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An
ANN and wavelet transformation based method for short term load forecast |
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Energy
Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 |
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International
Conference on , Volume: 2 , 1998 |
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A new
method for short term load forecast based on an artificial neural network
(ANN) |
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and
wavelet transformation is presented in this paper. The load series is
mapped onto |
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some
sub-series with wavelet transformation and then the sub-series are
forecast by |
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ANN.
Weather factors are taken into account in forecasting. After all sub-series
of load |
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series
are forecast, the whole predicted load series can be composed or
reconstructed. In |
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addition,
a new BP algorithm is proposed to speed up the training process and
improve the |
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convergence
of the ANN. All experimental results show the correctness of the
principles |
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proposed
and the feasibility of the algorithm.
|
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A
hybrid wavelet-Kalman filter method for load forecasting |
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Tongxin
Zheng, Adly A. Girgis and Elham B. Makram |
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Electric
Power Systems Research, Volume 54, Issue 1, 5 April 2000, Pages 11-17 |
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This
paper presents a wavelet transform method for load forecasting. The
stochastic |
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nature
of the wavelet coefficients for the daily load variation is studied by the |
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decomposition
scheme of multiresolution analysis (MRA). The study indicates that the |
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stochastic
process of the wavelet coefficients can be modeled as a random walk
process. |
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Therefore,
the wavelet coefficients are modeled as the state variables of Kalman
filters. |
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The
best estimation of the wavelet coefficients is obtained by the recursive
Kalman filter |
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algorithm.
The predicted daily load is the inverse of the predicted wavelet
coefficients. |
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Based
on the above procedure, two models (weather insensitive and sensitive
models) are |
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presented
in this paper. Results from an actual system are also presented.
|
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A
Novel Short-Term Load Forecasting Technique Using Wavelet Transform |
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IN-KEUN
YU ; CHANG-IL KIM ; Y. H. SONG |
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Electric
Machines and Power Systems, 28:537–549, 2000 |
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Copyright
c s2000 Taylor & Francis |
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This
paper proposes a novel wavelet transform–based technique for short-time |
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load
forecasting of weather-sensitive loads. In this paper, Daubechies D2, D4, |
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and
D10 wavelet transforms are adopted to predict short-term loads, and the |
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numerical
results reveal that certain wavelet components can eOEectively be used |
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to
identify the load characteristics in electric power systems. The wavelet
co-e“ |
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cients
associated with certain frequency and time localization are adjusted |
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using
the conventional multiple regression method and then reconstructed in |
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order
to forecast the ”nal loads through a three-scale synthesis technique.
The |
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outcome
of the study clearly indicates that the proposed wavelet transform ap-proach |
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can be
used as an attractive and eOEective means for short-term load |
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Wavelet
transform and neural networks for short-term electrical load forecasting |
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S. J.
Yao, Y. H. Song, L. Z. Zhang and X. Y. Cheng |
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Energy
Conversion and Management, Volume 41, Issue 18, 1 December 2000, Pages |
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Demand
forecasting is key to the efficient management of electrical energy
systems. A |
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novel
approach is proposed in this paper for short term electrical load
forecasting by |
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combining
the wavelet transform and neural networks. The electrical load at any |
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particular
time is usually assumed to be a linear combination of different components. |
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From
the signal analysis point of view, load can also be considered as a linear
combination |
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of
different frequencies. Every component of load can be represented by one
or several |
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frequencies.
The process of the proposed approach first decomposes the historical load |
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into
an approximate part associated with low frequencies and several detail
parts |
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associated
with high frequencies through the wavelet transform. Then, a radial basis |
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function
neural network, trained by low frequencies and the corresponding
temperature |
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records
is used to predict the approximate part of the future load. Finally, the
short term |
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load
is forecasted by summing the predicted approximate part and the weighted
detail |
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parts.
The approach has been tested by the 1997 data of a practical system. The
results |
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show
the application of the wavelet transform in short term load forecasting is |
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An
adaptive neural-wavelet model for short term load forecasting |
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Bai-Ling
Zhang and Zhao-Yang Dong |
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Electric
Power Systems Research, Volume 59, Issue 2, 28 September 2001, Pages |
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This
paper proposed a novel model for short term load forecast in the
competitive |
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electricity
market. The prior electricity demand data are treated as time series. The |
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forecast
model is based on wavelet multi-resolution decomposition by
autocorrelation shell |
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representation
and neural networks (multilayer perceptrons, or MLPs) modeling of |
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wavelet
coefficients. To minimize the influence of noisy low level coefficients,
we applied |
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the
practical Bayesian method Automatic Relevance Determination (ARD) model to |
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choose
the size of MLPs, which are then trained to provide forecasts. The
individual |
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wavelet
domain forecasts are recombined to form the accurate overall forecast. The |
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proposed
method is tested using Queensland electricity demand data from the
Australian |
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National
Electricity Market.
|
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Evolving
wavelet-based networks for short-term load forecasting |
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Huang,
C.-M.; Yang, H.-T. |
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Generation,
Transmission and Distribution, IEE Proceedings- , Volume: 148 Issue: 3 ,
May |
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A new
short-term load forecasting (STLF) approach using evolving wavelet-based |
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networks
(EWNs) is proposed. The EWNs have a three-layer structure, which contains |
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the
wavelet (input-layer), weighting (intermediate-layer), and summing
(output-layer) |
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nodes,
respectively. The networks are evolved by tuning the parameters of
translation and |
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dilation
in the wavelet nodes and the weighting factors in the weighting nodes.
Taking the |
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advantages
of global search abilities of evolutionary computing as well as the |
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multi-resolution
and localisation natures of the wavelets, the EWNs thus constructed call |
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identify
the inherent nonlinear characteristics of the power system loads. The
proposed |
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approach
is verified through different types of data for the Taiwan power (Taipower) |
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system
and substation loads, as well as corresponding weather variables.
Comparisons of |
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forecasting
error and constructing time reveal that the performance of the EWNs could |
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be
superior to that of the existing artificial neural networks (ANNs). |
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