An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic
Permanent link
https://hdl.handle.net/10037/24257Date
2021-12-23Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
Wind 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.
Publisher
IOP PublishingCitation
Chen 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). 2021:1-7Metadata
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