Calibrated probabilistic forecasting at the stateline wind energy center: The regime-switching space-time method

Tilmann Gneiting*, Kristin Larson, Kenneth Westrick, Marc Genton, Eric Aldrich

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

172 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)968-979
Number of pages12
JournalJournal of the American Statistical Association
Volume101
Issue number475
DOIs
StatePublished - Sep 1 2006

Keywords

  • Continuous ranked probability score
  • Minimum continuous ranked probability score estimation
  • Predictive distribution
  • Spatiotemporal
  • Truncated normal
  • Weather prediction

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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