Improving recurrent network load forecasting
- 19 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 899-904
- https://doi.org/10.1109/icnn.1995.487538
Abstract
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.Keywords
This publication has 12 references indexed in Scilit:
- Progress in forecasting by neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Recurrent neural networks and time series predictionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Heterogeneous artificial neural network for short term electrical load forecastingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Improving recurrent network load forecastingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A robust backpropagation learning algorithm for function approximationIEEE Transactions on Neural Networks, 1994
- Recurrent neural networks and robust time series predictionIEEE Transactions on Neural Networks, 1994
- Special Feature. Predicting time series by a fully connected neural network trained by back propagationComputing & Control Engineering Journal, 1992
- Properties of neural networks with applications to modelling non-linear dynamical systemsInternational Journal of Control, 1992
- Finding structure in timeCognitive Science, 1990
- Finite State Automata and Simple Recurrent NetworksNeural Computation, 1989