Improving recurrent network load forecasting

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


Published in:
1995 IEEE International Conference on Neural Networks Proceedings (Cat. No.95CH35828), vol.2, 899 - 904
Year:
1995
Keywords:
Note:
recurrent network load forecasting;NARMAX processes;learning convergence;
Laboratories:




 Record created 2007-04-04, last modified 2018-03-18


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