An ensemble forecasting model of wind power outputs based on improved statistical approaches

Yeojin Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method.

Original languageEnglish
Article number1071
JournalEnergies
Volume13
Issue number5
DOIs
StatePublished - 1 Mar 2020

Keywords

  • Autoregressive integrated moving average with exogenous variable
  • Ensemble method
  • Power curve modeling
  • Support vector regression
  • Wind power forecasting

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