A city-scale estimation of rooftop solar photovoltaic potential based on deep learning

Teng Zhong, Zhixin Zhang, Min Chen, Kai Zhang, Zixuan Zhou, Rui Zhu, Yijie Wang, Guonian Lü and Jinyue Yan

Applied Energy, 2021, vol. 298, issue C, No S0306261921005729

Abstract: The estimation of rooftop solar photovoltaic (PV) potential is crucial for policymaking around sustainable energy plans. But it is difficult to accurately estimate the availability of rooftop area for solar radiation on a city-scale. In this study, a generic framework for estimating the rooftop solar PV potential on a city-scale using publicly available high-resolution satellite images is proposed. A deep learning-based method is developed to extract the rooftop area with image semantic segmentation automatically. A spatial optimization sampling strategy is developed to solve the labor-intensive problem when training the rooftop extraction model based on prior knowledge of urban and rural spatial layout and land use. In the case study of Nanjing, China, the labor cost on preparing the dataset for training the rooftop extraction model has been reduced by about 80% with the proposed spatial optimization sampling strategy. Meanwhile, the robustness of the rooftop extraction model in districts with different architectural styles and land use has been improved. The total rooftop area extracted was 330.36 km2, and the overall accuracy reached 0.92. The estimation results show that Nanjing has significant potential for rooftop-mounted PV installations, and the potential installed capacity reached 66 GW. The annual rooftop solar PV potential was approximately 311,853 GWh, with a corresponding estimated power generation of 49,897 GWh in 2019.

Keywords: rooftop solar photovoltaic (PV) potential; geographic information systems (GIS); Deep learning; Sampling strategy; City-scale (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921005729
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:298:y:2021:i:c:s0306261921005729

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.2021.117132

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 ().