Predicting color and short-circuit current of colored BIPV modules

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Gewohn, T.; Schinke, C.; Lim, B.; Brendel, R.: Predicting color and short-circuit current of colored BIPV modules. In: AIP Advances 11 (2021), 095104. DOI: https://doi.org/10.1063/5.0063140

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/11808

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Sum total of downloads: 87




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Abstract: 
Photovoltaic modules for façade integration should have a widely modifiable appearance to adjust to the architect’s requirements. However, architects today usually have only a limited number of already manufactured samples to choose from. Changing the color will also change the photovoltaic yield. Therefore, it would be helpful to have a procedure that allows us to determine the appearance and expected yield in advance of module fabrication. We present such a method for creating a digital prototype of a colored building integrated photovoltaic module. Using reflectance and external quantum efficiency measurements of eight colored modules, we simulate the appearance and respective energy yield for arbitrary module colors. We validate our predictions for 29 different colored modules. We use textiles that have been colored by printing and laminate them onto the modules to change the appearance of the modules. However, our digital prototyping model is also applicable to other coloring techniques. We achieve an average color difference of ΔE00 = 1.34 between predicted and measured colors, which is barely perceptible to the human eye. The predicted short-circuit current density of the digital prototype deviates on average less than 1% from the measured one
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2021
Appears in Collections:Fakultät für Mathematik und Physik
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distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 41 47.13%
2 image of flag of United States United States 18 20.69%
3 image of flag of China China 7 8.05%
4 image of flag of Russian Federation Russian Federation 3 3.45%
5 image of flag of South Africa South Africa 2 2.30%
6 image of flag of Taiwan Taiwan 2 2.30%
7 image of flag of Netherlands Netherlands 2 2.30%
8 image of flag of Indonesia Indonesia 2 2.30%
9 image of flag of Vietnam Vietnam 1 1.15%
10 image of flag of No geo information available No geo information available 1 1.15%
    other countries 8 9.20%

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