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

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  • 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
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    Cited by:

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    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.

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