A Simple Approach for Module Temperature and Power Prediction

Authors

DOI:

https://doi.org/10.52825/siliconpv.v1i.904

Keywords:

Solar Modules, Performance Prediction, Field Measurement

Abstract

In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.

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References

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Published

2024-02-22

How to Cite

Schneider, A., Chochollek, J., & Nierhoff, T. (2024). A Simple Approach for Module Temperature and Power Prediction. SiliconPV Conference Proceedings, 1. https://doi.org/10.52825/siliconpv.v1i.904

Conference Proceedings Volume

Section

Energy Yield, Reliability & Cost