A model tree approach to forecasting solar irradiance variability

As the penetration of solar power increases, the variable generation from this renewable resource will necessitate solar irradiance forecasts for utility companies to balance the energy grid. In this study, the temporal irradiance variability is calculated by the temporal standard deviation of the Global Horizontal Irradiance (GHI) at eight sites in the Sacramento Valley and the spatial irradiance variability is quantified by the standard deviation across those same sites. Our proposed artificial intelligence forecasting technique is a model tree with a nearest neighbor option to predict the irradiance variability directly. The model tree technique reduces the mean absolute error of the variability prediction between 10% and 55% compared to using climatological average values of the temporal and spatial GHI standard deviation. These forecasts are made at 15-min intervals out to 180-min. A data denial experiment showed that the addition of surface weather observations improved the forecasting skill of the model tree by approximately 10%. These results indicate that the model tree technique can be implemented in real-time to produce solar variability forecasts to aid utility companies in energy grid management.