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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

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  • Malof, Jordan M.
  • Bradbury, Kyle
  • Collins, Leslie M.
  • Newell, Richard G.

Abstract

The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV array location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135km2) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV array locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV array detection, but with significant potential for improvement in estimating the precise shape/size of the PV arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.

Suggested Citation

  • Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:229-240
    DOI: 10.1016/j.apenergy.2016.08.191
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    Cited by:

    1. Marcela Bindzarova Gergelova & Slavomir Labant & Stefan Kuzevic & Zofia Kuzevicova & Henrieta Pavolova, 2020. "Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    2. Marcus Vinícius Coelho Vieira da Costa & Osmar Luiz Ferreira de Carvalho & Alex Gois Orlandi & Issao Hirata & Anesmar Olino de Albuquerque & Felipe Vilarinho e Silva & Renato Fontes Guimarães & Robert, 2021. "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation," Energies, MDPI, vol. 14(10), pages 1-15, May.
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    5. Hu, Wei & Bradbury, Kyle & Malof, Jordan M. & Li, Boning & Huang, Bohao & Streltsov, Artem & Sydny Fujita, K. & Hoen, Ben, 2022. "What you get is not always what you see—pitfalls in solar array assessment using overhead imagery," Applied Energy, Elsevier, vol. 327(C).
    6. Tan, Hongjun & Guo, Zhiling & Zhang, Haoran & Chen, Qi & Lin, Zhenjia & Chen, Yuntian & Yan, Jinyue, 2023. "Enhancing PV panel segmentation in remote sensing images with constraint refinement modules," Applied Energy, Elsevier, vol. 350(C).
    7. Daxini, Rajiv & Wilson, Robin & Wu, Yupeng, 2023. "Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting," Energy, Elsevier, vol. 284(C).
    8. Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    9. Konstantinos Ioannou & Dimitrios Myronidis, 2021. "Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks," Sustainability, MDPI, vol. 13(9), pages 1-15, May.
    10. 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).
    11. Lu, Ning & Li, Liang & Qin, Jun, 2024. "PV Identifier: Extraction of small-scale distributed photovoltaics in complex environments from high spatial resolution remote sensing images," Applied Energy, Elsevier, vol. 365(C).
    12. Juan-Pablo Villegas-Ceballos & Mateo Rico-Garcia & Carlos Andres Ramos-Paja, 2022. "Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images," Data, MDPI, vol. 7(6), pages 1-12, June.
    13. Yin, Hui & Zhou, Kaile, 2022. "Performance evaluation of China's photovoltaic poverty alleviation project using machine learning and satellite images," Utilities Policy, Elsevier, vol. 76(C).
    14. 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).
    15. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
    16. Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    17. Fabio Giussani & Eric Wilczynski & Claudio Zandonella Callegher & Giovanni Dalle Nogare & Cristian Pozza & Antonio Novelli & Simon Pezzutto, 2024. "Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
    18. Yongshi Jie & Xianhua Ji & Anzhi Yue & Jingbo Chen & Yupeng Deng & Jing Chen & Yi Zhang, 2020. "Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification," Energies, MDPI, vol. 13(24), pages 1-19, December.
    19. Gabriel Kasmi & Augustin Touron & Philippe Blanc & Yves-Marie Saint-Drenan & Maxime Fortin & Laurent Dubus, 2024. "Remote-Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data," Energies, MDPI, vol. 17(17), pages 1-23, August.
    20. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. 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).
    22. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    23. Slavomir Labant & Patrik Petovsky & Pavel Sustek & Lubomir Leicher, 2024. "Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia," Land, MDPI, vol. 13(6), pages 1-18, June.

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