We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions (NWP) and historical measurements. The wind speed forecast is then represented as stochastic predictive scenarios that are targeted for power grid applications where they account for the uncertainty associated with renewable energy generation. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements along with NWP model predictions in order to produce a probabilistic wind speed forecast within the prediction window. The process is expressed hierarchically in order to facilitate the specification of cross-variances between the two datasets. We illustrate this strategy on wind speed forecast during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, the samples are shown to produce realistic wind scenarios based on sample spectra.

Emil Constantinescu and Mihai Anitescu are co-authored on this work.