Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss

Abstract

Ice-growth on wind-turbines can lead to a large reduction of energy production. Since ice-growth on the turbines is not part of standard weather prediction data, forecasts of power production can have large errors when ice-growth occurs. We propose a statistical method based on random-forest regression to predict the production loss induced by ice-growth. It takes as input both regional weather forecasts and on-site measurements, and predicts relative power production loss up to 42 hours ahead in order to improve the prediction for the next-day energy production. The method is trained on past forecasts and measurements, and significantly outperforms a simple - but also useful - persistence baseline especially at longer lead times. It reduces the absolute error of production forecasts by ~100kW and is comparable in skill to physics-based icing models. The weather prediction data is the most important input for the statistical predictions, and on-site measurements are not absolutely necessary. The algorithm is computationally very inexpensive and can easily be retrained for every new forecast.

Keywords

• Wind energy
• machine learning
• weather forecasting