摘要
天然气是一种重要的资源,具有高效、清洁、安全等众多优点,现今已得到广泛应用。精准预测未来天然气需求量对能源战略的制定、安排天然气的生产及进出口具有显著意义。为预测中国未来天然气需求量,本文使用一种新的混合优化方法,即DDE-BAG算法,该方法是在原始的DEBA算法基础上,使用自适应原理,对原始算法的变异算子和交叉算子进行调整,改变其扰动方式和变异方式得到的。新算法不仅具有搜索全局最优性的能力,而且具有更强的局部搜索能力和更加迅速的收敛速度。本文选取人均生活用气量、天然气消耗在能源消耗中的占比、经济增长(GDP)和人口城镇化率四个影响因素作为模型的输入因子,使用1986~2015年的数据作为观察数据,运用DDE-BAG算法对天然气需求估计模型的多重线性形式和指数形式进行系数优化,得出两种模型的最优系数。结果表明,建立的两种模型的预测值与观察值极其接近,并将两种模型的判定系数及预测误差进行比较,最后使用两种模型采用两种不同的方式对中国2016~2030年期间的天然气需求量进行预测,结果发现,虽然采用的预测方式不同,但预测的结果相近。
Natural gas is an important resource,and it has many advantages such as high efficiency,cleanliness and safety.Nowadays,it has been widely used.Accurately forecasting the demand for natural gas in future will have a significant positive effect on the formulation of energy strategy and on the production,import and export of natural gas.In order to predict the demand of natural gas from China,a new hybrid optimization method,DDE-BAG,is proposed in this paper.This method is based on the original DEBA algorithm and uses the adaptive principle to adjust the mutation operator and the crossover operator,change the way of disturbance and mutation obtained.In this paper,we chose four factors of living gas consumption per capita,the proportion of natural gas consumption in energy consumption,economic growth (GDP) and population urbanization rate as the input factors.Using data from 1986 to 2015 for 30 years as the observation data,the DDE-BAG algorithm was used to optimize the coefficients of multiple linear and exponential forms of natural gas demand estimation models,and the optimal coefficients of the two models were obtained.The results showed that the predicted values and observed values of the two models were close to each other.Finally,two models were used to analyze the predicted values of two models in China from 2016 to 2030 during the forecast of natural gas demand.The results showed that,even though the forecasting methods adopted were different,the results of the forecasting were similar.
作者
邹绍辉
丁治立
ZOU Shaohui;DING Zhili(School of Management,Xi an University of Science and Technology,Xi an 710054,China;Energy Economy and Management Research Center,Xi an University of Science and Technology,Xi an 710054,China)
出处
《中国矿业》
北大核心
2018年第8期62-69,共8页
China Mining Magazine
基金
国家自然科学青年基金项目"生态环境约束下我国煤炭跨期最优开采规模及保障机制研究"资助(编号:71704140)
关键词
天然气
需求预测
通径分析
人工智能
能源结构
natural gas
demand forecast
path analysis
artificial intelligence
energy structure