Short-term load forecasting using a chaotic time series

被引：0

作者：

Michanos, SP ^{[1
]}

Tsakoumis, AC ^{[1
]}

Fessas, P ^{[1
]}

Vladov, SS ^{[1
]}

Mladenov, VM ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, GR-54006 Thessaloniki, Greece

来源：

SCS 2003: INTERNATIONAL SYMPOSIUM ON SIGNALS, CIRCUITS AND SYSTEMS, VOLS 1 AND 2, PROCEEDINGS | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new approach to short-term load forecasting (STLF) in power systems is described in this paper. The method uses a chaotic time series and artificial neural network. The paper describes chaos time series analysis of daily power system peak loads. Nonlinear mapping of deterministic chaos is identified by multiplayer perceptron (MLP). Using embedding dimension and delay time, an attractor in pseudo phase plane and an ANN model trained by this attractor are constructed. The proposed approach is demonstrated by an example.

引用

页码：437 / 440

页数：4

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