IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v219y2023ip1s0960148123013861.html
   My bibliography  Save this article

Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets

Author

Listed:
  • Guo, Zhiling
  • Zhuang, Zhan
  • Tan, Hongjun
  • Liu, Zhengguang
  • Li, Peiran
  • Lin, Zhengyuan
  • Shang, Wen-Long
  • Zhang, Haoran
  • Yan, Jinyue

Abstract

The widespread adoption of photovoltaic (PV) technology for renewable energy necessitates accurate segmentation of PV panels to estimate installation capacity. However, achieving highly efficient and precise segmentation methods remains a pressing challenge. Recent advancements in artificial intelligence and remote sensing techniques have shown promise in PV segmentation. Nevertheless, real-world scenarios introduce complexities such as diverse sensing platforms, sensors, panel categories, and testing regions. These factors contribute to resolution, size, and foreground-background class imbalances, impeding accurate and generalized PV panel segmentation over large areas. To address these challenges, we propose GenPV, a deep learning model that leverages data distribution analysis and PV panel characteristics to enhance segmentation accuracy and generalization. GenPV employs a multi-scale feature learning approach, utilizing an enhanced feature pyramid network to fuse data features from multiple resolutions, effectively addressing resolution imbalance. Moreover, inductive learning is employed through a multitask approach, facilitating the detection and identification of both small and large-sized PV panels to mitigate size imbalance. To address significant class imbalance in PV panel recognition tasks, we integrate the Focal loss function for effective hard sample mining. Through experimental evaluation conducted in Heilbronn, Germany, our proposed method demonstrates superior performance compared to state-of-the-art approaches in PV panel segmentation. The results exhibit progressively higher accuracy and improved generalization capability. These findings highlight the potential of our method to serve as an advanced and practical tool for PV segmentation in the renewable energy field.

Suggested Citation

  • Guo, Zhiling & Zhuang, Zhan & Tan, Hongjun & Liu, Zhengguang & Li, Peiran & Lin, Zhengyuan & Shang, Wen-Long & Zhang, Haoran & Yan, Jinyue, 2023. "Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets," Renewable Energy, Elsevier, vol. 219(P1).
  • Handle: RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123013861
    DOI: 10.1016/j.renene.2023.119471
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123013861
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119471?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    2. Di Tommaso, Antonio & Betti, Alessandro & Fontanelli, Giacomo & Michelozzi, Benedetto, 2022. "A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle," Renewable Energy, Elsevier, vol. 193(C), pages 941-962.
    3. 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.
    4. 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).
    5. Ghaleb, Belal & Asif, Muhammad, 2022. "Assessment of solar PV potential in commercial buildings," Renewable Energy, Elsevier, vol. 187(C), pages 618-630.
    6. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    7. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    8. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Ruiqing & He, Guojin & Yin, Ranyu & Wang, Guizhou & Zhang, Zhaoming & Long, Tengfei & Peng, Yan, 2024. "Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map," Applied Energy, Elsevier, vol. 361(C).
    2. Zhixin Zhang & Min Chen & Teng Zhong & Rui Zhu & Zhen Qian & Fan Zhang & Yue Yang & Kai Zhang & Paolo Santi & Kaicun Wang & Yingxia Pu & Lixin Tian & Guonian Lü & Jinyue Yan, 2023. "Carbon mitigation potential afforded by rooftop photovoltaic in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. S M Mezbahul Amin & Abul Hasnat & Nazia Hossain, 2023. "Designing and Analysing a PV/Battery System via New Resilience Indicators," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    4. Cuenca, Juan J. & Daly, Hannah E. & Hayes, Barry P., 2023. "Sharing the grid: The key to equitable access for small-scale energy generation," Applied Energy, Elsevier, vol. 349(C).
    5. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    6. Mahmoud Dhimish & Pavlos I. Lazaridis, 2022. "Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-16, November.
    7. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    8. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
    9. Thiago B. Murari & Aloisio S. Nascimento Filho & Marcelo A. Moret & Sergio Pitombo & Alex A. B. Santos, 2020. "Self-Affine Analysis of ENSO in Solar Radiation," Energies, MDPI, vol. 13(18), pages 1-17, September.
    10. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    11. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    12. Mireille B. Tadie Fogaing & Arman Hemmati & Carlos F. Lange & Brian A. Fleck, 2019. "Performance of Turbulence Models in Simulating Wind Loads on Photovoltaics Modules," Energies, MDPI, vol. 12(17), pages 1-16, August.
    13. Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. van Sark, 2019. "A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-18, May.
    14. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    15. Edwidge Raissa Mache Kengne & Alain Soup Tewa Kammogne & Thomas Tatietse Tamo & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Photovoltaic Systems Based on Average Current Mode Control: Dynamical Analysis and Chaos Suppression by Using a Non-Adaptive Feedback Outer Loop Controller," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    16. Femke J. M. M. Nijsse & Jean-Francois Mercure & Nadia Ameli & Francesca Larosa & Sumit Kothari & Jamie Rickman & Pim Vercoulen & Hector Pollitt, 2023. "The momentum of the solar energy transition," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    17. Liu, Junling & Li, Mengyue & Xue, Liya & Kobashi, Takuro, 2022. "A framework to evaluate the energy-environment-economic impacts of developing rooftop photovoltaics integrated with electric vehicles at city level," Renewable Energy, Elsevier, vol. 200(C), pages 647-657.
    18. 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).
    19. Li, Feng & Li, Yanjie & Zhou, Siqi & Chen, Yifang & Sun, Xuan & Deng, Yutong, 2022. "Wireless power transfer tuning model of electric vehicles with pavement materials as transmission media for energy conservation," Applied Energy, Elsevier, vol. 323(C).
    20. Guan, Bowen & Yang, Haobo & Zhang, Tao & Liu, Xiaohua & Wang, Xinke, 2024. "Technoeconomic analysis of rooftop PV system in elevated metro station for cost-effective operation and clean electrification," Renewable Energy, Elsevier, vol. 226(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123013861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.