Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging

Several variants of spatio-temporal kriging are used to perform very short-term solar irradiance forecasting by utilizing data from a sensor network. Kriging can produce forecasts not only at the locations of the irradiance monitoring stations, but also at locations where sensors are not installed. Leave-one-out cross-validation is used to test the kriging performance at unobserved locations. Kriging weights are determined either empirically or using a correlation function. Four parametric correlation functions (correlograms) are herein considered, namely, separable, fully symmetric, and two polynomial-adjusted correlation functions. A dense 1 km × 1.2 km network of 17 stations located on Oahu island, Hawaii, is used in this paper. We find that kriging based on a polynomial-adjusted correlation function (the best among the parametric models) is able to obtain forecast skill up to 0.43 and 0.36 for observed and unobserved locations respectively, for a forecast horizon of 50 s. It is also found that empirical kriging performs better than parametric models at small forecast horizons (such as 30 s). However, it loses accuracy for forecast horizons longer than 100 s.