A hybrid method of clipping and artificial neural network for electricity price zone forecasting

被引：0

作者：

Mori, H. ^{[1
]}

Awata, A. ^{[1
]}

机构：

[1] Meiji Univ, Dept Elect & Elect Engn, Tama Ku, 1-1-1 Higashimita, Kawasaki, Kanagawa 2148571, Japan

来源：

2006 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, VOLS 1 AND 2 | 2006年

关键词：

forecasting; prediction method; data mining; time series; neural network applications; MLP; intelligent systems;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a new method for electricity price zone forecasting. The proposed method makes use of the clipping technique that is one of data mining techniques for simplifying the relationship between input and output variables. It expresses an output variable in binary number. Electricity price forecasting is difficult to handle due to the nonlinearity of time series. This paper predicts the one-step-ahead price zone. In this paper, the normalized radial basis function network is used as an artificial neural network (ANN) to evaluate the predicted price. The proposed method is tested for the electricity price in the New England power market.

引用

页码：275 / 280

页数：6

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