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

Journal of the American Statistical Association, Vol. 101, pp. 968-979

12 Pages Posted: 22 Dec 2012

See all articles by Tilmann Gneiting

Tilmann Gneiting

University of Washington - Department of Statistics and Biostatistics

Kristin Larson

3TIER Environmental Forecast Group, Inc.

Kenneth Westrick

3TIER Environmental Forecast Group, Inc.

Marc Genton

Texas A&M University (TAMU) - Department of Statistics

Eric M. Aldrich

University of California, Santa Cruz

Date Written: September 1, 2006

Abstract

With the global proliferation of wind power, the need for accurate short-term forecasts of wind resources at wind energy sites is 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 into account all of the salient features of wind speed: alternating atmospheric regimes, temporal and spatial correlation, diurnal and seasonal nonstationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at a 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 near the Stateline wind energy center in the U.S. Pacific Northwest. The RST point forecasts and distributional forecasts were accurate, calibrated, and sharp, and they compared favorably with predictions based on state-of-the-art time series techniques. This suggests that quality meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts of wind resources.

Keywords: continuous ranked probability score, minimum continuous ranked probability score estimation, predictive distribution, Spatiotemporal, truncated normal, weather prediction

Suggested Citation

Gneiting, Tilmann and Larson, Kristin and Westrick, Kenneth and Genton, Marc and Aldrich, Eric Mark, Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space–Time Method (September 1, 2006). Journal of the American Statistical Association, Vol. 101, pp. 968-979, Available at SSRN: https://ssrn.com/abstract=1515764

Tilmann Gneiting (Contact Author)

University of Washington - Department of Statistics and Biostatistics ( email )

Seattle, WA
United States

Kristin Larson

3TIER Environmental Forecast Group, Inc. ( email )

2001 Sixth Avenue
Suite 2100
Seattle, WA
United States

Kenneth Westrick

3TIER Environmental Forecast Group, Inc. ( email )

2001 Sixth Avenue
Suite 2100
Seattle, WA
United States

Marc Genton

Texas A&M University (TAMU) - Department of Statistics ( email )

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Eric Mark Aldrich

University of California, Santa Cruz ( email )

Santa Cruz, CA 95064
United States
831-459-4247 (Phone)

HOME PAGE: http://ealdrich.com

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