首页|Researchers from Tsinghua University Detail Findings in Machine Learning (Data Augmentation-Based Estimation of Solar Radiation Components without Referring to Local Ground Truth in China)
Researchers from Tsinghua University Detail Findings in Machine Learning (Data Augmentation-Based Estimation of Solar Radiation Components without Referring to Local Ground Truth in China)
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A new study on artificial intelligence is now available. According to news originating from Beijing, People’s Republic of China, by NewsRx editors, the research stated, “The power generation of bifacial photovoltaic modules is greatly related to the diffuse solar radiation component received by the rear side, but radiation component data are scarce in China, where bifacial solar market is large.” Financial supporters for this research include National Key Research And Development Program of China; National Natural Science Foundation of China. The news editors obtained a quote from the research from Tsinghua University: “Radiation components can be estimated from satellite data, but sufficient ground truth data are needed for calibrating empirical methods or training machine learning methods. In this work, a data-augmented machine learning method was proposed to estimate radiation components. Instead of using observed ground truth, far more abundant radiation component data derived from sunshine duration measured at 2,453 routine weather stations in China were used to augment samples for training a machine-learning-based model. The inputs of the model include solar radiation (either from ground observation or satellite remote sensing) and surface meteorological data. Independent validation of the model at Chinese stations and globally distributed stations demonstrates its effectiveness and generality. Using a state-of-the-art satellite product of solar radiation as input, the model is applied to construct a satellite-based radiation component dataset over China.”
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