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Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China
Xiao, Yi1; Li, Keying1; Hu, Yi2; Xiao, Jin3; Wang, Shouyang4
2020-06-14
Source PublicationMITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE
ISSN1381-2386
Pages19
AbstractWith the orderly advancement ofChina Energy Development Strategic Action Plan, clean energy has become a major trend in the energy market. As a major industry of clean energy, natural gas industry plans to consume at least 10% of the total primary energy by 2020. The energy structure will be improved in an orderly manner to achieve the goal of energy conservation, consumption reduction, and emission reduction. To achieve energy saving and emission reduction, and using clean energy effectively, accurate prediction of natural gas consumption is of great importance. Because of the many influencing factors affecting natural gas demand, this paper first utilizes STRIPAT to analyze the factors affecting natural gas consumption and then uses a deep learning ensemble approach to analyze and predict China's natural gas consumption. One is an advanced deep neural network model named gated recurrent unit model which is used to model the nonlinear and complex relationships of natural gas consumption with its factors. The other is a powerful ensemble method named bootstrap aggregation which generates multiple data sets for training a set of base models. Our approach combines the advantages of these two technologies to forecast the demand for China's natural gas market. In empirical research, our method has been tested by some competitive methods and has shown superiority.
KeywordNatural gas consumption forecasting Emission reduction Deep learning STRIPAT model Gated recurrent unit model Bagging
DOI10.1007/s11027-020-09918-1
Indexed BySCI
Language英语
Funding Projectself-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE[CCNU19ZN024]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000540689300001
PublisherSPRINGER
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/51652
Collection中国科学院数学与系统科学研究院
Corresponding AuthorXiao, Yi
Affiliation1.Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.Sichuan Univ, Sch Business, Chengdu 610064, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xiao, Yi,Li, Keying,Hu, Yi,et al. Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China[J]. MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE,2020:19.
APA Xiao, Yi,Li, Keying,Hu, Yi,Xiao, Jin,&Wang, Shouyang.(2020).Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China.MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE,19.
MLA Xiao, Yi,et al."Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China".MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE (2020):19.
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