Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/243661 
Year of Publication: 
2019
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1172-1184
Publisher: 
Elsevier, Amsterdam
Abstract: 
The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 m) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. As such, the hybrid models are a good method to forecast wind speed data for wind generation.
Subjects: 
ANN model
Forecasting
Hybrid model
Wind speed
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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