Title

Soft computing methods with phase space reconstruction for wind speed forecasting—A performance comparison

Author

MARIO GRAFF GUERRERO

Juan Flores

José Cedeño

HECTOR RODRIGUEZ RANGEL

RODRIGO LOPEZ FARIAS

Felix Calderon

Access level

Open Access

Alternative identifier

pissn: 1996-1073

Publication reference

URL/https://www.mdpi.com/1996-1073/12/18/3545

Dataset reference

datasetDOI/https://doi.org/10.3390/en12183545

Summary or description

This article presents a comparison of wind speed forecasting techniques, starting with the Auto-regressive Integrated Moving Average, followed by Artificial Intelligence-based techniques. The objective of this article is to compare these methods and provide readers with an idea of what method(s) to apply to solve their forecasting needs. The Artificial Intelligence-based techniques included in the comparison are Nearest Neighbors (the original method, and a version tuned by Differential Evolution), Fuzzy Forecasting, Artificial Neural Networks (designed and tuned by Genetic Algorithms), and Genetic Programming. These techniques were tested against twenty wind speed time series, obtained from Russian and Mexican weather stations, predicting the wind speed for 10 days, one day at a time. The results show that Nearest Neighbors using Differential Evolution outperforms the other methods. An idea this article delivers to the reader is: what part of the history of the time series to use as input to a forecaster? This question is answered by the reconstruction of phase space. Reconstruction methods approximate the phase space from the available data, yielding m (the system’s dimension) and τ (the sub-sampling constant), which can be used to determine the input for the different forecasting methods.

Publisher

MDPI AG

Publish date

September 16, 2019

Publication type

Article

Publication version

Published Version

Format

application/pdf

Source

Energies, Volume 12, Issue 18, 16 September 2019, Article number 3545

Language

English

Citation suggestion

Flores, Juan.J., Cedeño González, J.R., Rodríguez, H., Graff, M., Lopez-Farias, R., Calderon, F., 2019. Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison. Energies 12, 3545. https://doi.org/10.3390/en12183545

Source repository

Repositorio Institucional de INFOTEC

Downloads

65

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