Title :
A SVM and variable structure neural network method for short-term 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 variable structure artificial 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 :
load forecasting; neural nets; power systems; support vector machines; SVM; neural call function; nonlinear wavelets; short-term electric load forecasting; variable structure 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; variable structure neural network;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5487080
