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dc.contributor.authorChen, Hao
dc.contributor.authorBirkelund, Yngve
dc.date.accessioned2022-03-04T08:14:59Z
dc.date.available2022-03-04T08:14:59Z
dc.date.issued2021-12-23
dc.description.abstractWind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step hourly wind power predictions for five wind parks inside the Norwegian Arctic regions, and their performance is compared. Consequently, we recommend the persistence model, multilayer perceptron, and support vector regression for univariate time-series wind power forecasting within the time horizon of 3 hours.en_US
dc.identifier.citationChen H, Birkelund Y. An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic. Journal of Physics: Conference Series (JPCS). 2021:1-7en_US
dc.identifier.cristinIDFRIDAID 1972089
dc.identifier.doi10.1088/1742-6596/2141/1/012016
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttps://hdl.handle.net/10037/24257
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.relation.journalJournal of Physics: Conference Series (JPCS)
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleAn Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arcticen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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