Vis enkel innførsel

dc.contributor.authorMorais da Costa, Bernardo
dc.contributor.authorSnæbjørnsson, Jonas Thor
dc.contributor.authorØiseth, Ole Andre
dc.contributor.authorWang, Jungao
dc.contributor.authorJakobsen, Jasna Bogunovic
dc.date.accessioned2022-02-16T11:47:27Z
dc.date.available2022-02-16T11:47:27Z
dc.date.created2021-11-26T10:40:31Z
dc.date.issued2021
dc.identifier.citationIOP Conference Series: Materials Science and Engineering. 2021, 1201 .en_US
dc.identifier.issn1757-8981
dc.identifier.urihttps://hdl.handle.net/11250/2979343
dc.description.abstractThis study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography and a historical dataset of wind measurements and topography at other locations. Five years of data from six different wind measurement mast locations were used. A k-fold cross-validation evaluated the model at each location, where four locations were used for the training data, another location was used for validation, and the remaining one to test the model. The model outperformed the approach given in the European standard, for both performance metrics used. The results of different hyperparameter optimizations are presented, allowing for uncertainty estimates of the model performances.en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleData-driven prediction of mean wind turbulence from topographic dataen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber10en_US
dc.source.volume1201en_US
dc.source.journalIOP Conference Series: Materials Science and Engineeringen_US
dc.identifier.doi10.1088/1757-899X/1201/1/012005
dc.identifier.cristin1959599
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal