Forecasting global solar radiation using a robust regularization approach with mixture kernels

Accurately forecasting global solar radiation plays a key role in photovoltaic evaluations. To quantify and control the uncertainties in global solar radiation forecasting, this study developed a robust and accurate forecasting model. This was constructed in the reproducing kernel Hilbert space with a novel regularization. Global solar radiation datasets were collected from the autonomous region of Tibet in China. Experimental results demonstrate that the proposed model can quantify uncertainties and obtain more accurate forecasting compared with machine learning models.