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Optimizing Bifacial Solar Modules with Trackers: Advanced Temperature Prediction Through Symbolic Regression

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  • Fabian Alonso Lara-Vargas

    (Programa de Ingeniería Electrónica, Grupo de Investigación ITEM, Universidad Pontificia Bolivariana Seccional Montería, Montería 230001, Colombia
    Institute for Energetic Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Carlos Vargas-Salgado

    (Institute for Energetic Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
    Electrical Engineering Deparment, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Jesus Águila-León

    (Departamento de Estudios del Agua y de la Energía, Universidad de Guadalajara, Guadalajara 44410, Mexico)

  • Dácil Díaz-Bello

    (Institute for Energetic Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

Accurate temperature prediction in bifacial photovoltaic (PV) modules is critical for optimizing solar energy systems. Conventional models face challenges to balance accuracy, interpretability, and computational efficiency. This study addresses these limitations by introducing a symbolic regression (SR) framework based on genetic algorithms to model nonlinear relationships between environmental variables and module temperature without predefined structures. High-resolution data, including solar radiation, ambient temperature, wind speed, and PV module temperature, were collected at 5 min intervals over a year from a 19.9 MW bifacial PV plant with trackers in San Marcos, Colombia. The SR model performance was compared with multiple linear regression, normal operating cell temperature (NOCT), and empirical regression models. The SR model outperformed others by achieving a root mean squared error (RMSE) of 4.05 °C, coefficient of determination (R 2 ) of 0.91, Spearman’s rank correlation coefficient of 0.95, and mean absolute error (MAE) of 2.25 °C. Its hybrid structure combines linear ambient temperature dependencies with nonlinear trigonometric terms capturing solar radiation dynamics. The SR model effectively balances accuracy and interpretability, providing information for modeling bifacial PV systems.

Suggested Citation

  • Fabian Alonso Lara-Vargas & Carlos Vargas-Salgado & Jesus Águila-León & Dácil Díaz-Bello, 2025. "Optimizing Bifacial Solar Modules with Trackers: Advanced Temperature Prediction Through Symbolic Regression," Energies, MDPI, vol. 18(8), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2019-:d:1634855
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    References listed on IDEAS

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    3. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
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