Neural short-term prediction based on dynamics reconstruction

被引：17
|
作者：
Camastra, F [1 ]
Colla, AM [1 ]
机构：
[1] Elsag Bailey Finmeccan SpA, Dept Res & Dev, I-16154 Genoa, Italy
来源：
NEURAL PROCESSING LETTERS | 1999年 / 9卷 / 01期
关键词：
dynamics reconstruction; electrical load forecasting; model order estimation; multi-layer perceptron; short-term prediction;
D O I：
10.1023/A:1018619928149
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：45 / 52
页数：8
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