of-the-art approaches in both tasks — roof orientations and roof superstructure segmentation. Moreover, the SolarNet+ framework enables rooftop solar estimation at large-scale applications for investigating the correlation between urban rooftop solar potential and various local climate zone (LCZ) types. The results in the city of Brussels reveal that three specific LCZ urban types exhibit the highest rooftop solar potential efficiency: compact highrise (LCZ1), compact midrise (LCZ2), and heavy industry (LCZ10). The annual photovoltaic potential for these LCZ types is reported as 10.56 GWh/year/km2, 11.77 GWh/year/km2, and 10.70 GWh/year/km2, respectively.

Keywords: Convolutional neural network; Remote sensing; Roof superstructure; Solar potential; Local climate zone (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924012224
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:374:y:2024:i:c:s0306261924012224

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123839

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().