基于改进灰色GM(1,1)模型的天然气负荷预测
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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.
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.
引文
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[10]张英豪,刘晓青,宁昕扬.灰色-马尔科夫模型在南俄5水电站大坝变形预测中的应用研究[J].水利水电技术,2018,49(2):139-143.
[11]蒋白懿,牟天蔚,王玲萍.灰色遗传神经网络模型对居民年需水量预测[J].给水排水,2018,54(1):137-142.
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[14]马啸,李露.基于改进灰色GM(1,1)模型的三峡库区工业废水量预测[J].环境工程,2018,36(1):162-167.
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[16]张朝飞,罗建军,徐兵华,等.基于灰色理论的新陈代谢自适应多参数预测方法[J].上海交通大学学报,2017,51(8):970-976.
[17]陈昌源,戴冉,冯纪军,等.改进灰色模型在海上交通综合安全指数预测应用[J].中国航海,2017,40(1):58-63.
[2]薛方刚,赵光强,高彦鹏,等.最小二乘曲线拟合法预测天然气消费量[J].辽宁化工,2013,42(5):503-505.
[3]张涛,杨娇敏.我国区域天然气消费量的趋势预测[J].天然气工业,2016,36(9):135-140.
[4]高俊莲,徐向阳.基于ARIMA模型的中国天然气消费量预测与分析[J].数学的实践与认识,2015,45(9):44-50.
[5]王效东,黄坤,王兵.利用BP网络预测天然气消费量[J].天然气与石油,2008(1):25-27.
[6]吕金涛.基于指数平滑模型的我国天然气消费量预测[J].商场现代化,2012(25):171-172.
[7]李君臣,董秀成,高建.我国天然气消费的系统动力学预测与分析[J].天然气工业,2010,30(4):127-129.
[8]王兵,田丹,刘书文,等.天然气消费量的灰色模型预测[J].管道技术与设备,2008(5):3-5.
[9]顾乐民.对GM灰色模型及理论的分析[J].计算机工程与应用,2016,52(6):1-7.
[10]张英豪,刘晓青,宁昕扬.灰色-马尔科夫模型在南俄5水电站大坝变形预测中的应用研究[J].水利水电技术,2018,49(2):139-143.
[11]蒋白懿,牟天蔚,王玲萍.灰色遗传神经网络模型对居民年需水量预测[J].给水排水,2018,54(1):137-142.
[12] Zou Z L,Zuo T Y,Song Y C. Improvement of unbiased grey-forecasting model and its application on subsidence prediction[J]. Geomatics Science and Technology,2016,4(1):11-18.
[13]邓聚龙.灰色系统理论教程[M].武汉:华中理工大学出版社,1992:20-22.
[14]马啸,李露.基于改进灰色GM(1,1)模型的三峡库区工业废水量预测[J].环境工程,2018,36(1):162-167.
[15]党耀国,刘思峰,刘斌.以x(1)(n)为初始条件的GM模型[J].中国管理科学,2005,13(1):132-135.
[16]张朝飞,罗建军,徐兵华,等.基于灰色理论的新陈代谢自适应多参数预测方法[J].上海交通大学学报,2017,51(8):970-976.
[17]陈昌源,戴冉,冯纪军,等.改进灰色模型在海上交通综合安全指数预测应用[J].中国航海,2017,40(1):58-63.