应用时效函数对天然气需求进行预测  

TIME EFFECT FUNCTION APPLIED TO PREDICT NATURAL GAS DEMAND

作　　者：殷建成[1] 刘志斌[2] 陈友福[1] 

机构地区：[1]中国石油西南油气田分公司 [2]西南石油学院

出　　处：《天然气工业》2005年第10期136-138,共3页Natural Gas Industry

基　　金：四川省教育厅自然科学基金"气井及气藏计算机仿真及优化配产软件系统"项目(2004A150)资助。

摘　　要：用线性回归、人工神经网络、灰色系统等预测模型对天然气消费需求量进行预测后,为综合利用各种方法提供的信息,避免单一预测模型丢失有用信息的缺陷,减少随机性,提高预测的准确性,采用了优化组合预测模型和变权重的自适应递推优化组合预测模型对天然气消费需求量进行动态预测,取得了较好的预测效果。但经过翔实的分析,发现变权重的自适应递推优化组合预测模型计算出的权系数,假定了“移动样本数据”的权重向量为常值,权重没有充分体现出数据的时效性。依据信息论“远小近大“的观点,引入时效函数对自适应优化组合预测模型进行改进。结果表明,融入时效函数的自适应递推优化组合预测模型比采用其它预测模型预测的结果更好。The gas demand is predicted with the linear regression forecast model; the artificial neutral network forecast model; and the gray system forecast model etc. respectively. To comprehensively utilize the information from different forecast model; avoid dropping the useful information, which may happen when using only one forecast model; decrease randomness and improve the forecast accuracy, the dynamic forecast is conducted for the gas demand by the optimized combination forecast model and the self-adapt optimized combination forecast model with weight variation. And good forecast results are achieved. But with careful analysis, it is found as for the weight coefficient calculated by the self-adapt optimized combination forecast model with weight variation, the weights don’t reflect the aging property of the data sufficiently since assuming the weight vector of “moved sample data” as a constant. According to the view of “the farer it is small, the closer it is big” by the information theory, the aging function is introduced to modify the self-adapt optimized combination forecast model. The results show the self-adapt optimized combination forecast model covering the aging function is better than the other forecast models for gas demand forecast.

关 键 词：天然气 需求量 动态分析 时间函数 优化 预测 数学模型 组合预测模型 天然气消费 时效性 

分 类 号：F407.22[经济管理—产业经济]