Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{JIANG:2020:ECM,
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author = "Yan Jiang and Shuoyu Liu and Ning Zhao and
Jingzhou Xin and Bo Wu",
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title = "Short-term wind speed prediction using time varying
filter-based empirical mode decomposition and group
method of data handling-based hybrid model",
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journal = "Energy Conversion and Management",
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volume = "220",
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pages = "113076",
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year = "2020",
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ISSN = "0196-8904",
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DOI = "doi:10.1016/j.enconman.2020.113076",
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URL = "http://www.sciencedirect.com/science/article/pii/S0196890420306208",
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keywords = "genetic algorithms, genetic programming, Short-term
wind speed prediction, Time varying filter-based
empirical mode decomposition, Group method of data
handling neural network, Nonlinear residuals, Selective
prediction",
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abstract = "The realization of precise and reliable short-term
wind speed prediction is extremely essential to wind
power development, especially for its integration into
traditional grid system. For this purpose, this study
develops a novel forecasting method based on time
varying filter-based empirical mode decomposition,
auto-regressive integrated moving average model and
group method of data handling-based hybrid model. This
method mainly contains four individual steps for
grasping the major behavioral characteristics of wind
speed data. The first step adopts time varying
filter-based empirical mode decomposition to handle the
nonlinearity and nonstationarity of the raw wind speed
data by decomposing them into a number of subseries
with more stability and regularity. Then,
auto-regressive integrated moving average model is
applied to depict the linear characteristic hidden in
the data. For the above modeling errors (i.e., the
nonlinear residuals), the third step employs three
nonlinear models with different action mechanisms
(i.e., least square support vector machine, genetic
programming algorithm and spatio-temporal radial basis
function neural network) to systematically capture
their complex nonlinear features. Finally, group method
of data handling neural network is used to combine
these nonlinear models and perform the selective
prediction, where the involved models and their weights
could be determined automatically. Four groups of the
measured wind speed datasets with two different time
intervals are used to assess the performance of the
proposed method. The experimental results indicate it
outperforms the other compared models and may have
great potential for the practical application in power
system",
- }
Genetic Programming entries for
Yan Jiang
Shuoyu Liu
Ning Zhao
Jingzhou Xin
Bo Wu
Citations