Study Results from University College Dublin Broaden Understanding of Machine Learning (Wind Power Forecasting Using Ensemble Learning for Day-ahead Energy Trading)
- Jun 23, 2022 4:40 am GMT
2022 JUN 22 (NewsRx) -- By a
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According to the news reporters, the research concluded: “Our analysis and experimental results show how boosting ensembles are a cost-effective solution in terms of runtime among other Machine Learning algorithms predicting wind power a day ahead.”
This research has been peer-reviewed.
For more information on this research see: Wind Power Forecasting Using Ensemble Learning for Day-ahead Energy Trading. Renewable Energy, 2022;191:685-698. Renewable Energy can be contacted at: Pergamon-elsevier
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The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.renene.2022.04.032. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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