DocumentCode :
3446559
Title :
Comparison of different BP neural network models for short-term load forecasting
Author :
Ning, Yuan ; Liu, Yufeng ; Zhang, Huiying ; Ji, Qiang
Author_Institution :
Coll. of Electr. Eng., Guizhou Univ., Guiyang, China
Volume :
3
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
435
Lastpage :
438
Abstract :
Short-term load forecasting(STLF) is of great importance for the safety and stabilization of grids. Based on the historical load data of meritorious power of some area in Guizhou power system, three BP neural networks in steepest descent algorithm back propogation neural network(SDBP), Levenberg -Marquardt algorithm back propogation neural network (LMBP) and Bayesian regularization algorithm back propogation neural network (BRBP) models in 24 hours ahead prediction are compared. Since the traditional BP algorithm has some drawbacks such as slow training convergence speed and possibility of local minimizing the optimized function, an optimized L-M algorithm, which can improve the stability of convergence and accelerate the training speed of neural network has been applied to carry out load forecasting work to reduce the mean relative error. Bayesian regularization also be applied which can overcome and improve the generalization of neural network. The prediction precision of BRBP are superior to LMBP and SDBP, while BRBP has poor training speed than others.
Keywords :
backpropagation; belief networks; load forecasting; neural nets; power systems; BRBP; Bayesian regularization; Bayesian regularization algorithm back propogation neural network; Guizhou power system; LMBP; Levenberg -Marquardt algorithm back propogation neural network; STLF; different BP neural network models; historical load data; short term load forecasting; Classification algorithms; Forecasting; Load modeling; Prediction algorithms; Rain; Bayesian regularization; Levenberg-Marquardt; Short-term load forecasting(STLF); steepest descent algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
Type :
conf
DOI :
10.1109/ICICISYS.2010.5658645
Filename :
5658645
Link To Document :
بازگشت