Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jennie Molinder"'
Autor:
Sebastian Scher, Jennie Molinder
Publikováno v:
IEEE Access, Vol 7, Pp 129421-129429 (2019)
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
Externí odkaz:
https://doaj.org/article/8d7b5cc641f94ccc87bb4e5cd1f35ec8
Publikováno v:
Energies, Vol 14, Iss 1, p 158 (2020)
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
Externí odkaz:
https://doaj.org/article/92f6835e68944379a2b822a3cdcbc993
Publikováno v:
Energies; Volume 14; Issue 1; Pages: 158
Energies, Vol 14, Iss 158, p 158 (2021)
Energies, Vol 14, Iss 158, p 158 (2021)
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d092672824f223f1ecf7f7dbe204ae6c
A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6652c6834acb7b83e75f3ff94186b2e2
http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-5444
http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-5444
Publikováno v:
Wind Energy Science, Vol 3, Pp 667-680 (2018)
The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5484e2c4e2a06085a4279d09ac6adf57
http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-4995
http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-4995