DocumentCode :
2210414
Title :
Researching about Short-Term Power Load Forecasting Based on Improved BP ANN Algorithm
Author :
Xu Yabin ; Du Peng
Author_Institution :
Inst. of Comput., Beijing Inf. Sci. & Technol. Univ., Beijing, China
fYear :
2009
fDate :
26-28 Dec. 2009
Firstpage :
4094
Lastpage :
4097
Abstract :
First, the forecasting principle and improved algorithms about BP ANN are briefly introduced. Then, an improved algorithm about BP ANN is put forward which based on subordinating degree function, and conduct simulating tests. The result indicates that convergence is rapid without changing the forecasting precision. Based on this and combined with the characteristic of power load forecasting, a model for BP ANN is built, and the corresponding software is designed. A good application effect is achieved to forecast the short-term power load with this software.
Keywords :
backpropagation; load forecasting; neural nets; power engineering computing; BP ANN algorithm; artificial neural network; backpropagation; short term power load forecasting; Artificial neural networks; Backpropagation algorithms; Convergence; Economic forecasting; Information science; Load forecasting; Power engineering and energy; Power grids; Power system planning; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4909-5
Type :
conf
DOI :
10.1109/ICISE.2009.1026
Filename :
5454632
Link To Document :
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