IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v108y2013icp392-401.html
   My bibliography  Save this article

A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants

Author

Listed:
  • Massi Pavan, A.
  • Mellit, A.
  • De Pieri, D.
  • Kalogirou, S.A.

Abstract

This paper presents a comparison between two different techniques for the determination of the effect of soiling on large scale photovoltaic plants. Four Bayesian Neural Network (BNN) models have been developed in order to calculate the performance at Standard Test Conditions (STCs) of two plants installed in Southern Italy before and after a complete clean-up of their modules. The differences between the STC power before and after the clean-up represent the losses due to the soiling effect. The results obtained with the BNN models are compared with the ones calculated with a well known regression model. Although the soiling effect can have a significant impact on the PV system performance and specific models developed are applicable only to the specific location in which the testing was conducted, this study is of great importance because it suggests a procedure to be used in order to give the necessary confidence to operation and maintenance personnel in applying the right schedule of clean-ups by making the right compromise between washing cost and losses in energy production.

Suggested Citation

  • Massi Pavan, A. & Mellit, A. & De Pieri, D. & Kalogirou, S.A., 2013. "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, Elsevier, vol. 108(C), pages 392-401.
    • Handle: RePEc:eee:appene:v:108:y:2013:i:c:p:392-401
    DOI: 10.1016/j.apenergy.2013.03.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913002195
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.03.023?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. Said, S.A.M., 1990. "Effects of dust accumulation on performances of thermal and photovoltaic flat-plate collectors," Applied Energy, Elsevier, vol. 37(1), pages 73-84.
    2. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    3. Kalogirou, Soteris A. & Agathokleous, Rafaela & Panayiotou, Gregoris, 2013. "On-site PV characterization and the effect of soiling on their performance," Energy, Elsevier, vol. 51(C), pages 439-446.
    4. Bouaouadja, N. & Bouzid, S. & Hamidouche, M. & Bousbaa, C. & Madjoubi, M., 2000. "Effects of sandblasting on the efficiencies of solar panels," Applied Energy, Elsevier, vol. 65(1-4), pages 99-105, April.
    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. Guilherme Souza & Ricardo Santos & Erlandson Saraiva, 2022. "A Log-Logistic Predictor for Power Generation in Photovoltaic Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. 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).
    3. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    4. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    5. Tuba Tanyıldızı Ağır, 2024. "Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    6. Yang Hu & Weiwei Lian & Yutong Han & Songyuan Dai & Honglu Zhu, 2018. "A Seasonal Model Using Optimized Multi-Layer Neural Networks to Forecast Power Output of PV Plants," Energies, MDPI, vol. 11(2), pages 1-17, February.
    7. 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.
    8. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    9. Obara, Shin’ya & Konno, Daisuke & Utsugi, Yuta & Morel, Jorge, 2014. "Analysis of output power and capacity reduction in electrical storage facilities by peak shift control of PV system with bifacial modules," Applied Energy, Elsevier, vol. 128(C), pages 35-48.
    10. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    11. Lu, Hao & Lu, Lin & Wang, Yuanhao, 2016. "Numerical investigation of dust pollution on a solar photovoltaic (PV) system mounted on an isolated building," Applied Energy, Elsevier, vol. 180(C), pages 27-36.
    12. Lu, Hao & Zhao, Wenjun, 2019. "CFD prediction of dust pollution and impact on an isolated ground-mounted solar photovoltaic system," Renewable Energy, Elsevier, vol. 131(C), pages 829-840.
    13. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    14. 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).
    15. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    16. Lu, Hao & Zhao, Wenjun, 2018. "Effects of particle sizes and tilt angles on dust deposition characteristics of a ground-mounted solar photovoltaic system," Applied Energy, Elsevier, vol. 220(C), pages 514-526.
    17. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    18. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    19. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    20. Hashemi, Behzad & Taheri, Shamsodin & Cretu, Ana-Maria & Pouresmaeil, Edris, 2021. "Systematic photovoltaic system power losses calculation and modeling using computational intelligence techniques," Applied Energy, Elsevier, vol. 284(C).
    21. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    22. Bianchini, Augusto & Gambuti, Michele & Pellegrini, Marco & Saccani, Cesare, 2016. "Performance analysis and economic assessment of different photovoltaic technologies based on experimental measurements," Renewable Energy, Elsevier, vol. 85(C), pages 1-11.

    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. Karim Menoufi, 2017. "Dust Accumulation on the Surface of Photovoltaic Panels: Introducing the Photovoltaic Soiling Index (PVSI)," Sustainability, MDPI, vol. 9(6), pages 1-12, June.
    2. Lu, Hao & Zhao, Wenjun, 2019. "CFD prediction of dust pollution and impact on an isolated ground-mounted solar photovoltaic system," Renewable Energy, Elsevier, vol. 131(C), pages 829-840.
    3. Lu, Hao & Lu, Lin & Wang, Yuanhao, 2016. "Numerical investigation of dust pollution on a solar photovoltaic (PV) system mounted on an isolated building," Applied Energy, Elsevier, vol. 180(C), pages 27-36.
    4. 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).
    5. Adinoyi, Muhammed J. & Said, Syed A.M., 2013. "Effect of dust accumulation on the power outputs of solar photovoltaic modules," Renewable Energy, Elsevier, vol. 60(C), pages 633-636.
    6. Sarver, Travis & Al-Qaraghuli, Ali & Kazmerski, Lawrence L., 2013. "A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 698-733.
    7. Yu Jiang & Lin Lu, 2016. "Experimentally Investigating the Effect of Temperature Differences in the Particle Deposition Process on Solar Photovoltaic (PV) Modules," Sustainability, MDPI, vol. 8(11), pages 1-9, October.
    8. Şevik, Seyfi & Aktaş, Ahmet, 2022. "Performance enhancing and improvement studies in a 600 kW solar photovoltaic (PV) power plant; manual and natural cleaning, rainwater harvesting and the snow load removal on the PV arrays," Renewable Energy, Elsevier, vol. 181(C), pages 490-503.
    9. 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.
    10. Dhaouadi, Rached & Al-Othman, Amani & Aidan, Ahmed A. & Tawalbeh, Muhammad & Zannerni, Rawan, 2021. "A characterization study for the properties of dust particles collected on photovoltaic (PV) panels in Sharjah, United Arab Emirates," Renewable Energy, Elsevier, vol. 171(C), pages 133-140.
    11. Klugmann-Radziemska, Ewa, 2015. "Degradation of electrical performance of a crystalline photovoltaic module due to dust deposition in northern Poland," Renewable Energy, Elsevier, vol. 78(C), pages 418-426.
    12. Lu, Hao & Zhao, Wenjun, 2018. "Effects of particle sizes and tilt angles on dust deposition characteristics of a ground-mounted solar photovoltaic system," Applied Energy, Elsevier, vol. 220(C), pages 514-526.
    13. Guan, Yanling & Zhang, Hao & Xiao, Bin & Zhou, Zhi & Yan, Xuzhou, 2017. "In-situ investigation of the effect of dust deposition on the performance of polycrystalline silicon photovoltaic modules," Renewable Energy, Elsevier, vol. 101(C), pages 1273-1284.
    14. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    15. Saidan, Motasem & Albaali, Abdul Ghani & Alasis, Emil & Kaldellis, John K., 2016. "Experimental study on the effect of dust deposition on solar photovoltaic panels in desert environment," Renewable Energy, Elsevier, vol. 92(C), pages 499-505.
    16. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    17. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    18. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
    19. Picotti, G. & Borghesani, P. & Cholette, M.E. & Manzolini, G., 2018. "Soiling of solar collectors – Modelling approaches for airborne dust and its interactions with surfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2343-2357.
    20. Fan, Siyuan & Wang, Xiao & Wang, Zun & Sun, Bo & Zhang, Zhenhai & Cao, Shengxian & Zhao, Bo & Wang, Yu, 2022. "A novel image enhancement algorithm to determine the dust level on photovoltaic (PV) panels," Renewable Energy, Elsevier, vol. 201(P1), pages 172-180.

    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:108:y:2013:i:c:p:392-401. 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.