DocumentCode :
3162976
Title :
Survey of hybrid fuzzy neural approaches to electric load forecasting
Author :
Srinivasan, Dipti ; Lee, Michael A.
Author_Institution :
Berkeley Initiative in Soft Comput., California Univ., Berkeley, CA, USA
Volume :
5
fYear :
1995
fDate :
22-25 Oct 1995
Firstpage :
4004
Abstract :
Economically efficient operation of electric power systems necessitates close tracking of the overall load by the system generation at all times. A wide range of methodologies and models have been developed over the years to predict the future load with reasonable accuracy and reliability. Several research groups have studied the use of artificial neural networks for this application and reported superior results compared to the conventional approaches. Application of fuzzy systems has also been proposed to include imprecise and probabilistic information in the input data. Synthesis of these two complementary technologies has emerged as a highly promising approach for electric load forecasting. This paper aims to provide an overview of the published literature on this topic, highlighting common features and drawing out some important aspects of the methodology used
Keywords :
fuzzy neural nets; load forecasting; power engineering computing; reviews; artificial neural networks; electric load forecasting; electric power systems; fuzzy systems; hybrid fuzzy neural approaches; imprecise information; probabilistic information; Artificial neural networks; Economic forecasting; Fuzzy systems; Network synthesis; Power generation; Power generation economics; Power system economics; Power system modeling; Power system reliability; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
Type :
conf
DOI :
10.1109/ICSMC.1995.538416
Filename :
538416
Link To Document :
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