Modelling power output at nuclear power plant by neural networks

Authors:
Jaakko Talonen;Miki Sirola;Eimontas Augilius
Affiliations:
Aalto University, School of Science and Technology, Aalto, Finland;Aalto University, School of Science and Technology, Aalto, Finland;Aalto University, School of Science and Technology, Aalto, Finland
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

In this paper, we propose two different neural network (NN) approaches for industrial process signal forecasting. Real data is available for this research from boiling water reactor type nuclear power reactors. NNs are widely used for time series prediction, but it isn't utilized for Olkiluoto nuclear power plant (NPP), Finland. Preprocessing, suitable input signals and delay analysis are important phases in modelling. Optimized number of delayed input signals and neurons in hidden-layer are found to make possible prediction of idle power process signal. It is mainly concentrated on algorithms on input signal selection and finding the optimal model for one-step ahead prediction.