A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques

Eun Ji Ahn, Jin Hur

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


South Korea has announced a plan to increase the proportion of renewable energy generation to 20% and reduce traditional energy generation by 2030. Among renewable energy resources, wind power has the advantage of relatively low power generation costs. However, it is difficult to forecast, as the output varies significantly depending on changing wind conditions such as the temperature, wind speed, and wind direction. We believe that short-term wind energy forecasts are the most important part for coping with these fluctuations and minimizing scheduling errors, thereby making the grid more reliable and reducing market service costs. Accordingly, we proposed a practical short-term wind power output forecasting method using a novel ensemble model based on a wavelet transform and autoregressive integrated moving average with explanatory variable (ARIMAX) approach. To demonstrate that the model has a good forecasting performance, we applied historical wind speed and wind power output data obtained from Jeju Island's wind farm to the model, and compared them with forecasted values. The normalized mean absolute error (NMAE) was used as the error metric. The comparison results were described for three supervisory control and data acquisition points. The average NMAE was approximately 3%. In addition, an N-1 contingency analysis was conducted to check the voltage profiles and flow limits in the context of a real power system, to ensure that the power system operated stably even with the forecasted values. The system worked successfully with the forecasted values, and can be deployed as application software for energy management systems in South Korea.

Original languageEnglish
Pages (from-to)394-402
Number of pages9
JournalRenewable Energy
StatePublished - Aug 2023

Bibliographical note

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  • ARIMAX techniques
  • Contingency analysis
  • Ensemble model
  • Short-term wind power output forecasting
  • Wavelet transform


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