International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 9, No. 4, 8 Apr. 2017
Cover page and Table of Contents: PDF (size: 538KB)
A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS
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Author(s)
Mi Zhang
1,*
Changhao Xia
1
Index Terms
Power system, short-term load forecasting, wavelet transform, wavelet function, wavelet neural network, SPSS, Wilcoxon signed rank test
Abstract
A power system load forecasting method using wavelet neural network with a process of decomposition-forecasting-reconstruction and error analysis based on SPSS is presented in this paper. First of all, the load sequence is decomposed by wavelet transform into each scale wavelet coefficients of navigation. In this step, choosing an appropriate wavelet function decomposition of load is needed. In this paper, by comparing the signal-to-noise ratio (SNR) and the mean square error (MSE) of the different wavelet functions for load after processing; It is concluded that the most suitable wavelet function for the load sequence in this paper is db4 wavelet function. The scale of wavelet coefficients is obtained by load wavelet decomposition. In the process of wavelet coefficient of processing, the db4 wavelet function is used to decompose the original sequence in 3 scales; High frequency and low frequency wavelet coefficient is got through setting threshold. Secondly, these wavelet coefficients are used as the training sample of the input to the nonlinear regression neural network for processing, and then the forecasting result is obtained by the wavelet reconstruction. Finally, the actual and forecasting values are compared by SPSS with a comprehensive statistical charting capability, which is able to draw beautiful charts and is easy to edit.
Cite This Paper
Mi Zhang, Changhao Xia, "A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.24-30, 2017. DOI:10.5815/ijitcs.2017.04.04
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