Short-term wind forecasting using spatio-temporal covariance models

This talk introduces a methodology for improving short-term wind speed forecasting in Alberta. Regime-switching spatio-temporal covariance models are applied using two datasets: (1) large-scale reanalysis dataset containing large scale atmospheric information for atmospheric clustering using k-means and hidden Markov models; (2) wind speed data from 131 weather stations across Alberta are used to train and test the covariance models. The predictive performance is assessed for different models and clustering methods.