In this paper we present an application of dynamics reconstruction techniques to model order estimation. Both the Grassberger-Procaccia and the Takens' method were applied, yielding similar values for the correlation dimension, hence for the model order. Based on this model order, appropriately structured neural nets for short-term prediction were designed. Satisfactory experimental results were obtained in one-hour-ahead electrical load forecasting on a six-month benchmark from an electric utility in the U.S.A.

Neural Short-term Prediction based on Dynamics reconstruction

CAMASTRA, Francesco;
1999

Abstract

In this paper we present an application of dynamics reconstruction techniques to model order estimation. Both the Grassberger-Procaccia and the Takens' method were applied, yielding similar values for the correlation dimension, hence for the model order. Based on this model order, appropriately structured neural nets for short-term prediction were designed. Satisfactory experimental results were obtained in one-hour-ahead electrical load forecasting on a six-month benchmark from an electric utility in the U.S.A.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/15020
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