DocumentCode :
2224764
Title :
Adaptive power signal prediction by non-fixed neural network model with modified fuzzy back-propagation learning algorithm
Author :
Hwang, Rey-Chue ; Huang, Huang-Chu ; Chen, Yu-Ju ; Jer-Guang Hsich ; Hsing Chao
Author_Institution :
Dept. of Electr. Eng., Kaohsiung Polytech. Inst., Taiwan
fYear :
1997
fDate :
9-12 Sep 1997
Firstpage :
689
Abstract :
The authors present a nonfixed artificial neural network (ANN) model with a modified fuzzy back-propagation (BP) learning rule for power signal prediction. This model is designed to avoid the ill-learning of ANN training caused by improper information. Taipower 1990-1993 loads and relevant weather data are implemented. The experiments of next day peak load forecasting and one-day-ahead hourly load forecasting are made in this study. Some experiments using conventional BP ANN approach are also performed as a comparison with the proposed model
Keywords :
backpropagation; fuzzy neural nets; load forecasting; power system analysis computing; Taipower; adaptive power signal prediction; modified fuzzy backpropagation learning rule; next day peak load forecasting; nonfixed artificial neural network; one-day-ahead hourly load forecasting; Artificial neural networks; Fuzzy logic; Fuzzy neural networks; Load forecasting; Neural networks; Power system modeling; Power system planning; Predictive models; Temperature; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
Type :
conf
DOI :
10.1109/ICICS.1997.652065
Filename :
652065
Link To Document :
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