Artikel

Application of a new grey prediction model and grey average weakening buffer operator to forecast China's shale gas output

Scientifically and accurately forecasting of future shale gas output tends is very important in making energy policies, especially for China whose historical data of shale gas output is very limited. The existing grey shale gas output prediction model does not perform well in prediction due to its defects. To overcome these shortcoming, this paper, based on the principle of "new information priority", combined with the contradiction between model prediction results and qualitative data analysis conclusions, designs a grey prediction model combining new initial conditions and original data reprocessing. Then the new model's accumulative order is optimized by fraction accumulation generation operation and its properties is discussed. Finally, the new model is used to simulate and forecast shale gas output in China from 2012 to 2018 and compared it with the existing shale gas prediction model. The comparison results show that the new model reduces by 84.34%, 68.96% and 75.60% of the mean relative simulation percentage error (MRSPE), mean relative prediction percentage error (MRPPE) and comprehensive mean relative percentage error (CMPPE) respectively. This paper not only has theoretical innovations but also provides a good mathematical method for predicting shale gas output.

Sprache
Englisch

Erschienen in
Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Pages: 1608-1618 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
China's shale gas output
Grey prediction model
Grey weakening buffer operator
Initial value
New information prioritization

Ereignis
Geistige Schöpfung
(wer)
Zeng, Bo
Zhou, Meng
Liu, Xianzhou
Zhang, Zhiwei
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2020

DOI
doi:10.1016/j.egyr.2020.05.021
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Zeng, Bo
  • Zhou, Meng
  • Liu, Xianzhou
  • Zhang, Zhiwei
  • Elsevier

Entstanden

  • 2020

Ähnliche Objekte (12)