A Two-Step Short-Term Probabilistic Wind Forecasting Methodology Based on Predictive Distribution Optimization

Mucun Sun, Cong Feng, Erol Kevin Chartan, Bri Mathias Hodge, Jie Zhang

Research output: Contribution to journalArticlepeer-review

39 Scopus Citations

Abstract

With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Finally, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.

Original languageAmerican English
Pages (from-to)1497-1505
Number of pages9
JournalApplied Energy
Volume238
DOIs
StatePublished - 15 Mar 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5D00-72434

Keywords

  • Machine learning
  • Optimization
  • Pinball loss
  • Probabilistic wind forecasting
  • Surrogate model

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