SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city

Guanglei Li, Guohao Wang, Tengqi Luo, Yuxiao Hu, Shouyuan Wu, Guanghui Gong, Chenchen Song, Zhiling Guo and Zhengguang Liu

Renewable Energy, 2024, vol. 237, issue PA

Abstract: Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source due to its ease of integration onto building rooftops, facades, and windows. For emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages satellite imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. SolarSAM segmented various building rooftops using text prompt-guided semantic segmentation during the process. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city's total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted for the BIPVs on different buildings; the levelized cost of electricity is 0.18–0.41 CNY/kWh, and the annual total carbon reduction is 7.08 × 107 T CO2. These findings demonstrated the model's performance and revealed the potential for BIPV power generation.

Keywords: Potential assessment; Building-integrated photovoltaic; Semantic segmentation; Satellite imagery (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124016288

DOI: 10.1016/j.renene.2024.121560

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