Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Document Type : Article

Authors

College of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China

Abstract

The aim of the paper is to develop a grey model for short term forecasting of natural
gas consumption and production in China and USA respectively. To enhance its prediction accuracy, the outliers are found by the error of the latent information function, and then corrected according to the test sample and the future trend. The sequence with corrected outliers is used to construct a grey model. The proposed model is employed to predict the natural gas consumption and production in China and USA. The results have demonstrated that the proposed model can raise the forecast accuracy of the grey model, and it also indicates that China will inevitably face a massive expansion in natural gas imports.

Keywords

Main Subjects


References
1. Karacaer-Ulusoy, M. and Kapusuzoglu, A. The dynamics
of nancial and macroeconomic determinants
in natural gas and crude oil markets: Evidence from
organization for economic cooperation and development/
gulf cooperation council/organization of the
petroleum exporting countries", International Journal
of Energy Economics and Policy, 7(3), pp. 167{187
(2017).
2. Azadeh, A. Asadzadeh, S.M. Mirseraji, G.H., et
al. An emotional learning-neuro-fuzzy inference approach
for optimum training and forecasting of gas
consumption estimation models with cognitive data",
Technological Forecasting and Social Change, 91, pp.
47{63 (2015).
3. Ozturk, I. and Al-Mulali, U. Natural gas consumption
and economic growth nexus: panel data analysis for
GCC countries", Renewable and Sustainable Energy
Reviews, 51, p. 9981003 (2015).
4. Furio, D. and Poblacion, J. Electricity and natural gas
prices sharing the long-term trend: some evidence from
the Spanish market", International Journal of Energy
Economics and Policy, 8(5), pp. 173{180 (2018).
5. Tamba, J.G., Essiane, S.N., et al. Forecasting natural
gas: a literature survey", International Journal of Energy
Economics and Policy, 8(3), pp. 216{249 (2018).
6. Melikoglu, M. Vision 2023: Forecasting Turkey's natural
gas demand between 2013 and 2030", Renewable
and Sustainable Energy Reviews, 22, pp. 393{400
(2013).
7. Mohr, S.H. and Evans, G.M. Long term forecasting
of natural gas production", Energy Policy, 39(9), pp.
5550{5560 (2011).
8. Boqiang, L. and Wang, T. Forecasting natural gas
supply in China: production peak and import trends",
Energy Policy, 49, pp. 225{233 (2012).
9. Primo, P., Boidar, S., Goran, I., et al. Comparison of
static and adaptive models for short-term residential
natural gas forecasting in Croatia", Applied Energy,
129, pp. 94{103 (2014).
10. Zhu, Q., Lu, Q.Y., Zhou, X.Y., et al. A driving force
analysis and forecast for gas consumption demand in
China", Mathematical Problems in Engineering, 2014,
pp. 1{11 (2014).
11. Palivonaite, R., Lukoseviciute, K., and Ragulskis,
M. Short-term time series algebraic forecasting with
mixed smoothing", Neurocomputing, 171, pp. 854{865
(2016).
12. Palivonaite, R., Lukoseviciute, K., and Ragulskis, M.
Algebraic segmentation of short nonstationary time
series based on evolutionary prediction algorithms",
Neurocomputing, 121(9), pp. 354-364 (2013).
13. Spoladore, A., Borelli, D., Devia, F., et al. Model for
forecasting residential heat demand based on natural
gas consumption and energy performance indicators",
Applied Energy, 182, pp. 488{499 (2016).
14. Soldo, B. Forecasting natural gas consumption", Applied
Energy, 92, pp. 26{37 (2012).
15. Wu, L., Liu, S., Yao, L., et al. The e ect of sample
size on the grey system model", Applied Mathematical
Modelling, 37(9), pp. 6577{6583 (2013).
16. Mao, S., He, Q., Xiao, X., et al. Study of the
correlation between oil price and exchange rate under
the new state of the economy", Scientia Iranica, 26(4),
pp. 2472{2483 (2019).
17. Li, G., Masuda, Sh., and Nagai, M. The prediction
for Japan's domestic and overseas automobile production",
Technological Forecasting and Social Change,
87, pp. 224{231 (2014).
18. Hamzacebi, C. and Es, H.A. Forecasting the annual
electricity consumption of Turkey using an optimized
grey model", Energy, 70(1), pp. 165{171 (2014).
19. Hashem-Nazari, M., Esfahanipour, A., and Fatemi
Ghomi, S.M.T. A basic form-focused modeling and
a modi ed parameter estimation technique for grey
prediction models", Scientia Iranica, E., 25(5), pp.
2867{2880 (2018).
20. Yi, Y., Yanhua, Ch., Jun, S., et al. An improved grey
neural network forecasting method based on genetic
algorithm for oil consumption of China", Journal of
Renewable and Sustainable Energy, 8(2), p. 024104
(2016).
21. Chang, C., Li, D., Chen, C., et al. A forecasting
model for small non-equigap data sets considering data
weights and occurrence possibilities", Computers &
Industrial Engineering, 67, pp. 139{145 (2014).
22. Li, G., Masuda, S., and Nagai, M. Predicting the
subscribers of xed-line and cellular phone in Japan by
a novel prediction model", Technological Forecasting
and Social Change, 81, pp. 321{330 (2014).
L. Wu et al./Scientia Iranica, Transactions E: Industrial Engineering 28 (2021) 386{394 393
23. Xiuli, L., Blanca, M., and Salom, G. A grey neural
network and input-output combined forecasting
model", Primary Energy Consumption Forecasts in
Spanish Economic Sectors, Energy, 115, pp. 1042{
1054 (2016).
24. Wang, J., Jiang, H., Zhou, Q., et al. Chinas natural
gas production and consumption analysis based on the
multicycle Hubbert model and rolling grey model",
Renewable and Sustainable Energy Reviews, 53, pp.
1149{1167 (2016).
25. Wu, L., Liu, S., Chen, H., et al. Using a novel grey
system model to forecast natural gas consumption in
China", Mathematical Problems in Engineering, 2015,
pp. 1{7 (2015).
26. Chang, C., Li, D., Dai, W., et al. A latent information
function to extend domain attributes to improve the
accuracy of small-data-set forecasting", Neurocomputing,
129, pp. 343{349 (2014).
27. Vaghe , M., Mahmoodi, K., and Akbari, M. A
comparison among data mining algorithms for outlier
detection using
ow pattern experiments", Scientia
Iranica, A., 25(2), pp. 590{605 (2018).
28. Li, Z. and Tian, X. The data outlier elimination
method based on grey relation", Proceedings of 2011
Cross Strait Quad-Regional Radio Science and Wireless
Technology Conference, Harbin, China (2011).
29. http://www.fool.com/investing/general/2013/
08/11/how-much-natural-gas-can-americaexport.aspx
30. Lianfu, H., Changfeng, F., Jun, W., et al. Outlier
detection and correction for the deviations of tooth
pro les of gears", Measurement Science Review, 13(2),
pp. 56{62 (2013).
31. Lee, Y.S. and Tong, L.I. Forecasting energy consumption
using a grey model improved by incorporating
genetic programming", Energy Conversion and Management,
52(1), pp. 147{152 (2011).
32. Tsai, C.F. Dynamic grey platform for ecient forecasting
management", Journal of Computer and System
Sciences, 81(6), pp. 966{980 (2015).