Application of GMDH to Short-term Load Forecasting

被引：0

作者：

Xu, Hongya ^{[1
]}

Dong, Yao ^{[2
]}

Wu, Jie ^{[2
]}

Zhao, Weigang ^{[2
]}

机构：

[1] Lanzhou Univ, Sch Phys Sci & Technol, Lanzhou, Peoples R China

[2] Lanzhou Univ, Sch Math & Stat, Lanzhou, Peoples R China

来源：

2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL III | 2010年

关键词：

Group Method of Data Handling (GMDH); short-term load forecasting (STLF); ARIMA; NEURAL-NETWORKS; MODEL;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA, we demonstrate that GMDH is a better method for STLF.

引用

页码：338 / 341

页数：4

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