Machine learning models for daily net radiation prediction across different climatic zones of China-Reference-Cited by-同舟云学术

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Machine learning models for daily net radiation prediction across different climatic zones of China

Published:2024-09-03 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Yu Haiying,Jiang Shouzheng,Chen Minzhi,Wang Mingjun,Shi Rui,Li Songyu,Wu Jinfeng,Kui Xiu,Zou Haoting,Zhan Cun

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-71550-9.pdf

Reference56 articles.

1. Feng, Y., Zhang, X., Jia, Y., Cui, N., Hao, W., Li, H. & Gong, D. High-resolution assessment of solar radiation and energy potential in China. Energy Convers. Manag. 240 (2021).

2. Zhang, Y. X., Cui, N. B., Feng, Y., Gong, D. Z., & Hu, X. T. Comparison of BP, PSO-BP and statistical models for predicting daily global solar radiation in arid Northwest China. Comput. Electron. Agric. 164 (2019).

3. Feng, Y., Cui, N. B., Gong, D. Z., Zhang, Q. W. & Zhao, L. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric. Water Manag. 193, 163–173 (2017).

4. Besharat, F., Dehghan, A. A. & Faghih, A. R. Empirical models for estimating global solar radiation: A review and case study. Renew. Sustain. Energy Rev. 21, 798–821 (2013).

5. Dorvlo, A. S. S., Jervase, J. A. & Al-Lawati, A. Solar radiation estimation using artificial neural networks. Appl. Energy 71, 307–319 (2002).

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