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Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations

Author

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  • J. M. Ripalda

    (IMN-CNM, CSIC (CEI UAM+CSIC) Isaac Newton, 8)

  • J. Buencuerpo

    (IMN-CNM, CSIC (CEI UAM+CSIC) Isaac Newton, 8
    National Renewable Energy Laboratory)

  • I. García

    (Universidad Politécnica de Madrid)

Abstract

Due to spectral sensitivity effects, using a single standard spectrum leads to a large uncertainty when estimating the yearly averaged photovoltaic efficiency or energy yield. Here we demonstrate how machine learning techniques can reduce the yearly spectral sets by three orders of magnitude to sets of a few characteristic spectra, and use the resulting proxy spectra to find the optimal solar cell designs maximizing the yearly energy production. When using standard conditions, our calculated efficiency limits show good agreement with current photovoltaic efficiency records, but solar cells designed for record efficiency under the current standard spectra are not optimal for maximizing the yearly energy yield. Our results show that more than 1 MWh m−2 year−1 can realistically be obtained from advanced multijunction systems making use of the direct, diffuse, and back-side albedo components of the irradiance.

Suggested Citation

  • J. M. Ripalda & J. Buencuerpo & I. García, 2018. "Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07431-3
    DOI: 10.1038/s41467-018-07431-3
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    Cited by:

    1. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Ce Yang & Haiyan Wang & Jiaxin Bai & Tiancheng He & Huhu Cheng & Tianlei Guang & Houze Yao & Liangti Qu, 2022. "Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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