DocumentCode :
3275219
Title :
Design of short term load forecasting model based on BP neural network and Fuzzy rule
Author :
Yanfei, Zeng ; Yinbo, Wu
Author_Institution :
Automatization Dept., Guangdong Polytech. Normal Univ., Guangzhou, China
fYear :
2011
fDate :
15-17 April 2011
Firstpage :
5828
Lastpage :
5830
Abstract :
By way of analyzing the more common advantages and disadvantages of short-term load forecasting, the short-term load forecasting model based on BP neural network and Fuzzy rule has been proposed. In the model, the load forecasting has been divided into two parts: the basic load component and the temperature and holiday load component. The former completed by the BP neural network, the latter completed by the fuzzy logic. Since introduction the smooth coefficient, forgetting factor, uneven membership into the model, the learning speed of BP neural network has been improved and the sensitivity of the load to temperature has been enhanced.
Keywords :
fuzzy logic; fuzzy set theory; load forecasting; neural nets; BP neural network; forgetting factor; fuzzy logic; fuzzy rule; short term load forecasting model; smooth coefficient; Analytical models; Artificial neural networks; Electronic mail; Load forecasting; Load modeling; Predictive models; BP neural network; Fuzzy rules; predictive control; short-term load;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
Type :
conf
DOI :
10.1109/ICEICE.2011.5777359
Filename :
5777359
Link To Document :
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