Crossmark

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Crossref DOI link: https://doi.org/10.1007/s40095-022-00493-6

Published Online: 2022-04-19

Published Print: 2022-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Rocha, Paulo A. C. https://orcid.org/0000-0002-4366-366X
Santos, Victor Oliveira
Funding

Funding for this research was provided by:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (001)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (305456/2019-9)
License Information

Text and Data Mining valid from 2022-04-19

Version of Record valid from 2022-04-19
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Article History

Received: 4 January 2022

Accepted: 26 March 2022

First Online: 19 April 2022

Declarations

:

: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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