Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space–Time Method

Abstract

With the global proliferation of wind power, accurate short-term forecasts of wind resources at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological and statistical expertise to obtain accurate and calibrated, fully proba-bilistic forecasts of wind speed and wind power. The model formulation is parsimonious, yet takes account of all the salient features of wind speed: alternating atmospheric regimes, tempo-ral and spatial correlation, diurnal and seasonal non-stationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identies forecast regimes at the wind energy site and ts a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as o-site predictors. The RST technique was applied to 2-hour ahead forecasts of hourly average wind speed at the Stateline wind farm in the US Pacic Northwest. In July 2003, for instance, the RST forecasts had root-mean-square error (RMSE) 28.6 % less than the persistence forecasts. For each month in the test period, the RST forecasts had lower RMSE than forecasts using state-of-the-art vector time series techniques. The RST method provides probabilistic forecasts in the form of