DocumentCode :
2049009
Title :
Modified neural and neuro-fuzzy approach for short term load forecasting
Author :
Chaturvedi, D.K. ; Premdayal, S.A.
Author_Institution :
Dayalbagh Educ. Inst., Agra, India
fYear :
2012
fDate :
17-19 Dec. 2012
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, a modified neural and neuro-fuzzy models have been developed for short-term load forecast using 33/11kV substation data. The substation load is recorded hourly and then neural networks / neuro-fuzy models have been tunned with preprocessed data. In the test case for implementation in short term load forecasting for 33/11 kv Substation of Dayalbagh Educational Institute, Agra was explored. The results have been compared with actual load and forecasted data using different approaches.
Keywords :
fuzzy neural nets; fuzzy set theory; load forecasting; power engineering computing; substations; Agra; Dayalbagh Educational Institute; modified neural approach; neuro-fuzy model; neuro-fuzzy approach; short term load forecasting; substation data; substation load; ANN; Load Forecasting; Neuro-fuzzy; Soft Computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power, Control and Embedded Systems (ICPCES), 2012 2nd International Conference on
Conference_Location :
Allahabad
Print_ISBN :
978-1-4673-1047-5
Type :
conf
DOI :
10.1109/ICPCES.2012.6508136
Filename :
6508136
Link To Document :
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