@article{(Open Science Index):https://publications.waset.org/pdf/139,
	  title     = {Implementation of Neural Network Based Electricity Load Forecasting},
	  author    = {Myint Myint Yi and  Khin Sandar Linn and  Marlar Kyaw},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {2},
	  number    = {6},
	  year      = {2008},
	  pages     = {1122 - 1127},
	  ee        = {https://publications.waset.org/pdf/139},
	  url   	= {https://publications.waset.org/vol/18},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 18, 2008},
	}