Title :
Improved RBF network applied to short-term load forecasting
Author :
Dongxiao, Niu ; Ling, Ji ; Jie, Tian
Author_Institution :
Dept. of Bus. & Adm. Manage., North China Electr. Power Univ., Beijing, China
Abstract :
From the practical application of short-term load forecasting, this article introduced the radial basis function network and use nearest neighbor clustering algorithm to determine the width of radial basis function, select the cluster centers and weights. The predicted results show that the method is faster and has higher precision.
Keywords :
load forecasting; pattern clustering; power engineering computing; radial basis function networks; RBF network; nearest neighbor clustering algorithm; radial basis function network; short-term load forecasting; Clustering algorithms; Heuristic algorithms; Load forecasting; Prediction algorithms; Predictive models; Radial basis function networks; Training; network; short-term load forecasting; the radial basis function;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-9699-0
DOI :
10.1109/ICSESS.2011.5982477
