IngentaConnect Calibrated Probabilistic Forecasting at the Stateline Wind Energy...

Authors: Gneiting, Tilmann¹; Larson, Kristin²; Westrick, Kenneth³; Genton, Marc G.⁴; Aldrich, Eric⁵

Source: Journal of the American Statistical Association, Volume 101, Number 475, September 2006 , pp. 968-979(12)

Publisher: American Statistical Association

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 ESTIMA; PREDICTIVE DISTRIBUTION; SPATIOTEMPORAL; TRUNCATED NORMAL; WEATHER PREDICTION

Document Type: Research article

DOI: 10.1198/016214506000000456

Affiliations: 1: Associate Professor, Department of Statistics, University of Washington, Seattle, WA 98195 2: Senior Research Meteorologist, 3 Tier Environmental Forecast Group, Inc., Seattle, WA 98121 3: Founder and CEO, 3 Tier Environmental Forecast Group, Inc., Seattle, WA 98121 4: Associate Professor, Department of Statistics, Texas A&M University, College Station, TX 77843 5: Ph. D. Student, Department of Economics, Duke University, Durham, NC 27708

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