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

针对天然气时负荷预测问题,提出了一种基于Haar小波变换和ARIMA-RBF的天然气时负荷组合预测模型。首先,对天然气时负荷数据样本时间序列进行小波分解,采用Mallat快速算法,母小波为Haar小波,对分解出来的高频分量进行ARIMA预测,低频分量进行RBF预测;其次,对高频分量预测结果和低频分量预测结果进行Haar小波重构;最后,以某市实际采集的天然气时负荷为例进行研究,并与自组织特征映射(Self-organizing Feature Map,SOFM)网络和多层感知器(Multilayer Perceptron,MLP)网络(SOFM+MLP)组合预测模型进行对比分析。结果表明,组合预测模型较SOFM+MLP预测模型的MAPE值指标高出2.593 2%,预测精度显著提高,为实际工程的在线应用提供了有益参考。

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.