Title :
Application of Support Vector Machine and Fuzzy Rules Method for Power Load Forecasting
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 fuzzy rules 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; support vector machines; wavelet transforms; SVM; fuzzy rules method; neural call function; nonlinear wavelets; power load forecasting model; short-term electric load forecasting; support vector machine; Application software; Artificial neural networks; Computer applications; Estimation error; Load forecasting; Power engineering and energy; Power engineering computing; Predictive models; Risk management; Support vector machines; SVM; electric load forecasting; fuzzy rules;
Conference_Titel :
Computer Engineering and Applications (ICCEA), 2010 Second International Conference on
Conference_Location :
Bali Island
Print_ISBN :
978-1-4244-6079-3
Electronic_ISBN :
978-1-4244-6080-9
DOI :
10.1109/ICCEA.2010.110
