Electricity power grid operations require information about demand and supply on a variety of time-scales and areas. The advent of significant generation contributions by time variable renewable energy sources means that forecasting methods are increasingly required. Some of the earliest requirements will be for spatial-temporal estimation of solar irradiance and the resulting photovoltaic-generated electricity. Accurate forecasts represent an important step towards building a smart grid for renewable energy driven cities or regions, and to this end we develop forecasting tools that use data from ground-based irradiance sensors. 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. (C) 2013 Elsevier Ltd. All rights reserved.