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
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;
Conference_Titel :
System Theory, 1995., Proceedings of the Twenty-Seventh Southeastern Symposium on
Conference_Location :
Starkville, MS
Print_ISBN :
0-8186-6985-3
DOI :
10.1109/SSST.1995.390613
