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https://doi.org/10.5194/wes-2017-28
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Research articles 18 Sep 2017

Research articles | 18 Sep 2017

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

Probabilistic forecasting of wind power production losses in cold climates: A case study

Jennie P. Söderman1, Heiner Körnich2, Esbjörn Olsson2, Hans Bergström1, and Anna Sjöblom1 Jennie P. Söderman et al.
  • 1Uppsala University, Uppsala, SWE
  • 2Swedish Meteorological and Hydrological Institute, Norrköping, SWE

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

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Jennie P. Söderman et al.
Jennie P. Söderman et al.
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Short summary
This study shows that using probabilistic forecasting can improve next-day production forecasts for wind energy in cold climates. Wind turbines can suffer from severe production losses due to icing on the turbine blades. Short-range forecasts including the icing-related production losses are therefore valuable when planning for next-day energy production. Probabilistic forecasting can also provide likelihood for icing and icing-related production losses.
This study shows that using probabilistic forecasting can improve next-day production forecasts...
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