An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic
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, s...
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ftunivtroemsoe:oai:munin.uit.no:10037/24257 2023-05-15T14:25:54+02:00 An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic Chen, Hao Birkelund, Yngve 2021-12-23 https://hdl.handle.net/10037/24257 https://doi.org/10.1088/1742-6596/2141/1/012016 eng eng IOP Publishing Journal of Physics: Conference Series (JPCS) 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). 2021:1-7 FRIDAID 1972089 doi:10.1088/1742-6596/2141/1/012016 1742-6588 1742-6596 https://hdl.handle.net/10037/24257 openAccess Copyright 2021 The Author(s) Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.1088/1742-6596/2141/1/012016 2022-03-09T23:57:52Z 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. Article in Journal/Newspaper Arctic Arctic University of Tromsø: Munin Open Research Archive Arctic Journal of Physics: Conference Series 2141 1 012016 |
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Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Chen, Hao Birkelund, Yngve |
spellingShingle |
Chen, Hao Birkelund, Yngve An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
author_facet |
Chen, Hao Birkelund, Yngve |
author_sort |
Chen, Hao |
title |
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
title_short |
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
title_full |
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
title_fullStr |
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
title_full_unstemmed |
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic |
title_sort |
evaluation on diverse machine learning algorithms for hourly univariate wind power prediction in the arctic |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/24257 https://doi.org/10.1088/1742-6596/2141/1/012016 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic |
genre_facet |
Arctic Arctic |
op_relation |
Journal of Physics: Conference Series (JPCS) 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). 2021:1-7 FRIDAID 1972089 doi:10.1088/1742-6596/2141/1/012016 1742-6588 1742-6596 https://hdl.handle.net/10037/24257 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.1088/1742-6596/2141/1/012016 |
container_title |
Journal of Physics: Conference Series |
container_volume |
2141 |
container_issue |
1 |
container_start_page |
012016 |
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1766298392449777664 |