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The Construction Of Seasonal Grey Model And Its Application In Natural Gas Consumption

Posted on:2022-07-18Degree:MasterType:Thesis
Country:ChinaCandidate:J PanFull Text:PDF
GTID:2480306518970619Subject:Business Administration
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With the development and implementation of China's national energy and environmental policies,natural gas has attracted the attention of the people in recent years,and its proportion in the primary energy consumption structure has also increased rapidly.With my country's call for "coal to gas" and the construction of the West-East Gas Pipeline project,the demand for natural gas continues to rise.In the context of rational planning for clean energy,accurate prediction of natural gas consumption is even more important.In recent years,the economy has developed rapidly,and the level of industrialization and modernization has been continuously improved.China's economy has entered a period of high-quality growth.For a long time,coal has been the main source of energy consumption in China,although it has provided strong impetus to my country's economy.However,the natural environmental problems caused by it are getting worse and the air pollution is getting worse.Inspired by policies related to economic development and environmental protection,it is imperative for China to adjust its energy consumption structure.On the one hand,it reduces the proportion of coal in the primary energy consumption structure,and on the other hand,it actively improves the development and utilization of high-quality natural gas energy.In the future,the consumption of natural gas will continue to increase,but as a nonrenewable energy source,its storage capacity is extremely limited.If it is not planned and utilized,it will face the dilemma of energy shortage in the future.Therefore,a more accurate prediction of the natural gas consumption sequence can better plan the utilization of natural gas and ensure the safety of natural gas energy.Natural gas consumption is a typical seasonal time series,and its consumption will be subject to obvious seasonal changes,which is mainly reflected in its monthly and quarterly series.Different changes in the natural gas consumption sequence with seasonal characteristics will have different impacts on the local economy.The forecast of monthly and quarterly natural gas consumption is more important than the forecast of annual natural gas consumption.Because it can not only ensure the safety of natural gas energy,but also make more detailed planning of natural gas resources.At present,there is a lack of high-precision research on natural gas consumption with seasonal characteristics,and the natural gas consumption sequence with seasonal characteristics is not only affected by the season but also by the mobile holiday effect.The existing models are not sufficient to deal with the above double disturbance factor.Based on the above background,this article reviews and analyzes the existing research on natural gas prediction,and finds that whether foreign or domestic,the prediction methods of natural gas consumption can be roughly summarized into three categories: one is statistical measurement models,such as the SARIMA model;the other is artificial intelligence Models,such as the ANN model and SVR model;the third is the gray model,which in turn plays an important role in predicting natural gas consumption because of its simple modeling and no statistical requirements for data.Therefore,a novel seasonal gray model is established to optimize the accuracy of the model from the "antidisturbance" of dual disturbance factors,which has both theoretical and practical significance.This paper first sorts out the relevant factors that affect natural gas consumption data,and proposes a discrete cumulative grey seasonal model based cycle accumulation(CDGSM(1,1)model)based on relevant influencing factors,selects monthly and quarterly natural gas consumption data for empirical analysis,and combines the forecast results with statistics Measurement model.The artificial neural network model is compared to test the validity of the model and the prediction accuracy.The results show that the new model has the best prediction accuracy.Finally,it is applied to the forecast of natural gas consumption in the next five years,and reasonable suggestions are given for the future development of natural gas production and related energy security.
Keywords/Search Tags:natural gas consumption, grey forecast, CDGSM(1,1) model, natural gas policy
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