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Prediction of SO2 Emission from Industrial Sector in Shanghai City based on Novel Discrete Grey Model.

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Title: Prediction of SO2 Emission from Industrial Sector in Shanghai City based on Novel Discrete Grey Model.
Authors: Jiefang Liu1,2 liujflOI@126.com, Yunrui Guo1, Bingjun Li2, Pumei Gao3, Jian Liu4, Wanqin Zhang1, Mingjun Jiao1
Source: Journal of Grey System. 2017, Vol. 29 Issue 3, p26-35. 10p.
Subjects: Prediction models, Gray forecasting model, Sulfur oxides, Greenhouse gas mitigation, Perturbation theory
Geographic Terms: Shanghai (China)
Abstract: In order to reduce the modeling errors of the discrete grey prediction model and increase the stability of the solution, this paper presents the fractional-order reverse accumulative discrete grey forecasting model(FORA-DGM (1,1) model). The perturbation bounds of the model was analyzed through the matrix perturbation theory. And it is proved that the FORA-DGM (1,1) model has the smaller perturbation bounds of solution than traditional discrete grey forecasting model. Thus, it has good stability. Finally, the FORA-DGM (1,1) mode was applied to predict the SO2 emission from industrial sector in Shanghai city. The modeling results show that the simulation error and prediction error of FORA-DGM (1,1) mode was less than traditional discrete grey forecasting mode, especially in the prediction aspects. And it verified the validity and practicability of the FORA-DGM (1,1) mode. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Grey System is the property of Research Information Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 124888592
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Prediction of SO<subscript>2</subscript> Emission from Industrial Sector in Shanghai City based on Novel Discrete Grey Model.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Grey+System%22">Journal of Grey System</searchLink>. 2017, Vol. 29 Issue 3, p26-35. 10p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+forecasting+model%22">Gray forecasting model</searchLink><br /><searchLink fieldCode="DE" term="%22Sulfur+oxides%22">Sulfur oxides</searchLink><br /><searchLink fieldCode="DE" term="%22Greenhouse+gas+mitigation%22">Greenhouse gas mitigation</searchLink><br /><searchLink fieldCode="DE" term="%22Perturbation+theory%22">Perturbation theory</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Shanghai+%28China%29%22">Shanghai (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to reduce the modeling errors of the discrete grey prediction model and increase the stability of the solution, this paper presents the fractional-order reverse accumulative discrete grey forecasting model(FORA-DGM (1,1) model). The perturbation bounds of the model was analyzed through the matrix perturbation theory. And it is proved that the FORA-DGM (1,1) model has the smaller perturbation bounds of solution than traditional discrete grey forecasting model. Thus, it has good stability. Finally, the FORA-DGM (1,1) mode was applied to predict the SO2 emission from industrial sector in Shanghai city. The modeling results show that the simulation error and prediction error of FORA-DGM (1,1) mode was less than traditional discrete grey forecasting mode, especially in the prediction aspects. And it verified the validity and practicability of the FORA-DGM (1,1) mode. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Grey System is the property of Research Information Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 26
    Subjects:
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Gray forecasting model
        Type: general
      – SubjectFull: Sulfur oxides
        Type: general
      – SubjectFull: Greenhouse gas mitigation
        Type: general
      – SubjectFull: Perturbation theory
        Type: general
      – SubjectFull: Shanghai (China)
        Type: general
    Titles:
      – TitleFull: Prediction of SO2 Emission from Industrial Sector in Shanghai City based on Novel Discrete Grey Model.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Jiefang Liu
      – PersonEntity:
          Name:
            NameFull: Yunrui Guo
      – PersonEntity:
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            NameFull: Bingjun Li
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            NameFull: Pumei Gao
      – PersonEntity:
          Name:
            NameFull: Jian Liu
      – PersonEntity:
          Name:
            NameFull: Wanqin Zhang
      – PersonEntity:
          Name:
            NameFull: Mingjun Jiao
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          Dates:
            – D: 01
              M: 07
              Text: 2017
              Type: published
              Y: 2017
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              Value: 09573720
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              Value: 29
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              Value: 3
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            – TitleFull: Journal of Grey System
              Type: main
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