An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images

Shuai Tian, Guoqiang Yang, Sihong Du, Dian Zhuang, Ke Zhu, Xin Zhou, Xing Jin, Yu Ye, Peixian Li and Xing Shi

Renewable Energy, 2024, vol. 224, issue C

Abstract: The large-scale use of solar energy is an important means of achieving carbon neutrality. In cities, large stocks of building roofs are ideal for photovoltaic (PV) installations. However, the difficulty in acquiring urban building stock data limits the assessment of urban roof PV potential. Therefore, an instance segmentation model was used to extract all the roofs and their corresponding deep features from high-resolution open-source satellite maps. Subsequently, a multi-round semi-supervised clustering process was proposed to classify similar roofs. Finally, the total urban roof area was evaluated, involving pitched roof area correction. The PV available area ratios of all roofs were estimated by sampling each roof cluster to obtain the available roof area for PV installation. The corresponding urban PV potential capacity and energy generation were calculated. The proposed method was applied and validated in the Yangpu District of Shanghai, China. The results showed that the total building roof area of Yangpu District was 11.16 km2, and the roof PV available area ratio (Ra s) varied between 0.4 and 0.92. The available roof area for PV installation was 7.46 km2. The PV installation area and capacity were 4.14 km2 and 913.74 MW, respectively. The annual PV energy production was 940.34 GW h.

Keywords: Urban solar potential; Photovoltaic; Deep learning; Semi-supervised learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:224:y:2024:i:c:s096014812400140x

DOI: 10.1016/j.renene.2024.120075

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