The emergence of the power pool as a popular
institution for trading of power in different countries
has led to increased interest in the prediction of
power demand and price. We investigate whether the time
series of power-pool demand and price can be modelled
as the output of a low-dimensional chaotic dynamical
system by using delay embedding and estimation of the
embedding dimension, attractor-dimension or
correlation-dimension calculation, Lyapunov-spectrum
and Lyapunov-dimension calculation, stationarity and
nonlinearity tests, as well as prediction analysis.
Different dimension estimates are consistent and show
close similarity, thus increasing the credibility of
the fractal-dimension estimates. The Lyapunov spectrum
consistently shows one positive Lyapunov exponent and
one zero exponent with the rest being negative,
pointing to the existence of chaos. The authors then
propose a least squares genetic programming (LS-GP) to
reconstruct the nonlinear dynamics from the power-pool
time series. Compared to some standard predictors
including the radial basis function (RBF) neural
network and the local state-space predictor, the
proposed method does not only achieve good prediction
of the power-pool time series but also accurately
predicts the peaks in the power price and demand based
on the data sets used in the present study.
%0 Journal Article
%1 VLB06
%A Varadan, Vinay
%A Leung, Henry
%A Bosse, Eloi
%D 2006
%J IEEE Transactions on Instrumentation and Measurement
%K (RBF) Chaos, GP, Lyapunov Lyapunov-dimension Lyapunov-spectrum, algorithms, analysis, and approximations, attractor-dimension, basis calculation, chaos, chaotic correlation-dimension delay demand demand, dimension, dynamical dynamics, embedding embedding, estimates, estimation, exponents, fractal fractal-dimension fractals, function genetic least local low-dimensional markets, methods, model net network, neural nonlinear nonlinearity power power-pool prediction prediction, predictor, price price, programming, radial reconstruction, series series, squares state-space stationarity system, systems, tests, theory, time time-series
%N 1
%P 327--336
%R doi:10.1109/TIM.2005.861492
%T Dynamical model reconstruction and accurate prediction
of power-pool time series
%V 55
%X The emergence of the power pool as a popular
institution for trading of power in different countries
has led to increased interest in the prediction of
power demand and price. We investigate whether the time
series of power-pool demand and price can be modelled
as the output of a low-dimensional chaotic dynamical
system by using delay embedding and estimation of the
embedding dimension, attractor-dimension or
correlation-dimension calculation, Lyapunov-spectrum
and Lyapunov-dimension calculation, stationarity and
nonlinearity tests, as well as prediction analysis.
Different dimension estimates are consistent and show
close similarity, thus increasing the credibility of
the fractal-dimension estimates. The Lyapunov spectrum
consistently shows one positive Lyapunov exponent and
one zero exponent with the rest being negative,
pointing to the existence of chaos. The authors then
propose a least squares genetic programming (LS-GP) to
reconstruct the nonlinear dynamics from the power-pool
time series. Compared to some standard predictors
including the radial basis function (RBF) neural
network and the local state-space predictor, the
proposed method does not only achieve good prediction
of the power-pool time series but also accurately
predicts the peaks in the power price and demand based
on the data sets used in the present study.
@article{VLB06,
abstract = {The emergence of the power pool as a popular
institution for trading of power in different countries
has led to increased interest in the prediction of
power demand and price. We investigate whether the time
series of power-pool demand and price can be modelled
as the output of a low-dimensional chaotic dynamical
system by using delay embedding and estimation of the
embedding dimension, attractor-dimension or
correlation-dimension calculation, Lyapunov-spectrum
and Lyapunov-dimension calculation, stationarity and
nonlinearity tests, as well as prediction analysis.
Different dimension estimates are consistent and show
close similarity, thus increasing the credibility of
the fractal-dimension estimates. The Lyapunov spectrum
consistently shows one positive Lyapunov exponent and
one zero exponent with the rest being negative,
pointing to the existence of chaos. The authors then
propose a least squares genetic programming (LS-GP) to
reconstruct the nonlinear dynamics from the power-pool
time series. Compared to some standard predictors
including the radial basis function (RBF) neural
network and the local state-space predictor, the
proposed method does not only achieve good prediction
of the power-pool time series but also accurately
predicts the peaks in the power price and demand based
on the data sets used in the present study.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Varadan, Vinay and Leung, Henry and Bosse, Eloi},
biburl = {https://www.bibsonomy.org/bibtex/2fe815f3547f94a14d0f88792dd21cbf9/brazovayeye},
doi = {doi:10.1109/TIM.2005.861492},
interhash = {e40cae731f32e30083ce8870c15f8991},
intrahash = {fe815f3547f94a14d0f88792dd21cbf9},
issn = {0018-9456},
journal = {IEEE Transactions on Instrumentation and Measurement},
keywords = {(RBF) Chaos, GP, Lyapunov Lyapunov-dimension Lyapunov-spectrum, algorithms, analysis, and approximations, attractor-dimension, basis calculation, chaos, chaotic correlation-dimension delay demand demand, dimension, dynamical dynamics, embedding embedding, estimates, estimation, exponents, fractal fractal-dimension fractals, function genetic least local low-dimensional markets, methods, model net network, neural nonlinear nonlinearity power power-pool prediction prediction, predictor, price price, programming, radial reconstruction, series series, squares state-space stationarity system, systems, tests, theory, time time-series},
month = {February},
notes = {INSPEC Accession Number:8768025
Dept. of Electr. Eng., Columbia Univ., New York, NY,
USA},
number = 1,
pages = {327--336},
size = {10 pages},
timestamp = {2008-06-19T17:53:36.000+0200},
title = {Dynamical model reconstruction and accurate prediction
of power-pool time series},
volume = 55,
year = 2006
}