Application of nuSupport Vector Regression in Short-Term Load Forecasting

被引：0

作者：

Omidi, Adnan ^{[1
]}

Barakati, S. Masoud ^{[1
]}

Tavakoli, Saeed ^{[1
]}

机构：

[1] Univ Sistan & Baluchestan, Fac Elect & Comp, Zahadan, Iran

来源：

2015 20TH CONFERENCE ON ELECTRICAL POWER DISTRIBUTION NETWORKS CONFERENCE (EPDC) | 2015年

关键词：

Short-term load forecasting; support vector regression; multilayer perceptron (MLP) neural networks; MODEL;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Short-term load forecasting (STLF) of electric power systems plays an essential role in the optimal operation of power systems. Economic performance and reliability of a power system is substantially dependent on the load prediction. STLF is a complex process in electric grid due to having many non-linear factors, such as daily and weekly cyclical changes. Support vector regression has a good ability to estimate non-linear equations. In this paper, a new support vector machine model called nu support vector regression (nu-SVR) is proposed for electrical load forecasting. Results of the proposed method are compared with forecasting results achieved using an artificial neural network (ANN). Results show that the nu-SVR is a proper method for STLF.

引用

页码：32 / 36

页数：5

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