Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison

Juan Flores, José R. Cedeño González, Héctor Rodríguez, Mario Graff, Rodrigo Lopez-Farias and Felix Calderon
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José R. Cedeño González: Division de Estudios de Posgrado, Facultad de Ingenieria Electrica, Universidad Michoacana de San Nicolas de Hidalgo, Avenida Francisco J. Mugica S/N, Ciudad Universitaria, Morelia 58030, Mexico
Héctor Rodríguez: Division de Estudios de Posgrado e Investigacion, Tecnologico Nacional de Mexico campus Culiacan, Juan de Dios Batiz 310 pte, Culiacan 80220, Mexico
Mario Graff: CONACYT—INFOTEC Centro de Investigacion e Innovacion en Tecnologias de la Informacion y Comunicacion, Circuito Tecnopolo Sur No 112, Fracc. Tecnopolo Pocitos II, Aguascalientes 20313, Mexico
Rodrigo Lopez-Farias: CONACYT—CentroGeo, Centro de Investigación en Ciencias de Información Geoespacial, Contoy 137, Col. Lomas de Padierna, Delegación Tlalpan, CDMX 14240, Mexico
Felix Calderon: Division de Estudios de Posgrado, Facultad de Ingenieria Electrica, Universidad Michoacana de San Nicolas de Hidalgo, Avenida Francisco J. Mugica S/N, Ciudad Universitaria, Morelia 58030, Mexico

Energies, 2019, vol. 12, issue 18, 1-19

Abstract: 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.

Keywords: time series forecasting; wind speed forecasting; machine learning (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: 2019
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