IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v376y2024ipbs0306261924017185.html
   My bibliography  Save this article

DGImNet: A deep learning model for photovoltaic soiling loss estimation

Author

Listed:
  • Fang, Mingyu
  • Qian, Weixing
  • Qian, Tao
  • Bao, Qiwei
  • Zhang, Haocheng
  • Qiu, Xiao

Abstract

Deep learning models have recently been introduced to photovoltaic (PV) soiling loss estimation tasks. Most PV soiling loss (PVSL) estimation models are based on a single image and the environmental factors at a specific time point, while the temporal characteristic of environmental factors is less utilized. DGImNet, a PVSL estimation model utilizing both PV panel images and time series environmental factors (TSEFs), is proposed. DGImNet takes a PV panel image and TSEFs of 50 continuous time points to estimate the PVSL. The TSEFs are processed by gate recurrent units to produce a 96D feature, while the image is extracted to generate another 96D feature by a set of analysis units. The multi-modal features are fused to yield estimation results. It is proved that the exploitation of TSEFs is beneficial to improve PVSL prediction performance, and the engagement of compact bilinear pooling is useful for better fusion of image and TSEF features. Cooperating with a real-time data collection system, the proposed model is able to run on edge computing devices and be employed for real-time PVSL estimation tasks in actual PV power stations.

Suggested Citation

  • Fang, Mingyu & Qian, Weixing & Qian, Tao & Bao, Qiwei & Zhang, Haocheng & Qiu, Xiao, 2024. "DGImNet: A deep learning model for photovoltaic soiling loss estimation," Applied Energy, Elsevier, vol. 376(PB).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pb:s0306261924017185
    DOI: 10.1016/j.apenergy.2024.124335
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924017185
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124335?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. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    2. Mithhu, Md. Mahamudul Hasan & Rima, Tahmina Ahmed & Khan, M. Ryyan, 2021. "Global analysis of optimal cleaning cycle and profit of soiling affected solar panels," Applied Energy, Elsevier, vol. 285(C).
    3. Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
    4. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    6. Deb, Dipankar & Brahmbhatt, Nisarg L., 2018. "Review of yield increase of solar panels through soiling prevention, and a proposed water-free automated cleaning solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3306-3313.
    7. Fernández-Solas, Álvaro & Montes-Romero, Jesús & Micheli, Leonardo & Almonacid, Florencia & Fernández, Eduardo F., 2022. "Estimation of soiling losses in photovoltaic modules of different technologies through analytical methods," Energy, Elsevier, vol. 244(PB).
    8. 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).
    9. Touati, Farid & Al-Hitmi, M.A. & Chowdhury, Noor Alam & Hamad, Jehan Abu & San Pedro Gonzales, Antonio J.R., 2016. "Investigation of solar PV performance under Doha weather using a customized measurement and monitoring system," Renewable Energy, Elsevier, vol. 89(C), pages 564-577.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marta Redondo & Carlos Antonio Platero & Antonio Moset & Fernando Rodríguez & Vicente Donate, 2024. "Review and Comparison of Methods for Soiling Modeling in Large Grid-Connected PV Plants," Sustainability, MDPI, vol. 16(24), pages 1-18, December.
    2. Boris I. Evstatiev & Dimitar T. Trifonov & Katerina G. Gabrovska-Evstatieva & Nikolay P. Valov & Nicola P. Mihailov, 2024. "PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning," Energies, MDPI, vol. 17(20), pages 1-20, October.

    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. Conceição, Ricardo & González-Aguilar, José & Merrouni, Ahmed Alami & Romero, Manuel, 2022. "Soiling effect in solar energy conversion systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    3. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    4. Ali Unlu & Abdulhamit Subasi, 2025. "Substance use prediction using artificial intelligence techniques," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-40, February.
    5. Chanchangi, Yusuf N. & Ghosh, Aritra & Micheli, Leonardo & Fernández, Eduardo F. & Sundaram, Senthilarasu & Mallick, Tapas K., 2022. "Soiling mapping through optical losses for Nigeria," Renewable Energy, Elsevier, vol. 197(C), pages 995-1008.
    6. Honey Brahma & Shraiya Pant & Leonardo Micheli & Greg P. Smestad & Nabin Sarmah, 2022. "Effect of Environmental Factors on Photovoltaic Soiling: Experimental and Statistical Analysis," Energies, MDPI, vol. 16(1), pages 1-22, December.
    7. AL-Rasheedi, Majed & Gueymard, Christian A. & Al-Khayat, Mohammad & Ismail, Alaa & Lee, Jared A. & Al-Duaj, Hamad, 2020. "Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Lucia Cattani & Paolo Cattani & Anna Magrini, 2021. "Photovoltaic Cleaning Optimization: A Simplified Theoretical Approach for Air to Water Generator (AWG) System Employment," Energies, MDPI, vol. 14(14), pages 1-17, July.
    9. Khaled M. Alawasa & Rashid S. AlAbri & Amer S. Al-Hinai & Mohammed H. Albadi & Abdullah H. Al-Badi, 2021. "Experimental Study on the Effect of Dust Deposition on a Car Park Photovoltaic System with Different Cleaning Cycles," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    10. Ramez Abdallah & Adel Juaidi & Salameh Abdel-Fattah & Mahmoud Qadi & Montaser Shadid & Aiman Albatayneh & Hüseyin Çamur & Amos García-Cruz & Francisco Manzano-Agugliaro, 2022. "The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine," Energies, MDPI, vol. 15(12), pages 1-18, June.
    11. Hassan Shaban & Essam H. Houssein & Marco Pérez-Cisneros & Diego Oliva & Amir Y. Hassan & Alaa A. K. Ismaeel & Diaa Salama AbdElminaam & Sanchari Deb & Mokhtar Said, 2021. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    12. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    13. Sohani, Ali & Sayyaadi, Hoseyn & Miremadi, Seyed Rahman & Yang, Xiaohu & Doranehgard, Mohammad Hossein & Nizetic, Sandro, 2023. "Determination of the best air space value for installation of a PV façade technology based on 4E characteristics," Energy, Elsevier, vol. 262(PB).
    14. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    15. Alami Merrouni, Ahmed & Elwali Elalaoui, Fakhreddine & Mezrhab, Ahmed & Mezrhab, Abdelhamid & Ghennioui, Abdellatif, 2018. "Large scale PV sites selection by combining GIS and Analytical Hierarchy Process. Case study: Eastern Morocco," Renewable Energy, Elsevier, vol. 119(C), pages 863-873.
    16. Park, Nochang & Kim, Ju-Hee & Kim, Hyun-A. & Moon, Jin-Chel, 2017. "Development of an algebraic model that predicts the maximum power output of solar modules including their degradation," Renewable Energy, Elsevier, vol. 113(C), pages 141-147.
    17. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    18. Barbón, A. & Bayón-Cueli, C. & Bayón, L. & Rodríguez-Suanzes, C., 2022. "Analysis of the tilt and azimuth angles of photovoltaic systems in non-ideal positions for urban applications," Applied Energy, Elsevier, vol. 305(C).
    19. Haneen Abuzaid & Mahmoud Awad & Abdulrahim Shamayleh, 2024. "Photovoltaic Modules’ Cleaning Method Selection for the MENA Region," Sustainability, MDPI, vol. 16(21), pages 1-23, October.
    20. Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.

    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:appene:v:376:y:2024:i:pb:s0306261924017185. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.