Representative Identification of Spectra and Environments (RISE) using K-means

Erin E. Looney, Zhe Liu, Andrej Classen, Haohui Liu, Nicholas Riedel, Marilía Braga, Pradeep Balaji, André Augusto, Tonio Buonassisi, Ian Marius Peters*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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, CdTe) in four locations: Singapore (fully humid equatorial climate), Colorado (cold arid), Brazil (warm, humid, 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.
Original languageEnglish
JournalProgress in Photovoltaics
Number of pages16
Publication statusAccepted/In press - 2021


  • Solar spectra classification
  • Energy yield
  • Photovoltaics
  • Machine learning


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