摘要：AbstractIn this paper, online Gaussian process regression (GPR) is used to model and forecast Global Horizontal Irradiance, at forecast horizons ranging from 30 min to 5 h. It is shown that the covariance function (or kernel) is a key element, deeply influencing forecast results. As a consequence, Gaussian processes with simple kernels and with more complex kernels have been tested and compared to the classic persistence model. Using two datasets of 45 days, it is shown that online GPR models based on quasiperiodic kernels outperform both the persistence model and GPR models based on simple kernels, including the widely used squared exponential kernel.
