Work

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

Year: 2025

Type: article

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 (R2) 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.

Source: Energies

DOI: 10.3390/en18082019

Authors: Fabian Alonso Lara-Vargas, Carlos Vargas-Salgado, Jesus Águila-León, Dácil Díaz-Bello

Institutions: Universidad Pontificia Bolivariana, Universitat Politècnica de València, Universidad de Guadalajara

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,137