A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

Brian Hodge, Venkat Krishnan, Mingjian Cui, Jie Zhang

Research output: Contribution to journalArticlepeer-review

61 Scopus Citations


Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic WPR forecast (cp-WPRF) model based on copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model is adopted to characterize the WPR uncertainty and features. The Canonical maximum likelihood method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

Original languageAmerican English
Article number8368287
Pages (from-to)3870-3882
Number of pages13
JournalIEEE Transactions on Smart Grid
Issue number4
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-70233


  • Conditional probabilistic forecast
  • copula theory
  • Gaussian mixture model
  • wind power ramps


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