Prediction of Wind Speed Using Hybrid Techniques

Luis Lopez, Ingrid Oliveros, Luis Torres, Lacides Ripoll, Jose Soto, Giovanny Salazar and Santiago Cantillo
Additional contact information
Luis Lopez: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Ingrid Oliveros: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Luis Torres: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Lacides Ripoll: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Jose Soto: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Giovanny Salazar: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
Santiago Cantillo: Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia

Energies, 2020, vol. 13, issue 23, 1-13

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 h 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. Moreover, 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 root mean square error (RMSE).

Keywords: empirical mode decomposition; hybrid techniques; LSSVM; wavelet transform; wind speed prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/23/6284/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/23/6284/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:23:p:6284-:d:452986

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().