DocumentCode
2593228
Title
A review of ANN-based short-term load forecasting models
Author
Rui, Y. ; El-Keib, A.A.
Author_Institution
Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA
fYear
1995
fDate
12-14 Mar 1995
Firstpage
78
Lastpage
82
Abstract
Artificial neural networks (ANN) have recently received considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appeared in the literature. An extensive survey of ANN-based load forecasting models is given. The six most important factors which affect the accuracy and efficiency of the load forecasters are presented and discussed. The paper also includes conclusions reached by the authors as a result of their research in this area
Keywords
backpropagation; load forecasting; neural nets; power engineering computing; accuracy; artificial neural network-based short-term load forecasting models; efficiency; Artificial intelligence; Artificial neural networks; Load forecasting; Load modeling; Neural networks; Power system modeling; Power system reliability; Power system security; Predictive models; Transfer functions;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1995., Proceedings of the Twenty-Seventh Southeastern Symposium on
Conference_Location
Starkville, MS
ISSN
0094-2898
Print_ISBN
0-8186-6985-3
Type
conf
DOI
10.1109/SSST.1995.390613
Filename
390613
Link To Document