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Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria

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  • Bailek, Nadjem
  • Bouchouicha, Kada
  • Hassan, Muhammed A.
  • Slimani, Abdeldjalil
  • Jamil, Basharat

Abstract

The determination of the PV module temperature is a key parameter for the assessment of the actual performance of the PV systems. The application of available models for PV module temperature estimation in literature can be verified, but the application of these correlations for different climate conditions does not lead to unequivocal results. The main objective of this study is to suggest new empirical models for estimating the back surface module temperature under outdoor hot dry climatic conditions of Adrar province (Algerian Sahara) and to compare the developed models to different existing models in the literature. The models are developed based on meteorological and irradiance data collected from two different plants with different module technologies. The best site-specific approach uses a simple formula to derive the PV-back module temperature from the meteorological variables such as ambient temperature, and irradiance. The relative root mean square error and the Pearson’s correlation coefficient of the best developed model are 10.662% and 0.955, respectively. In addition, MAPE and RMSE values are considerably small for the studied stations. A general model for predicting the PV-back temperature was also recommended for simple PV modules or open rack systems in rural locations with no measurement equipment nearby. The results are quite useful for studying PV system performance and estimating its energy output.

Suggested Citation

  • Bailek, Nadjem & Bouchouicha, Kada & Hassan, Muhammed A. & Slimani, Abdeldjalil & Jamil, Basharat, 2020. "Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria," Renewable Energy, Elsevier, vol. 156(C), pages 57-67.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:57-67
    DOI: 10.1016/j.renene.2020.04.073
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    References listed on IDEAS

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    8. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Potential of four different machine-learning algorithms in modeling daily global solar radiation," Renewable Energy, Elsevier, vol. 111(C), pages 52-62.
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