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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2004
Accession Number
ADA459831

Entities

People

  • Eric Aldrich
  • Kenneth Westrick
  • Kristin Larson
  • Marc G. Genton
  • Tilmann Gneiting

Organizations

  • University of Washington

Tags

DTIC Thesaurus Topics

  • Computational Science
  • Data Mining
  • Data Science
  • Distribution Functions
  • Energy
  • Information Science
  • Meteorological Phenomena
  • Meteorology
  • Normal Distribution
  • Predictive Modeling
  • Probability
  • Probability Distributions
  • Renewable Energy
  • Statistical Algorithms
  • Weather Forecasting
  • Wind Energy
  • Wind Turbines

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Distributed Systems and Data Platform Development
  • Statistical inference.

Technology Areas

  • Space