IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924020750.html
   My bibliography  Save this article

An integrated industrial PV panel cleaning recommendation system for optimal dust removal

Author

Listed:
  • Zhang, Chao
  • Ma, Yunfeng
  • Yang, Guolin
  • Chen, Tao

Abstract

Developed from prior research, this paper presents a comprehensive study on the effectiveness of the PV Nexus Cleaning Recommendation System (PNCRS), an intelligent cleaning recommendation system designed for optimizing photovoltaic (PV) panel cleaning schedules under various environmental conditions. Traditional fixed interval and performance degradation strategies for PV panel maintenance often prove inadequate, primarily due to their inability to adapt to fluctuating environmental impacts, particularly under extreme weather conditions. These conventional methods, while straightforward, fail to capture the complex interplay between environmental variability and panel efficiency, leading to suboptimal cleaning schedules and diminished energy output. The intelligent cleaning recommendation system utilizes real-time environmental adaptability, data-driven decision making, and comprehensive profit optimization to significantly determine PV panel cleaning schedule over traditional methods. In this paper, the intelligent cleaning recommendation system PNCRS incorporates cutting-edge data augmentation and machine learning techniques, including Variational Mode Decomposition (VMD) and Conditional Generative Adversarial Networks (CGANs). This integration is essential for enhancing data representation, particularly in scenarios where input data is sparse or unrepresentative, such as during unusual weather patterns. Additionally, the system employs the Wavelet Packet Energy Transmissibility Function (WPETF) to innovatively reduce the model’s dependency on less impactful environmental features under extreme conditions. Furthermore, the system uses profit-based Bayesian Optimization (BO) to dynamically adjust the importance weights of model features when profit curves deviate from expectations. Our evaluation of the PNCRS across two PV farms with unique operational features quantitatively validates its effectiveness, as profit curves indicate a 29% profit increase at Farm 1 and a 34% profit increase at Farm 2.

Suggested Citation

  • Zhang, Chao & Ma, Yunfeng & Yang, Guolin & Chen, Tao, 2025. "An integrated industrial PV panel cleaning recommendation system for optimal dust removal," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020750
    DOI: 10.1016/j.apenergy.2024.124692
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020750
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124692?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. Cheema, Armaghan & Shaaban, M.F. & Ismail, Mahmoud H., 2021. "A novel stochastic dynamic modeling for photovoltaic systems considering dust and cleaning," Applied Energy, Elsevier, vol. 300(C).
    2. Micheli, Leonardo & Fernández, Eduardo F. & Aguilera, Jorge T. & Almonacid, Florencia, 2021. "Economics of seasonal photovoltaic soiling and cleaning optimization scenarios," Energy, Elsevier, vol. 215(PA).
    3. Zhang, Chao & Ma, Yunfeng & Mi, Zengqiang & Yang, Fan & Zhang, Long, 2024. "A rolling-horizon cleaning recommendation system for dust removal of industrial PV panels," Applied Energy, Elsevier, vol. 353(PB).
    4. Heinrich, Matthias & Meunier, Simon & Samé, Allou & Quéval, Loïc & Darga, Arouna & Oukhellou, Latifa & Multon, Bernard, 2020. "Detection of cleaning interventions on photovoltaic modules with machine learning," Applied Energy, Elsevier, vol. 263(C).
    5. Kaiss, El-Cheikh Amer & Hassan, Noha M., 2024. "Optimizing the cleaning frequency of solar photovoltaic (PV) systems using numerical analysis and empirical models," Renewable Energy, Elsevier, vol. 228(C).
    6. Hussain, Akhtar & Arif, Syed Muhammad & Aslam, Muhammad, 2017. "Emerging renewable and sustainable energy technologies: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 12-28.
    7. Peinado Gonzalo, Alfredo & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Survey of maintenance management for photovoltaic power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    8. Ullah, Asad & Amin, Amir & Haider, Turab & Saleem, Murtaza & Butt, Nauman Zafar, 2020. "Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan," Renewable Energy, Elsevier, vol. 150(C), pages 456-468.
    9. Hammad, Bashar & Al–Abed, Mohammad & Al–Ghandoor, Ahmed & Al–Sardeah, Ali & Al–Bashir, Adnan, 2018. "Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2218-2234.
    10. 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).
    11. Song, Chenchen & Guo, Zhiling & Liu, Zhengguang & Hongyun, Zhang & Liu, Ran & Zhang, Haoran, 2024. "Application of photovoltaics on different types of land in China: Opportunities, status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    12. Micheli, Leonardo & Theristis, Marios & Talavera, Diego L. & Almonacid, Florencia & Stein, Joshua S. & Fernandez, Eduardo F., 2020. "Photovoltaic Cleaning Frequency Optimization Under Different Degradation Rate Patterns," MPRA Paper 105008, University Library of Munich, Germany, revised 07 Oct 2020.
    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. Kaiss, El-Cheikh Amer & Hassan, Noha M., 2024. "Optimizing the cleaning frequency of solar photovoltaic (PV) systems using numerical analysis and empirical models," Renewable Energy, Elsevier, vol. 228(C).
    2. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    3. 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).
    4. Huang, Maoquan & Yang, Rui & Tang, G.H. & Pu, Jin Huan & Sun, Qie & Du, Mu, 2025. "Quantifying the effects of dust characteristics on the performance of radiative cooling PV systems," Applied Energy, Elsevier, vol. 377(PD).
    5. Micheli, Leonardo & Theristis, Marios & Talavera, Diego L. & Nofuentes, Gustavo & Stein, Joshua S. & Almonacid, Florencia & Fernández, Eduardo F., 2022. "The economic value of photovoltaic performance loss mitigation in electricity spot markets," Renewable Energy, Elsevier, vol. 199(C), pages 486-497.
    6. Rediske, Graciele & Michels, Leandro & Siluk, Julio Cezar Mairesse & Rigo, Paula Donaduzzi & Rosa, Carmen Brum & Lima, Andrei Cunha, 2024. "A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants," Applied Energy, Elsevier, vol. 354(PA).
    7. Yao, Wanxiang & Kong, Xiangru & Xu, Ai & Xu, Puyan & Wang, Yan & Gao, Weijun, 2023. "New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(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. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Chiteka, Kudzanayi & Arora, Rajesh & Sridhara, S.N. & Enweremadu, C.C., 2021. "Optimizing wind barrier and photovoltaic array configuration in soiling mitigation," Renewable Energy, Elsevier, vol. 163(C), pages 225-236.
    11. 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).
    12. Micheli, Leonardo & Theristis, Marios & Talavera, Diego L. & Almonacid, Florencia & Stein, Joshua S. & Fernández, Eduardo F., 2020. "Photovoltaic cleaning frequency optimization under different degradation rate patterns," Renewable Energy, Elsevier, vol. 166(C), pages 136-146.
    13. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
    14. Isaacs, Stewart & Kalashnikova, Olga & Garay, Michael J. & van Donkelaar, Aaron & Hammer, Melanie S. & Lee, Huikyo & Wood, Danielle, 2023. "Dust soiling effects on decentralized solar in West Africa," Applied Energy, Elsevier, vol. 340(C).
    15. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    16. Hasan Masrur & Keifa Vamba Konneh & Mikaeel Ahmadi & Kaisar R. Khan & Mohammad Lutfi Othman & Tomonobu Senjyu, 2021. "Assessing the Techno-Economic Impact of Derating Factors on Optimally Tilted Grid-Tied Photovoltaic Systems," Energies, MDPI, vol. 14(4), pages 1-21, February.
    17. Segovia Ramírez, Isaac & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2022. "A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections," Renewable Energy, Elsevier, vol. 187(C), pages 371-389.
    18. Alkharusi, Tarik & Huang, Gan & Markides, Christos N., 2024. "Characterisation of soiling on glass surfaces and their impact on optical and solar photovoltaic performance," Renewable Energy, Elsevier, vol. 220(C).
    19. Shi, Xueli & Liang, Bingchen & Li, Shaowu & Zhao, Jianchun & Wang, Junhui & Wang, Zhenlu, 2024. "Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering," Energy, Elsevier, vol. 299(C).
    20. Moghadam, Saman Salehi & Gholamian, Mohammad Reza & Zahedi, Rahim & Shaqaqifar, Maziar, 2024. "Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy," Applied Energy, Elsevier, vol. 369(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:appene:v:377:y:2025:i:pd:s0306261924020750. 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.