articleTop 1% cited
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 · 2018 · Vol. 164 · pp. 102–111
Junliang Fan(Northwest A&F University)Xiukang Wang(Yan'an University)Lifeng Wu✉(Nanchang Institute of Technology)Hanmi Zhou(Henan University of Science and Technology)Fucang Zhang(Northwest A&F University)Xiang Yu(Nanchang Institute of Technology)Xianghui Lu(Nanchang Institute of Technology)Youzhen Xiang(Northwest A&F University)
Solar Radiation and PhotovoltaicsPhotovoltaic System Optimization TechniquesEnergy Load and Power ForecastingSupport vector machineGradient boostingMachine learningEmpirical modellingPrecipitationBoosting (machine learning)Environmental scienceMeteorologyMean squared errorExtreme learning machine
Funding
- National Natural Science Foundation of China
- Higher Education Discipline Innovation Project
- National Key Research and Development Program of China
- Scientific Startup Foundation for Doctors of Northwest A and F University
Citations
624
FWCI
34.35
field-weighted impact
References
73
Percentile
100%
vs. same field & year
Citations per year
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References
On the relationship between incoming solar radiation and daily maximum and minimum temperature
Agricultural and Forest Meteorology · 1984 · 1,093 citations
Machine learning methods for solar radiation forecasting: A review
Renewable Energy · 2017 · 1,645 citations
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