Development and Validation of a Physical Model Optimized by Evolutionary Algorithms for the Accurate Estimation of Cell Temperature in Photovoltaic Systems.
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| Title: | Development and Validation of a Physical Model Optimized by Evolutionary Algorithms for the Accurate Estimation of Cell Temperature in Photovoltaic Systems. |
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| Authors: | Dimitrova-Angelova, Doroteya1 (AUTHOR), Fernández, Diego Carmona1,2 (AUTHOR), Godoy, Manuel Calderón1 (AUTHOR), Moreno, Juan Antonio Álvarez1,2 (AUTHOR), González, Juan Félix González2 (AUTHOR) jfelixgg@unex.es |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2286. 23p. |
| Subject Terms: | *Evolutionary algorithms, *Calibration, *Digital twin, *Thermal properties, *Renewable energy sources, *Temperature measurements, *Computer simulation of heat transfer |
| Abstract: | Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19102286 |
