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Analysis and experimental validation of solar potential in urban roads using Google panorama images for solar electric vehicles

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  • Baek, Jieun
  • Kim, Minji

Abstract

In this study, Google panoramic images were used to analyze reductions in direct and diffuse solar radiation caused by surrounding structures on urban roads and to predict road-scale solar irradiance for estimating the energy generation of solar electric vehicles (SEVs). Hemispherical images were generated from panoramic views, and a U-Net model classified sky and non-sky regions. By overlaying the annual solar trajectory, reduction ratios for annual direct and diffuse solar radiation were calculated. Annual solar potentials were estimated using typical meteorological year (TMY) data. A case study in Nam-gu, Busan, showed average irradiance of 0.93 MW/m2, with the highest at 1.47 MW/m2 on elevated roads and the lowest at 0.01 MW/m2 in tree-covered areas. Roads near residential buildings exhibited higher shading losses, with up to 17 % greater loss in October and up to 1.7 km/kWp shorter daily driving range in September compared to non-residential roads. Driving experiments validated the irradiance predictions by measuring actual reductions caused by buildings, trees, and elevated structures. The findings of this study can guide the development of navigation systems for SEVs.

Suggested Citation

  • Baek, Jieun & Kim, Minji, 2025. "Analysis and experimental validation of solar potential in urban roads using Google panorama images for solar electric vehicles," Renewable Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:renene:v:252:y:2025:i:c:s0960148125011887
    DOI: 10.1016/j.renene.2025.123526
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    References listed on IDEAS

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    1. Kenji Araki & Yasuyuki Ota & Anju Maeda & Minoru Kumano & Kensuke Nishioka, 2023. "Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors," Energies, MDPI, vol. 16(8), pages 1-25, April.
    2. Suh, Jangwon, 2025. "Economic analysis of a solar roof as an optional extra to electric vehicles in Korea: A case study," Renewable Energy, Elsevier, vol. 239(C).
    3. Baek, Jieun & Choi, Yosoon, 2023. "Optimal installation and operation planning of parking spaces for solar-powered electric vehicles using hemispherical images," Renewable Energy, Elsevier, vol. 219(P1).
    4. Kim, Hanjin & Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2022. "A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea," Renewable Energy, Elsevier, vol. 190(C), pages 713-729.
    5. Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2024. "Energy-saving path planning navigation for solar-powered vehicles considering shadows," Renewable Energy, Elsevier, vol. 236(C).
    6. Kenji Araki & Yasuyuki Ota & Akira Nagaoka & Kensuke Nishioka, 2023. "3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System," Energies, MDPI, vol. 16(11), pages 1-20, May.
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    1. Yang, Wei & Wang, Aochong & Zhang, Guangyu & Zhang, Yan & Xu, Tingting & Liao, Meide, 2026. "Assessment of solar utilization potential for streetlights using street view imagery and deep learning: A case study in Hong Kong," Renewable Energy, Elsevier, vol. 258(C).

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