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城市天然气月用量的预测方法
引用本文:梁金凤,郭开华,皇甫立霞.城市天然气月用量的预测方法[J].化工学报,2015,66(Z2):392-398.
作者姓名:梁金凤  郭开华  皇甫立霞
作者单位:中山大学工学院, 广东广州 510275
基金项目:广东省普通高校液化天然气与低温技术重点实验室资助项目(39000-3211101);中山大学BP液化天然气中心资助项目(99103-9390001)。
摘    要:基于城市天然气用量具有"有限制增长"特点以及以12个月为周期变化的规律建立城市月用气量预测模型,分析不同类型城市预测模型的相关参数,并进行实例应用。结果表明,基于逻辑斯蒂原理以及月用气量变化分布曲线函数建立的城市月用气量预测模型可以很好地表征城市月用气量的增长过程特点和周期变化规律;不同类型城市的月用气量变化分布曲线特征值及其变化范围各异,而且高峰点前后曲线宽度不同。集中采暖类城市月用气量高峰特征值与低谷特征值相差较大,非集中采暖类城市月用气量高峰特征值与低谷特征值相近,完全生产类城市月用气量具有明显的个体特点。

关 键 词:算法  城市  天然气  用量  模型  预测  
收稿时间:2015-05-29
修稿时间:2015-06-09

Prediction method of city natural gas monthly consumption
LIANG Jinfeng,GUO Kaihua,HUANGFU Lixia.Prediction method of city natural gas monthly consumption[J].Journal of Chemical Industry and Engineering(China),2015,66(Z2):392-398.
Authors:LIANG Jinfeng  GUO Kaihua  HUANGFU Lixia
Affiliation:School of Engineering, Sun Yat-sen University, Guangzhou 510275, Guangdong, China
Abstract:A prediction model of city nature gas monthly consumption was established basing on city natural gas monthly consumption's limited growth and period of 12 months,the related parameters of different types of city of prediction model is analyzed,and example is applied.The results showed that the prediction model of city nature gas monthly consumption which was established basing on logistic and distribution curve,can be a good characterization of the growth of city gas in process characteristics and change rules; the characteristic value and its variation range of distribution curve was different from each type of city,and the width was different before and after the peak point curve.The peak and trough eigenvalue of distribution curve of natural gas monthly consumption was big difference when the city had central heating system,the peak and trough eigenvalue of distribution curve of natural gas monthly consumption was little difference when the city had non central heating system,the natural gas monthly consumption of complete production city had obvious individual characteristics.
Keywords:gorithm  city  natural gas  consumption  model  prediction  
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