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Representative identification of spectra and environments (RISE) using k‐means
Name
Progress in Photovoltaics - 2020 - Looney - Representative identification of spectra and environments RISE using k‐means.pdf
Description
Published version
Size
17.46 MB
Format
Adobe PDF
Checksum (MD5)
914ccb83f20c835b7c8477c5668b36d8
Author(s)
Looney, Erin E
Liu, Zhe
Classen, Andrej
Liu, Haohui
Riedel, Nicholas
Braga, Marília
Balaji, Pradeep
Augusto, André
Buonassisi, Tonio
Marius Peters, Ian
Date Issued
2021
Journal
Progress in Photovoltaics: Research and Applications
Publisher
Wiley
Citation
Looney, Erin E, Liu, Zhe, Classen, Andrej, Liu, Haohui, Riedel, Nicholas et al. 2021. "Representative identification of spectra and environments (RISE) using k‐means." Progress in Photovoltaics: Research and Applications, 29 (2).
Version
Final published version
Abstract
Spectral differences affect solar cell performance, an effect that is especially visible when comparing different solar cell technologies. To reproduce the impact of varying spectra on solar cell performance in the lab, a unique classification of spectra is needed, which is currently missing in literature. The most commonly used classification, average photon energy (APE), is not unique, and a single APE value may represent various spectra depending on location. In this work, we propose a classification method based on an iterative use of the k-means clustering algorithm. We call this method RISE (Representative Identification of Spectra and the Environment). We define a set of 18 spectra using RISE and reproduce the spectral impact on energy yield for various solar cell technologies and locations. We explore effects on yield for commercially available solar cell technologies (Si and CdTe) in four locations: Singapore (fully humid equatorial climate), Colorado (cold arid), Brazil (warm, humid, and subtropical), and Denmark (fully humid warm temperature). We then reduce our findings to practice by implementing the spectrum set into an LED current–voltage (IV) tester. We verify our performance predictions using our set of representative spectra to reproduce energy yield differences between Si solar cells and CdTe solar cells with an average error of less than 1.5 ± 0.5% as compared to over 5% when using standard testing conditions.
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1002/PIP.3358
