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dc.contributor.authorChen, Hao
dc.contributor.authorBirkelund, Yngve
dc.contributor.authorBatalden, Bjørn-Morten
dc.contributor.authorBarabadi, Abbas
dc.date.accessioned2022-06-13T06:26:40Z
dc.date.available2022-06-13T06:26:40Z
dc.date.issued2022-06-10
dc.description.abstractThe day-ahead wind power forecast is essential for the designation of dispatch schedules for the grid and rational arrangement for production planning by power generation companies. This paper specifically investigates the effect of adding noise to the original wind data for forecasting models. Linear regression, artificial neural networks, and adaptive boosting predictive models based on data-intensification white noise and uniform noise are evaluated in detail and their superiority over the original data-based models is compared. The results demonstrate that solely injecting noise into the dataset can statistically boost the performance of all forecasting models with learning algorithms. The findings of this study suggest a fresh perspective for developing wind power prediction models and carry certain wind energy engineering merits.en_US
dc.identifier.citationChen, Birkelund, Batalden, Barabadi. Noise-intensification data augmented machine learning for day-ahead wind power forecast. Energy Reports. 2022en_US
dc.identifier.cristinIDFRIDAID 2030987
dc.identifier.doi10.1016/j.egyr.2022.05.265
dc.identifier.issn2352-4847
dc.identifier.urihttps://hdl.handle.net/10037/25449
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalEnergy Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleNoise-intensification data augmented machine learning for day-ahead wind power forecasten_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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