石油化工高等学校学报Issue(4)：75-80,6.DOI:10.3969/j.issn.1006-396X.2015.04.016
基于 Haar 小波变换和 ARIMA-RBF 的天然气时负荷预测
Hourly Load Prediction for Natural Gas Based on Haar Wavelet Tansforming and ARIMA-RBF
摘要
Abstract
A resultant forecast model for prediction of hourly load of natural gas is proposed based on Haar wavelet transforming and ARIMA-RBF in this paper.Firstly,adopting Mallat fast algorithm and choosing Haar wavelet as mother wavelet,the gas hour load is decomposed,then the high frequency signals are predicted with ARIMA,and the low frequency is predicted with RBF.Secondly,the high frequency and the low frequency are reconstructed by Haar wavelet.Finally,taking gas hour load of a city for example,the effectiveness of prediction model is verified and compared with SOFM+MLP.The results indicate that the MAPE of the combination forecasting model is higher than 2.593 2%,the prediction accuracy is significantly improved in this paper,which provide a new useful reference for the short-term forecasting in online engineering application.关键词
天然气时负荷/Haar 小波变换/ARIMA/RBF/预测Key words
Gas hour load/Haar wavelet transform/ARIMA/RBF/Forecast分类
能源科技引用本文复制引用
乔伟彪,陈保东..基于 Haar 小波变换和 ARIMA-RBF 的天然气时负荷预测[J].石油化工高等学校学报,2015,(4):75-80,6.基金项目
中国石油集团公司重点研究项目资助(KY2011-13)。 （KY2011-13）
