Deep Learning Models to Estimate and Predict the Solar Irradiation in Brazil-Reference-Cited by-同舟云学术

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Deep Learning Models to Estimate and Predict the Solar Irradiation in Brazil

Published:2023-12-02 Issue: Volume: Page:63-82
ISSN:0302-9743
Container-title:Energy Informatics
language:
Short-container-title:

Author:

Souza Wesley A.^ORCID,Alonso Augusto M. S.^ORCID,Bernardino Luiz G. R.^ORCID,Castoldi Marcelo F.,Nascimento Claudionor F.^ORCID,Marafão Fernando P.^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-48652-4_5

Reference52 articles.

1. Adedoyin, F. F., Bekun, F. V. & Alola, A. A. Growth impact of transition from non-renewable to renewable energy in the EU: The role of research and development expenditure. Renewable Energy 159, 1139–1145. ISSN: 0960–1481 (2020)

2. Brodny, J., Tutak, M.: Analyzing similarities between the european union countries in terms of the structure and volume of energy production from renewable energy sources. Energies 13 (2020) ISSN: 1996– 1073

3. EPE (Brasil). Balanço Energético Nacional 2022: Ano base 2021 In Portuguese. Tech. rep. (2022)

4. Wang, Z., Hong, T., Piette, M.A.: Building thermal load prediction through shallow machine learning and deep learning. Appli. Energy 263, 11468 (2020). ISSN: 0306–2619

5. Notton, G., Voyant, C., Fouilloy, A., Duchaud, J.L., Nivet, M.L.: Some applications of ANN to solar radiation estimation and forecasting for energy applications. Appli. Sci. 9, 2076–3417 (2019)

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