Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging

Spatial-temporal datasets that enjoy the properties of stationarity, full symmetry and separability are in general more amenable to forecasting using time-forward kriging algorithms. Usually, none of these properties obtain in meteorological data such as wind velocity fields and solar irradiance distributions. In this paper, we construct a statistical forecast system to mitigate this problem. We first achieve temporal stationarity by detrending solar irradiance time series at individual monitoring stations. We then approximate spatial stationarity through deformations of the geographic coordinates. Various spatial-temporal variance-covariance structures are formed to explore full symmetry and separability. Finally, time-forward kriging is used to forecast the hourly spatial-temporal solar irradiance data from 10 Singapore weather stations. The aim of the proposed system is to forecast irradiance and PV electricity generation at arbitrary spatial locations within a monitored area.