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Simultaneous operating temperature and output power prediction method for photovoltaic modules

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  • Dong, Xiao-Jian
  • Shen, Jia-Ni
  • Ma, Zi-Feng
  • He, Yi-Jun

Abstract

Accurate cell temperature and output power prediction are vital for the optimal design and operation of photovoltaic (PV) systems. However, capturing the accurate relationships between cell temperature/circuit parameters and weather conditions is still a challenging task. In this study, a universal radial basis function neural network based hybrid modeling approach is proposed to model the cell temperature and circuit parameters. A simultaneous optimization model with l1 norm penalty is established and a separate parameter estimation strategy is proposed to handle the high computational parameter estimation procedure. The effectiveness of the proposed hybrid modeling approach is validated based on four practical experimental datasets of both commercial and laboratory PV plants. It is thus indicated that the proposed modeling approach could provide a promising potential solution framework for the accurate output power prediction under different PV types and relatively wide weather conditions.

Suggested Citation

  • Dong, Xiao-Jian & Shen, Jia-Ni & Ma, Zi-Feng & He, Yi-Jun, 2022. "Simultaneous operating temperature and output power prediction method for photovoltaic modules," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018114
    DOI: 10.1016/j.energy.2022.124909
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    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Piliougine, M. & Spagnuolo, G. & Sidrach-de-Cardona, M., 2020. "Series resistance temperature sensitivity in degraded mono–crystalline silicon modules," Renewable Energy, Elsevier, vol. 162(C), pages 677-684.
    3. Ruhang, Xu, 2016. "Characteristics and prospective of China׳s PV development route: Based on data of world PV industry 2000–2010," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1032-1043.
    4. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    5. Carrero, C. & Amador, J. & Arnaltes, S., 2007. "A single procedure for helping PV designers to select silicon PV modules and evaluate the loss resistances," Renewable Energy, Elsevier, vol. 32(15), pages 2579-2589.
    6. Zhu, Lei & Xu, Yuan & Pan, Yingjie, 2019. "Enabled comparative advantage strategy in China's solar PV development," Energy Policy, Elsevier, vol. 133(C).
    7. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    8. Lo Brano, Valerio & Ciulla, Giuseppina, 2013. "An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data," Applied Energy, Elsevier, vol. 111(C), pages 894-903.
    9. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    10. Wang, Qian-Kun & He, Yi-Jun & Shen, Jia-Ni & Ma, Zi-Feng & Zhong, Guo-Bin, 2017. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach," Energy, Elsevier, vol. 138(C), pages 118-132.
    11. Skoplaki, E. & Palyvos, J.A., 2009. "Operating temperature of photovoltaic modules: A survey of pertinent correlations," Renewable Energy, Elsevier, vol. 34(1), pages 23-29.
    12. Obiwulu, Anthony Umunnakwe & Erusiafe, Nald & Olopade, Muteeu Abayomi & Nwokolo, Samuel Chukwujindu, 2020. "Modeling and optimization of back temperature models of mono-crystalline silicon modules with special focus on the effect of meteorological and geographical parameters on PV performance," Renewable Energy, Elsevier, vol. 154(C), pages 404-431.
    13. Bonanno, F. & Capizzi, G. & Graditi, G. & Napoli, C. & Tina, G.M., 2012. "A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, Elsevier, vol. 97(C), pages 956-961.
    14. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    15. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    16. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
    17. Abbassi, Abdelkader & Abbassi, Rabeh & Heidari, Ali Asghar & Oliva, Diego & Chen, Huiling & Habib, Arslan & Jemli, Mohamed & Wang, Mingjing, 2020. "Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach," Energy, Elsevier, vol. 198(C).
    18. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    19. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
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