Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss
Stockholm Univ, Bolin Ctr Climate Res, Dept Meteorol, S-10691 Stockholm, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
2019 (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
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 7, p. 129421-129429
Keywords [en]
Wind energy, machine learning, weather forecasting
National Category
Climate Research
Identifiers
URN: urn:nbn:se:uu:diva-395563DOI: 10.1109/ACCESS.2019.2939657ISI: 000487235500012OAI: oai:DiVA.org:uu-395563DiVA, id: diva2:1362644
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21Bibliographically approved

Open Access in DiVA

fulltext(9212 kB)12 downloads
File information
File name FULLTEXT01.pdfFile size 9212 kBChecksum SHA-512
54c72cd948dd7f57060769c4a0911f098e4d25c229441d45853e8fac414ea31a9da4106afac744a5561714746e679ffa3170f3f00569591f15c0ba586045c661
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Molinder, Jennie
By organisation
LUVAL
In the same journal
IEEE Access
Climate Research

Search outside of DiVA

GoogleGoogle Scholar
Total: 12 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 14 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf