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A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules

Author

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  • Minsu Kim

    (School of Chemical Engineering, Yeungnam University, Gyeongsan-Si 38541, Korea)

  • Hongmyeong Kim

    (School of Chemical Engineering, Yeungnam University, Gyeongsan-Si 38541, Korea)

  • Jae Hak Jung

    (School of Chemical Engineering, Yeungnam University, Gyeongsan-Si 38541, Korea)

Abstract

Various equations are being developed and applied to predict photovoltaic (PV) module generation. Currently, quite diverse methods for predicting module generation are available, with most equations showing accuracy with ≤5% error. However, the accuracy can be determined only when the module temperature and the value of irradiation that reaches the module surface are precisely known. The prediction accuracy of outdoor generation is actually extremely low, as the method for predicting outdoor module temperature has extremely low accuracy. The change in module temperature cannot be predicted accurately because of the real-time change of irradiation and air temperature outdoors. Calculations using conventional equations from other studies show a mean error of temperature difference of 4.23 °C. In this study, an equation was developed and verified that can predict the precise module temperature up to 1.64 °C, based on the experimental data obtained after installing an actual outdoor module.

Suggested Citation

  • Minsu Kim & Hongmyeong Kim & Jae Hak Jung, 2021. "A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules," Energies, MDPI, vol. 14(5), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1503-:d:513627
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    Cited by:

    1. Stefan Hensel & Marin B. Marinov & Michael Koch & Dimitar Arnaudov, 2021. "Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation," Energies, MDPI, vol. 14(19), pages 1-14, September.

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