Hybrid of ARIMA and SVMs for Short-Term Load Forecasting

被引：162

作者：

Nie, Hongzhan ^{[1
]}

Liu, Guohui ^{[1
]}

Liu, Xiaoman ^{[1
]}

Wang, Yong ^{[2
]}

机构：

[1] NE Dianli Univ, Sch Elect Engn, Jilin, Jilin, Peoples R China

[2] Northeast China Grid Co Ltd, Changchun Extrahigh Voltage Bur, Changchun, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON FUTURE ENERGY, ENVIRONMENT, AND MATERIALS, PT C | 2012年 / 16卷

关键词：

short-term load forecasting; ARIMA model; SVMs model; hybrid ARIMA-SVMs model;

D O I：

10.1016/j.egypro.2012.01.229

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 is a variable affected by many factors. It is difficult to forecast accurately with a single model. Taking advantage of the autoregressive integrated moving average (ARIMA) to forecast the linear basic part of load and of the support vector machines (SVMs) to forecast the non-linear sensitive part of load, a method based on hybrid model of ARIMA and SVMs is presented in this paper. It firstly uses ARIMA to forecast the daily load, and then uses SVMs, which is known for the great power to learn and generalize, to correct the deviation of former forecasting. Applying this hybrid model to a large sample prediction, the results show that it achieves the forecasting accuracy and has very good prospective in applications. So it can be used as a new load forecasting method. (C) 2011 Published by Elsevier B. V. Selection and/or peer-review under responsibility of International Materials Science Society.

引用

页码：1455 / 1460

页数：6

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