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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods are evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend to select which method to use based on the forecasted weather.
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https://doi.org/10.5194/wes-2021-31
https://doi.org/10.5194/wes-2021-31

  17 May 2021

17 May 2021

Review status: this preprint is currently under review for the journal WES.

The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

Christoffer Hallgren1, Stefan Ivanell1, Heiner Körnich2, Ville Vakkari3,4, and Erik Sahlée1 Christoffer Hallgren et al.
  • 1Department of Earth Sciences, Uppsala University, Uppsala, Sweden
  • 2Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • 3Finnish Meteorological Institute, Helsinki, Finland
  • 4Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend to select which method to use based on the forecasted weather conditions. Combining forecasts from neighbouring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

Christoffer Hallgren et al.

Status: open (until 28 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Christoffer Hallgren et al.

Christoffer Hallgren et al.

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Short summary
As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods are evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend to select which method to use based on the forecasted weather.
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