DocumentCode :
3353651
Title :
Forecasting Short-Term Load of Southwestern Power Market in China by Chaotic BP Network
Author :
Yin, Kuang ; Gang, Luo
Author_Institution :
Key Lab. of Network Applic. Project, Neijiang Normal Univ., Neijiang
fYear :
2009
fDate :
27-31 March 2009
Firstpage :
1
Lastpage :
3
Abstract :
Power is important to modern society and national economy. To forecast short-term load more accurately, phase space of the complex nonlinear system was reestablished according to chaos theory and properties of short-term load were analyzed. It proves that forecasting short-term load is a classic decision-making process, full of chaos. Combining with chaos theory and traditional BP network, an improved BP network (chaotic BP network, CBP network) was presented in the chaotic phase space. Learning algorithm of traditional BP network was improved because of initial value sensitivity and good ergodicity of chaos operator. The forecasting system has been applied in the power market in southwestern China. The results show that the forecasting system based on CBP network is more accurate than traditional BP network and reliability and accuracy can be used as needed.
Keywords :
backpropagation; learning (artificial intelligence); load forecasting; neural nets; power engineering computing; power markets; chaotic BP network; chaotic phase space; complex nonlinear system; decision-making process; learning algorithm; power market; short-term load forecasting; southwestern China; Artificial neural networks; Chaos; Delay effects; Demand forecasting; Economic forecasting; Laboratories; Load forecasting; Power markets; Predictive models; Timing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-2486-3
Electronic_ISBN :
978-1-4244-2487-0
Type :
conf
DOI :
10.1109/APPEEC.2009.4918388
Filename :
4918388
Link To Document :
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