DocumentCode
3147423
Title
Recurrent neural networks and load forecasting
Author
Connor, Jerome T. ; Atlas, Les E. ; Martin, Doug
Author_Institution
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
fYear
1991
fDate
23-26 Jul 1991
Firstpage
22
Lastpage
25
Abstract
The ability of a recurrent network to model load forecasting is investigated. Its performance in a competition is then contrasted with that of feedforward networks and linear models. Its weaknesses and strengths are then analyzed to give guidelines to the design of neural net predictors with the hope of designing better predictors in the future
Keywords
feedforward neural nets; load forecasting; power engineering computing; feedforward networks; linear models; load forecasting; neural net predictors; recurrent neural networks; Interactive systems; Load forecasting; Neural networks; Neurofeedback; Neurons; Predictive models; Recurrent neural networks; State-space methods; Statistics; Technological innovation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0065-3
Type
conf
DOI
10.1109/ANN.1991.213491
Filename
213491
Link To Document