Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting--A Performance Comparison
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{flores:2019:Energies,
-
author = "Juan. J. Flores and Jose R. {Cedeno Gonzalez} and
Hector Rodriguez and Mario Graff and
Rodrigo Lopez-Farias and Felix Calderon",
-
title = "Soft Computing Methods with Phase Space Reconstruction
for Wind Speed {Forecasting--A} Performance
Comparison",
-
journal = "Energies",
-
year = "2019",
-
volume = "12",
-
number = "18",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1996-1073",
-
URL = "https://www.mdpi.com/1996-1073/12/18/3545",
-
DOI = "doi:10.3390/en12183545",
-
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 systems dimension) and τ (the
sub-sampling constant), which can be used to determine
the input for the different forecasting methods.",
-
notes = "also known as \cite{en12183545}",
- }
Genetic Programming entries for
Juan J Flores
Jose R Cedeno Gonzalez
Hector Rodriguez
Mario Graff Guerrero
Rodrigo Lopez-Farias
Felix Calderon
Citations