Leveraging turbine-level data for improved probabilistic wind power forecasting

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This paper describes two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data. The first is a feature engineering approach whereby deterministic power forecasts from the turbine level are used as explanatory variables in a wind farm level forecasting model. The second is a novel bottom-up hierarchical approach where the wind farm forecast is inferred from the joint predictive distribution of the power output from individual turbines. Notably, the latter produces probabilistic forecasts that are coherent across both turbine and farm levels, which the former does not. The methods are tested at two utility scale wind farms and are shown to provide consistent improvements of up to 5%, in terms of continuous ranked probability score compared to the best performing state-of-the-art benchmark model. The bottom-up hierarchical approach provides greater improvement at the site characterized by a complex layout and terrain, while both approaches perform similarly at the second location. We show that there is a clear benefit in leveraging readily available turbine-level information for wind power forecasting.

Original languageEnglish
Pages (from-to)1152-1160
Number of pages9
JournalIEEE Transactions on Sustainable Energy
Issue number3
Early online date6 Jun 2019
Publication statusPublished - 31 Jul 2020


  • wind power forecasting
  • turbine-level data
  • wind farm

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