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Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China

Fan, Junliang, Wang, Xiukang, Wu, Lifeng, Zhou, Hanmi, Zhang, Fucang, Yu, Xiang, Lu, Xianghui, Xiang, Youzhen·2018·Energy Conversion and Management
DOI 10.1016/j.enconman.2018.02.087
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Research works printed but not linked: 33

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Metadata

Authors
Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X., & Xiang, Y.
Venue
Energy Conversion and Management
Publisher
Elsevier BV
Published
2018-05
Volume
164
Pages
102-111
ISSN
0196-8904 (ISSN-L)
DOI
10.1016/j.enconman.2018.02.087
OpenAlex
W2792986592
Cited by
641
References
38
FWCI
28.29
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Key Research and Development Program of China (2016YFC0400201)
  • the National Natural Science Foundation of China (61703199, 51509208)
  • Jiangxi Natural Science Foundation of China (20171BAB216051)
  • Scientific Startup Foundation for Doctors of Northwest A&F University (Z109021613)
  • “111” Project (B12007)

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CoChat. (2026). Citation check: Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. https://app.cochat.ai/papers/10.1016/j.enconman.2018.02.087
The paper
Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X., & Xiang, Y. (2018). Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2018.02.087

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