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Unsupervised azimuth estimation of solar arrays in low-resolution satellite imagery through semantic segmentation and Hough transform

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  • Edun, Ayobami S.
  • Perry, Kirsten
  • Harley, Joel B.
  • Deline, Chris

Abstract

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm.

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  • Edun, Ayobami S. & Perry, Kirsten & Harley, Joel B. & Deline, Chris, 2021. "Unsupervised azimuth estimation of solar arrays in low-resolution satellite imagery through semantic segmentation and Hough transform," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006905
    DOI: 10.1016/j.apenergy.2021.117273
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    Cited by:

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    2. 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).
    3. Mayer, Kevin & Rausch, Benjamin & Arlt, Marie-Louise & Gust, Gunther & Wang, Zhecheng & Neumann, Dirk & Rajagopal, Ram, 2022. "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D," Applied Energy, Elsevier, vol. 310(C).

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