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.

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Detalles Bibliográficos
Autores principales:	J. M. Ripalda, J. Buencuerpo, I. García
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ca2058cb97814de0812dcc6103f7579c
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spelling	oai:doaj.org-article:ca2058cb97814de0812dcc6103f7579c2021-12-02T15:34:06ZSolar cell designs by maximizing energy production based on machine learning clustering of spectral variations10.1038/s41467-018-07431-32041-1723https://doaj.org/article/ca2058cb97814de0812dcc6103f7579c2018-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07431-3https://doaj.org/toc/2041-1723Though 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.J. M. RipaldaJ. BuencuerpoI. GarcíaNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q J. M. Ripalda J. Buencuerpo I. García Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
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.
format	article
author	J. M. Ripalda J. Buencuerpo I. García
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 Portfolio
publishDate	2018
url	https://doaj.org/article/ca2058cb97814de0812dcc6103f7579c
work_keys_str_mv	AT jmripalda solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations AT jbuencuerpo solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations AT igarcia solarcelldesignsbymaximizingenergyproductionbasedonmachinelearningclusteringofspectralvariations
_version_	1718386928933404672

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

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