Artikel
Forecasting natural gas consumption in China by Bayesian Model Averaging
With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA). It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.
- Language
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Englisch
- Bibliographic citation
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Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 1 ; Year: 2015 ; Pages: 216-220 ; Amsterdam: Elsevier
- Classification
-
Wirtschaft
- Subject
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Bayesian Model Averaging
Forecasting
Natural gas consumption
- Event
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Geistige Schöpfung
- (who)
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Zhang, Wei
Yang, Jun
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
-
2015
- DOI
-
doi:10.1016/j.egyr.2015.11.001
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Zhang, Wei
- Yang, Jun
- Elsevier
Time of origin
- 2015
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