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conference paper
Improving recurrent network load forecasting
1995
1995 IEEE International Conference on Neural Networks Proceedings
We present a not fully connected recurrent network applied to the problem of load forecasting. Although many authors have pointed out that recurrent networks were able to model NARMAX processes, we present a constructing scheme for the MA part. In addition we present a modification of the learning step which improves learning convergence and the accuracy of the forecast. At last, the use of a continuous learning scheme and a robust learning scheme, which appeared to be necessary when using a MA part, enables us to reach a good precision of the forecast, compared to the accuracy of the model in use at the utility at present
Type
conference paper
Web of Science ID
WOS:A1995BF46H00173
Authors
Publication date
1995
Published in
1995 IEEE International Conference on Neural Networks Proceedings
Volume
2
Start page
899
End page
904
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
April 4, 2007
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