Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prediction of Wind Speed Using Hybrid Techniques. Three locations: Colombia, Ecuador and Spain

Luis Lopez
* ORCID logo ,
Ingrid Oliveros
,
Luis Torres
,
Lacides Ripoll
,
Jose Soto
,
Giovanny Salazar
,
Santiago Cantillo
Version 1 : Received: 30 September 2020 / Approved: 5 October 2020 / Online: 5 October 2020 (14:06:29 CEST)

A peer-reviewed article of this Preprint also exists.

Lopez, L.; Oliveros, I.; Torres, L.; Ripoll, L.; Soto, J.; Salazar, G.; Cantillo, S. Prediction of Wind Speed Using Hybrid Techniques. Energies 2020, 13, 6284. Lopez, L.; Oliveros, I.; Torres, L.; Ripoll, L.; Soto, J.; Salazar, G.; Cantillo, S. Prediction of Wind Speed Using Hybrid Techniques. Energies 2020, 13, 6284.

Abstract

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

Keywords

Empirical Mode Decomposition; Hybrid techniques; LSSVM; Wavelet transform; Wind speed prediction

Subject

Engineering, Automotive Engineering

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.

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