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Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image

Author

Listed:
  • Wenbo Cui

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Xiangang Peng

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Jinhao Yang

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Haoliang Yuan

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Loi Lei Lai

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m 2 , a physical potential of 25,186,181.7 m 2 , and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China.

Suggested Citation

  • Wenbo Cui & Xiangang Peng & Jinhao Yang & Haoliang Yuan & Loi Lei Lai, 2023. "Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6563-:d:1238214
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    References listed on IDEAS

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    1. Huang, Zhaojian & Mendis, Thushini & Xu, Shen, 2019. "Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 250(C), pages 283-291.
    2. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    3. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
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    1. Qi, Qingqing & Zhao, Jinghao & Tan, Zekun & Tao, Kejun & Zhang, Xiaoqing & Tian, Yajun, 2024. "Development assessment of regional rooftop photovoltaics based on remote sensing and deep learning," Applied Energy, Elsevier, vol. 375(C).
    2. Yu Xiao & Long Lin & Jun Ma & Maoqiang Bi, 2024. "Enhancing Rooftop Photovoltaic Segmentation Using Spatial Feature Reconstruction and Multi-Scale Feature Aggregation," Energies, MDPI, vol. 18(1), pages 1-19, December.

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