Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.

Automated summary

A probabilistic machine learning method, quantile regression forests, is applied to forecast icing-related production losses in wind energy in cold climates. The method has shown to produce valuable probabilistic forecasts and the output from a physical icing model enhances forecast skill when combined with Numerical Weather Prediction data. Training data from other icing-affected stations can also increase the forecast accuracy by providing additional data which poses a challenge in forecasting for wind energy in cold climates.