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.
Guardado en:
Autores principales: | J. M. Ripalda, J. Buencuerpo, I. García |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ca2058cb97814de0812dcc6103f7579c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning in spectral domain
por: Lorenzo Giambagli, et al.
Publicado: (2021) -
Wavefront shaping assisted design of spectral splitters and solar concentrators
por: Berk N. Gün, et al.
Publicado: (2021) -
Spatiotemporal dynamics of maximal and minimal EEG spectral power.
por: Melisa Menceloglu, et al.
Publicado: (2021) -
Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
por: Abut F, et al.
Publicado: (2015) -
Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals.
por: Pavlos Stephanos Bekiaris, et al.
Publicado: (2021)