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Machine Learning-Ba...
Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss
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- Scher, Sebastian (författare)
- Stockholms universitet,Meteorologiska institutionen (MISU),Stockholm Univ, Bolin Ctr Climate Res, Dept Meteorol, S-10691 Stockholm, Sweden
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- Molinder, Jennie (författare)
- Uppsala universitet,Luft-, vatten- och landskapslära
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(creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
- 2019
- Engelska.
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 129421-129429
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)
Nyckelord
- Wind energy
- machine learning
- weather forecasting
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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