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Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss
Stockholm University, Faculty of Science, Department of Meteorology .
Number of Authors: 22019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 129421-129429Article in journal (Refereed) Published
Abstract [en]

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 similar to 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.

Place, publisher, year, edition, pages
2019. Vol. 7, p. 129421-129429
Keywords [en]
Wind energy, machine learning, weather forecasting
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-174990DOI: 10.1109/ACCESS.2019.2939657ISI: 000487235500012OAI: oai:DiVA.org:su-174990DiVA, id: diva2:1361520
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16Bibliographically approved

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