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

Though multijunction solar cells can exceed silicon technology in terms of standard efficiency, the uncertainty in solar spectral changes impacts its energy production. Here, the authors use machine learning techniques to predict the optimal solar cell designs in terms of yearly averaged efficiency.

Bibliographic Details
Main Authors:	J. M. Ripalda, J. Buencuerpo, I. García
Format:	Article
Language:	English
Published:	Nature Publishing Group 2018-12-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-018-07431-3

id	doaj-ca2058cb97814de0812dcc6103f7579c
record_format	Article
spelling	doaj-ca2058cb97814de0812dcc6103f7579c2021-05-11T10:21:52ZengNature Publishing GroupNature Communications2041-17232018-12-01911810.1038/s41467-018-07431-3Solar cell designs by maximizing energy production based on machine learning clustering of spectral variationsJ. M. Ripalda0J. Buencuerpo1I. García2Instituto de Micro y Nanotecnología, IMN-CNM, CSIC (CEI UAM+CSIC) Isaac Newton, 8Instituto de Micro y Nanotecnología, IMN-CNM, CSIC (CEI UAM+CSIC) Isaac Newton, 8Instituto de Energía Solar, Universidad Politécnica de MadridThough multijunction solar cells can exceed silicon technology in terms of standard efficiency, the uncertainty in solar spectral changes impacts its energy production. Here, the authors use machine learning techniques to predict the optimal solar cell designs in terms of yearly averaged efficiency.https://doi.org/10.1038/s41467-018-07431-3
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	J. M. Ripalda J. Buencuerpo I. García
spellingShingle	J. M. Ripalda J. Buencuerpo I. García Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations Nature Communications
author_facet	J. M. Ripalda J. Buencuerpo I. García
author_sort	J. M. Ripalda
title	Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
title_short	Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
title_full	Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
title_fullStr	Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
title_full_unstemmed	Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
title_sort	solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2018-12-01
description	Though multijunction solar cells can exceed silicon technology in terms of standard efficiency, the uncertainty in solar spectral changes impacts its energy production. Here, the authors use machine learning techniques to predict the optimal solar cell designs in terms of yearly averaged efficiency.
url	https://doi.org/10.1038/s41467-018-07431-3
work_keys_str_mv	AT jmripalda solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations AT jbuencuerpo solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations AT igarcia solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations
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Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations

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