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

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. Therefor...

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Veröffentlicht in:Renewable energy 2024-04, Vol.224, p.120075, Article 120075
Hauptverfasser: Tian, Shuai, Yang, Guoqiang, Du, Sihong, Zhuang, Dian, Zhu, Ke, Zhou, Xin, Jin, Xing, Ye, Yu, Li, Peixian, Shi, Xing
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container_start_page 120075
container_title Renewable energy
container_volume 224
creator Tian, Shuai
Yang, Guoqiang
Du, Sihong
Zhuang, Dian
Zhu, Ke
Zhou, Xin
Jin, Xing
Ye, Yu
Li, Peixian
Shi, Xing
description 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. •Instance segmentation model was used for city-scale building roofs extraction.•A multi-rounds semi-supervised clustering process was proposed to classify roofs.•Potential roof PV capacity and energy production in Yangpu District were evaluated.
doi_str_mv 10.1016/j.renene.2024.120075
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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. 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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. 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subjects carbon
China
Deep learning
energy
Photovoltaic
satellites
Semi-supervised learning
solar energy
Urban solar potential
title An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images
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