Version 1
: Received: 25 July 2018 / Approved: 26 July 2018 / Online: 26 July 2018 (04:22:14 CEST)
How to cite:
Ferreira, M.; Santos, A.; Lucio, P. Short-Term Forecast of Wind Speed through Mathematical Models. Preprints2018, 2018070501. https://doi.org/10.20944/preprints201807.0501.v1
Ferreira, M.; Santos, A.; Lucio, P. Short-Term Forecast of Wind Speed through Mathematical Models. Preprints 2018, 2018070501. https://doi.org/10.20944/preprints201807.0501.v1
Ferreira, M.; Santos, A.; Lucio, P. Short-Term Forecast of Wind Speed through Mathematical Models. Preprints2018, 2018070501. https://doi.org/10.20944/preprints201807.0501.v1
APA Style
Ferreira, M., Santos, A., & Lucio, P. (2018). Short-Term Forecast of Wind Speed through Mathematical Models. Preprints. https://doi.org/10.20944/preprints201807.0501.v1
Chicago/Turabian Style
Ferreira, M., Alexandre Santos and Paulo Lucio. 2018 "Short-Term Forecast of Wind Speed through Mathematical Models" Preprints. https://doi.org/10.20944/preprints201807.0501.v1
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 meters) 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. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.
Keywords
wind speed; ANN model; hybrid model
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
