EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving 0-24 h offshore wind power forecasts over the Baltic Sea: comparing post-processing methods of varying complexity

Christoffer Hallgren1, Stefan Ivanell1, Heiner Körnich2, Ville Vakkari3, 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 and Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa

Accurately forecasting short-term wind power production is a challenging task. As the share of wind power in the electrical system is rapidly growing, this task is becoming increasingly important not only for power production companies but also for transmission system operators. By applying post-processing methods to forecasts of wind speed from numerical weather prediction (NWP) models, power production forecasts can be improved. In this study, we used two years of lidar measurements of the wind speed from a coastal site in the Baltic Sea to calculate a theoretical power production and evaluated forecasts from the NWP model HARMONIE-AROME. Six post-processing methods of varying degree of complexity were implemented and tested in order to mimic how they could be used operationally. The performance of the methods in different weather situations was analysed in terms of the mean absolute error (MAE) skill score. For the test period it was found that, in general, the simple method of temporally smoothing the wind speed forecast by applying a low-pass filter (moving average) with a window of ±1 h outperformed the other methods tested. The main reason for this being a reduced risk of double penalty due to small time shifts in wind speed variations in the forecast compared to the observations. However, under weak synoptic forcing the best skill score was achieved using a mix of the forecast from the previous and the current day. Additionally, when low-level jets were forecasted, the best result was achieved using the machine learning random forest algorithm.

How to cite: Hallgren, C., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Improving 0-24 h offshore wind power forecasts over the Baltic Sea: comparing post-processing methods of varying complexity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13481,, 2022.