Implementation of Neural Network Based Electricity Load Forecasting
This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.
[1] H.S. Hippert et al, "Neural Networks for Short-Term Load
Forecasting: A Review and Evaluation," IEEE Trans.on Power
Systems, Vol.16, No.1, Feb. 2001.
[2] W.R. Foster et al, "Neural Network forecasting of short, noisy time
series," Computers Chem. Engng., Vol. 16, No.4, 1992.
[3] S. Haykin, "Neural Networks," Macmillan College Publishing
Company, Inc.1994
[4] N.K. Bose and P.Laing, "Neural Network Fundamentals with Graphs,
Algorithms, and Applications," McGraw- Hill Electrical and Computer
Engineering Series, McGraw-Hill, Inc.1996.
[5] K.L.Toe, "Neural Network in Electricity Load Forecasting with Wavelet
Decomposition and Temperature Dependency," Thesis of Electrical
Engineering, University of Queensland, August.2001.
[6] "Wavelet Toolbox," MATLAB Software.
[7] H. Maier, ÔÇÿA Review of Artificial Neural Networks," Department of
Civil and Enviromental Engineering, University of Adelaide, Research
Report No.R[3], August,1995.
[8] G. Chicoo et al, "Laod pattern clustering for short-term load forecasting
of anomalous days," IEEE Porto PowerTech 2001 Conferences,
September,2001.
[9] B.L.Zhang and Z.Y.Dong, An Adaptive Neural-Wavelet Model for
Short Term Load Forecasting," Electrical Power Systems Research
59(2001).
[10] B.N. Tran et al, "Wavelets," J.Webster(ed) Wiley Encyclopedia of
Electrical and Electronics Engineering, John Wiley & Sons, Inc, 1999.
[11] A. Louis et al, "Wavelets Theorey and Applications," John Wiley &
Sons Ltd, England,1997.
[1] H.S. Hippert et al, "Neural Networks for Short-Term Load
Forecasting: A Review and Evaluation," IEEE Trans.on Power
Systems, Vol.16, No.1, Feb. 2001.
[2] W.R. Foster et al, "Neural Network forecasting of short, noisy time
series," Computers Chem. Engng., Vol. 16, No.4, 1992.
[3] S. Haykin, "Neural Networks," Macmillan College Publishing
Company, Inc.1994
[4] N.K. Bose and P.Laing, "Neural Network Fundamentals with Graphs,
Algorithms, and Applications," McGraw- Hill Electrical and Computer
Engineering Series, McGraw-Hill, Inc.1996.
[5] K.L.Toe, "Neural Network in Electricity Load Forecasting with Wavelet
Decomposition and Temperature Dependency," Thesis of Electrical
Engineering, University of Queensland, August.2001.
[6] "Wavelet Toolbox," MATLAB Software.
[7] H. Maier, ÔÇÿA Review of Artificial Neural Networks," Department of
Civil and Enviromental Engineering, University of Adelaide, Research
Report No.R[3], August,1995.
[8] G. Chicoo et al, "Laod pattern clustering for short-term load forecasting
of anomalous days," IEEE Porto PowerTech 2001 Conferences,
September,2001.
[9] B.L.Zhang and Z.Y.Dong, An Adaptive Neural-Wavelet Model for
Short Term Load Forecasting," Electrical Power Systems Research
59(2001).
[10] B.N. Tran et al, "Wavelets," J.Webster(ed) Wiley Encyclopedia of
Electrical and Electronics Engineering, John Wiley & Sons, Inc, 1999.
[11] A. Louis et al, "Wavelets Theorey and Applications," John Wiley &
Sons Ltd, England,1997.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49284", author = "Myint Myint Yi and Khin Sandar Linn and Marlar Kyaw", title = "Implementation of Neural Network Based Electricity Load Forecasting", abstract = "This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.", keywords = "Neural network, Load forecast, Time series, wavelettransform.", volume = "2", number = "6", pages = "1036-6", }