基于改进灰色GM(1,1)模型的天然气负荷预测  

Natural Gas Load Forecasting Based on Improved Grey GM(1,1) Mode

作　　者：孙相博 王岳[1] Sun Xiangbo;Wang Yue(College of Petroleum Engineering,Liaoning Shihua University,Fushun Liaoning 113001,China)

机构地区：[1]辽宁石油化工大学石油天然气工程学院

出　　处：《辽宁石油化工大学学报》2019年第3期52-57,共6页Journal of Liaoning Petrochemical University

基　　金：辽宁省科技厅计划项目(20131088)

摘　　要：构建三种改进的灰色预测模型,提高传统预测方法的精度。以北京市2007—2015年天然气负荷量作为原始数据建立模型,并用2016年数据进行结果检验。依次对三种改进灰色GM(1,1)模型分析和比较,选出最佳的改进模型与新陈代谢模型结合,替换旧数据、填补新数据,依次建模,构建北京市2017—2020年天然气负荷量预测模型。结果表明,组合后的模型可降低原始数据对预测系统的干扰,预测精度符合实际要求,且精度远高于传统的灰色模型,能真实反映未来天然气年负荷量的发展趋势,预测结果具有可靠性和实用性。同时,也为燃气市场的规划和调控提供参考。Three improved grey forecasting models are constructed to improve the accuracy of traditional forecasting methods. This paper took natural gas load of Beijing from 2007 to 2015 as raw data for modeling, and tested the predicting results of 2016. The traditional Grey Model and the three kind s of improved Grey Model were established to predict respectively,and the optimal model with best precision was chosen to combine with metabolism model which abandons the old data, constantly supplies new data, and repeats modeling. The optimal improved Grey Model was established to predict the annual gas load of Beijing from 2017 to 2020. The results show that the combined model can reduce the interference of the original data to the prediction system, the prediction accuracy meets the actual requirements, and the accuracy is much higher than the traditional gray model. It can truly reflect the development trend of annual natural gas load, and the prediction results are reliable and practical. It can provide reference for natural gas deployment and optimization of the pipeline network.

关 键 词：天然气 年负荷量 灰色模型 改进 预测 

分 类 号：TE89[石油与天然气工程—油气储运工程]