Title :
Short-Term Power Load Forecasting Based on LS-SVM
Author :
Bin, Liu ; Guang, Xu
Author_Institution :
Univ. of Sci. & Technol., Shaanxi Univ. of Sci. & Technol., Xi´´an, China
Abstract :
In order to solve the Short-term Load Forecasting problems in Power Systems, this article puts forward the Least Squares Support Vector Machine´s improved model by selecting the appropriate Gauss kernel function and proposing the error calculation analytical method, thus reduces the computational complicate problems when large amount of data is input in Short-term Power Load Forecasting. An example is given to prove the validity of the algorithm.
Keywords :
least squares approximations; load forecasting; power engineering computing; support vector machines; Gauss kernel function; error calculation analytical method; least squares support vector machine; power systems; short-term power load forecasting; Classification algorithms; Load forecasting; Load modeling; Power quality; Predictive models; Support vector machines; LS-SVM; Power System; Short-term Load Forecasting;
Conference_Titel :
Information Science and Management Engineering (ISME), 2010 International Conference of
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-7669-5
Electronic_ISBN :
978-1-4244-7670-1
DOI :
10.1109/ISME.2010.86
