Title :
Application of Input Variables Selecting Method for Support Vector Machine Model
Author :
Yang, Kuihe ; Shan, Ganlin ; Zhao, Lingling
Abstract :
It is very important to select input variables when the support vector machine forecasting model is proposed. The input variables selection for short-term load forecasting is relevant to the performance of support vector machine forecasting. By using the correlation coefficient idea on input variables selection for support vector machine short-term load forecasting, a systemic and operable method for input variables sets selection is first proposed. An example of short-term load forecasting is given. The result shows that a more preferable input variables set can be obtained, and the forecasting errors are smaller, which validates that the method is effective
Keywords :
load forecasting; power engineering computing; support vector machines; input variables selection; short-term load forecasting; support vector machine forecasting model; Educational institutions; Input variables; Intelligent control; Load forecasting; Load modeling; Mathematical model; Predictive models; Scattering; Support vector machines; Weather forecasting; Support vector machine; input variables selection; short-term load forecasting;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712674