基于GARCH的短时风速预测方法

姜言; 黄国庆; 彭新艳; 李永乐

doi:10.3969/j.issn.0258-2724.2016.04.009

基于GARCH的短时风速预测方法

doi: 10.3969/j.issn.0258-2724.2016.04.009

基金项目:

青年千人计划项目高铁联合基金资助项目(U1334201)

四川省应用基础研究计划资助项目(2015JY0060)

详细信息

作者简介:
姜言(1989-),男,博士研究生,研究方向为桥梁抗风,E-mail:xnjtjiangyan@163.com

通讯作者:
黄国庆(1976-),男,教授,博士生导师,研究方向为风工程,E-mail:ghuang1001@gmail.com

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出版历程
- 收稿日期: 2015-06-24
- 刊出日期: 2016-08-25

Method of Short-Term Wind Speed Forecasting Based on Generalized Autoregressive Conditional Heteroscedasticity Model

摘要

摘要: 为提高高速列车运行的安全性,基于线性递归的差分自回归移动平均模型(auto-regressive integrated moving average, ARIMA)和非线性递归的广义自回归条件异方差模型(generalized auto-regressive conditionally heteroscedastic, GARCH),提出一种组合模型ARIMA-GARCH进行高速铁路强风风速的短时预测.首先对数据的非平稳性进行预处理,以降低数据非平稳性对所提模型的影响;其次建立线性递归的ARIMA模型对数据进行分析和预测;最后,引入非线性递归的GARCH模型对数据进行分析和预测.基于现场测量的样本仿真分析表明:相比原始数据,ARIMA-GARCH模型的预测精度较高且随着预测步长的增加,平均绝对误差仅从0.836 m/s增加到1.272 m/s;ARIMA-GARCH模型考虑了异方差这一非线性特性,其预测精度明显好于线性的ARIMA模型,其中超前6步预测的平均绝对误差精度提高11.54%.
- 高速列车 /
- 风速预测 /
- ARIMA模型 /
- 非线性 /
- GARCH模型
Abstract: In order to improve the security of the train operation, a short-term wind speed forecasting method was proposed based on a linear recursive auto-regressive integrated moving average (ARIMA) algorithm and a non-linear recursive generalized auto-regressive conditionally heteroscedastic (GARCH) algorithm (ARIMA-GARCH). Firstly, the non-stationarity embedded in the original wind speed data was pre-processed to eliminate its effect on the proposed model. Then, a linear recursive ARIMA model was build to predict wind speed. Finally, a non-linear recursive forecasting model was established based on the GARCH algorithm. Numerical example based on wind samples from field measurements The result shows that compared with the source data, the proposed approach has a higher prediction accuracy. With the increasing of prediction step, the mean absolute error only increases from 0.836 m/s to 1.272 m/s. The new method explains parts of the non-linear characteristics (heteroscedasticity) of wind speed time series and improves the prediction accuracy compared with the usual ARIMA approach. For instance, compared with the ARIMA model, the accuracy of mean absolute error of the six-step in advance forecast based on the GARCH model is improved by 11.54%.
- high-speed trains /
- wind speed forecasting /
- ARIMA /
- non-linear /
- GARCH

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