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

Sebastian Krapf, Nils Kemmerzell, Syed Khawaja Haseeb Uddin, Manuel Hack Vázquez, Fabian Netzler and Markus Lienkamp
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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

Energies, 2021, vol. 14, issue 13, 1-22

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.

Keywords: photovoltaic economic potential; aerial images; deep learning; semantic segmentation; roof segments; roof superstructures; public data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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