Title :
Application of SVM and wavelet neural network method for short-term power load forecasting
Author :
Zhang, Qian ; Liu, Tongna
Author_Institution :
Dept. of Economic Manage., North China Electr. Power Univ., Baoding, China
Abstract :
This paper put forward a new method of the SVM and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast short-term electric load.
Keywords :
fuzzy set theory; load forecasting; power engineering computing; radial basis function networks; support vector machines; fuzzy rules; short-term electric load forecasting; short-term power load forecasting; support vector machine; wavelet neural network method; Artificial neural networks; Economic forecasting; Energy management; Estimation error; Load forecasting; Neural networks; Power generation economics; Predictive models; Risk management; Support vector machines; SVM; electric Load Forecasting; fuzzy rules;
Conference_Titel :
Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5585-0
Electronic_ISBN :
978-1-4244-5586-7
DOI :
10.1109/ICCAE.2010.5451579
