DocumentCode
420836
Title
A SVM approach to ship power load forecasting based on RBF kernel
Author
Zhu, Sifeng ; Wang, Xihuai
Author_Institution
Dept. of Electr. & Autom., Shanghai Maritime Univ., China
Volume
2
fYear
2004
fDate
15-19 June 2004
Firstpage
1824
Abstract
A support vector machine (SVM) is a new generation machine learning technique based on the statistical learning theory. A SVM algorithm based on the radial basis function (RBF) kernel and its application to predict the ship power load were presented. The simulation results show that support vector machines have outstanding advantages in high forecasting accuracy, global optimal property and small time complexity. The results of solving the practical problem about ship power load forecasting are fine.
Keywords
learning (artificial intelligence); load forecasting; power engineering computing; radial basis function networks; ships; support vector machines; RBF kernel; SVM approach; machine learning technique; radial basis function; ship power load forecasting; statistical learning theory; support vector machine; Automation; Electronic mail; Kernel; Load forecasting; Machine learning; Machine learning algorithms; Marine vehicles; Power generation; Statistical learning; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN
0-7803-8273-0
Type
conf
DOI
10.1109/WCICA.2004.1340990
Filename
1340990
Link To Document