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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Author

Listed:
  • Sebastian Krapf

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

  • Nils Kemmerzell

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

  • Syed Khawaja Haseeb Uddin

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

  • Manuel Hack Vázquez

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

  • Fabian Netzler

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

  • Markus Lienkamp

    (Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany)

Abstract

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3800-:d:581325
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    References listed on IDEAS

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    3. 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.
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    6. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
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    8. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).

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