Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) 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 probabilistic 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, temporal and spatial correlation, diurnal and seasonal non-stationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at the wind energy site and fits a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as off-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 Pacific 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 predictive cumulative distribution functions, and those were well calibrated and sharp. The RST prediction intervals were substantially shorter on average than prediction intervals derived from univariate time series techniques. These results suggest that quality meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts of wind resources. It is anticipated that the RST technique can be successfully applied at wind energy sites all over the world. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

Probabilistic Forecasts, Calibration and Sharpness

“…Gneiting et al (2004) proposed a novel spatiotemporal approach, the regime-switching space-time (RST) method, that merges meteorological and statistical expertise to obtain fully probabilistic forecasts of wind resources. Henceforth, we illustrate our diagnostic approach to the evaluation of predictive performance by a comparison and ranking of three competing methodologies for 2-hour-ahead forecasts of hourly average wind speed at the Stateline wind energy centre.…”

“…The predictive distribution assigns a typically small positive mass to the negative half-axis, and, in view of the non-negativity of the predictand, we redistribute this mass to wind speed 0. The details are described in Gneiting et al (2004), where the method is referred to as the AR-D technique. The third method is the RST approach of Gneiting et al (2004).…”

“…The details are described in Gneiting et al (2004), where the method is referred to as the AR-D technique. The third method is the RST approach of Gneiting et al (2004). The RST model is parsimonious yet takes account of all the salient features of wind speed: alternating atmospheric regimes, temporal and spatial autocorrelation, diurnal and seasonal non-stationarity, conditional heteroscedasticity and non-Gaussianity.…”

“…The method uses off-site information from the nearby meteorological towers at Goodnoe Hills and Kennewick, identifies atmospheric regimes and fits conditional predictive models for each regime, based on a sliding 45-day training period. Details can be found in Gneiting et al (2004), where the method is referred to as the RST-D-CH technique. Any minor discrepancies in the results that are reported below and in Gneiting et al (2004) stem from the use of R rather than S-PLUS and differences in optimization routines.…”

“…Details can be found in Gneiting et al (2004), where the method is referred to as the RST-D-CH technique. Any minor discrepancies in the results that are reported below and in Gneiting et al (2004) stem from the use of R rather than S-PLUS and differences in optimization routines.…”