Neural Short-Term Prediction Based on Dynamics Reconstruction

摘要

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.