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Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images

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  • Mao, Hongzhi
  • Chen, Xie
  • Luo, Yongqiang
  • Deng, Jie
  • Tian, Zhiyong
  • Yu, Jinghua
  • Xiao, Yimin
  • Fan, Jianhua

Abstract

Solar photovoltaic (PV) system, as one kind of the most promising renewable energy technologies, plays a key role in reducing carbon emissions to achieve the targets of global net zero carbon. In the past few decades, PV installations have seen a rapid growth. Predicting the installed amount and the capacity of solar PV systems is therefore useful for formulating effective carbon reduction policies in the related area. In the present study, the methods of identifying PV installation based on satellite and aerial images have been reviewed. Suggestions have been put forward to optimize the identification process and to predict the potential of rooftop PV installation. The results show that the specific purposes of PV identification can be categorized as image classification, object detection and semantic segmentation. The available identification methods encompass pixel-based analysis method (PBIA), object-based analysis method (OBIA) and deep learning. Deep learning has a high accuracy in segmentation for all sizes of PV systems, with precision and recall of rooftop PV segmentation in the range of 41–98.9% and 54.5–95.8%, respectively. OBIA has the best accuracy in detecting centralized PV systems with relatively low-resolution multispectral images. Furthermore, a grading segmentation strategy for PV segmentation in the large region is presented, combining the three identification methods and the images with different resolutions. In addition, the potential of rooftop PV installation can be predicted by segmenting the available roof area in the images. After considering the shading effects, upper structure and other uses, the roof availability coefficient tends to be in the range of 0.25–0.46. It is also suggested to combine PV and roof segmentation to estimate the installation potential more accurately, in the context of rapid growth of the rooftop PV.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:179:y:2023:i:c:s1364032123001326
    DOI: 10.1016/j.rser.2023.113276
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    1. Gaviria, Jorge Felipe & Narváez, Gabriel & Guillen, Camilo & Giraldo, Luis Felipe & Bressan, Michael, 2022. "Machine learning in photovoltaic systems: A review," Renewable Energy, Elsevier, vol. 196(C), pages 298-318.
    2. Smriti Mallapaty, 2020. "How China could be carbon neutral by mid-century," Nature, Nature, vol. 586(7830), pages 482-483, October.
    3. L. Kruitwagen & K. T. Story & J. Friedrich & L. Byers & S. Skillman & C. Hepburn, 2021. "A global inventory of photovoltaic solar energy generating units," Nature, Nature, vol. 598(7882), pages 604-610, October.
    4. Siddharth Joshi & Shivika Mittal & Paul Holloway & Priyadarshi Ramprasad Shukla & Brian Ó Gallachóir & James Glynn, 2021. "High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. 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).
    6. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    7. Khan, Jibran & Arsalan, Mudassar Hassan, 2016. "Estimation of rooftop solar photovoltaic potential using geo-spatial techniques: A perspective from planned neighborhood of Karachi – Pakistan," Renewable Energy, Elsevier, vol. 90(C), pages 188-203.
    8. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    9. Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    15. 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.
    16. Wang, Yu & He, Jijiang & Chen, Wenying, 2021. "Distributed solar photovoltaic development potential and a roadmap at the city level in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    17. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    18. 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.
    19. 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.
    20. Gang He & Jiang Lin & Froylan Sifuentes & Xu Liu & Nikit Abhyankar & Amol Phadke, 2020. "Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    21. Abdulaziz Alhammad & Qian (Chayn) Sun & Yaguang Tao, 2022. "Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study," Energies, MDPI, vol. 15(1), pages 1-21, January.
    22. Ko, Li & Wang, Jen-Chun & Chen, Chia-Yon & Tsai, Hsing-Yeh, 2015. "Evaluation of the development potential of rooftop solar photovoltaic in Taiwan," Renewable Energy, Elsevier, vol. 76(C), pages 582-595.
    23. Bódis, Katalin & Kougias, Ioannis & Jäger-Waldau, Arnulf & Taylor, Nigel & Szabó, Sándor, 2019. "A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    24. 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).
    25. 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.
    26. Zhang, Libo & Chen, Changqi & Wang, Qunwei & Zhou, Dequn, 2021. "The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China," Energy, Elsevier, vol. 232(C).
    27. 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).
    28. Gang He & Jiang Lin & Froylan Sifuentes & Xu Liu & Nikit Abhyankar & Amol Phadke, 2020. "Author Correction: Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
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