Predicting primary energy consumption using NDGM(1,1,k,c) model with Simpson formula

Document Type : Article

Authors

1 - School of Science, Southwest University of Science and Technology, 621010, Mianyang, China. - V.C. and V.R. Key Lab of Sichuan Province, Sichuan Normal University, 610068, Chengdu, China

2 - School of Science, Southwest University of Science and Technology, 621010, Mianyang, China. - State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, 610500, Chengdu, China.

3 - School of Science, Southwest Petroleum University, 610500, Chengdu, China - State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, 610500, Chengdu, China

4 College of Engineering and Technology, Southwest University, 400715, Chongqing, China

5 College of Business Planning, Chongqing Technology and Business University, 400067, Chongqing, China

Abstract

Energy consumption plays a key role in economics development for all countries. Catching the future trend of energy consumption is very important for the governments and energy companies. In this paper, the primary energy consumption of Saudi Arabia, India, Philippines and Vietnam are systematically studied by various forecasting models. Based on the actual data from 2006 to 2016, a novel grey forecasting model termed NDGM_S (1,1,k,c) is proposed where the Simpson numerical integration formula is applied to construct the background value. The time response function and the restored value of the present model are deduced, and then the unbiased property is proved. As shown in the computational results, the NDGM_S (1,1,k,c) model can achieve better prediction accuracy than other forecasting models, and it is quite suitable for predicting sequence with homogeneous/non-homogeneous exponential law.

Keywords


References
1. BP Statistical Review of World Energy 2017", www.bp.com (Accessed Jun 2017).
2. Lee, C.C. Energy consumption and GDP in developing countries: a cointegrated panel analysis", Energy
Econ., 27(3), pp. 415{427 (2005).
3. Neto, A.H. and Fiorelli, F.A.S. Comparison between detailed model simulation and arti cial neural network
for forecasting building energy consumption", Energy
Build., 40(12), pp. 2169{2176 (2008).
4. Ma, M., Cai, W., Cai, W., and Dong, L. Whether
carbon intensity in the commercial building sector
decouples from economic development in the service
industry? empirical evidence from the top  ve urban
agglomerations in China", J. Cleaner Prod., 222, pp.
193{205 (2019).
5. Ma, M., Ma, X., Cai, W., and Cai, W. Carbondioxide
mitigation in the residential building sector:
a household scale-based assessment", Energ. Convers.
Manage., https://doi:10.1016/j.enconman.2019.111915
(2019).
6. Wang, Y., Zhang, C., Chen, T., and Ma, X. Modeling
the nonlinear 
ow for a multiple fractured horizontal
well with multiple  nite-conductivity fractures in triple
media carbonate reservoir", Journal of Porous Media,
21(12), pp. 1283{1305 (2018).
7. Liang, Y., Cai, W., and Ma, M. Carbon dioxide
intensity and income level in the Chinese megacities'
residential building sector: decomposition and decoupling
analyses", Sci. Toal Environ., 677, pp. 315{327
(2019).
8. Yang, W., Wang, J., Lu, H., Niu, T., and Du, P.
Hybrid wind energy forecasting and analysis system
based on divide and conquer scheme: a case study in
China", J. Cleaner Prod., 222, pp. 942{959 (2019).
9. Du, P., Wang, J., Yang, W., and Niu, T. A novel
hybrid model for short-term wind power forecasting",
Appl. Soft Comput., 80, pp. 93{106 (2019).
10. Wu, L., Liu, S., Yao, L., and Yu, L. Fractional order
grey relational analysis and its application", Scientia
Iranica, 22, pp. 1171{1178 (2015).
11. Zeng, B., Duan, H., and Zhou, Y. A new multivariable
grey prediction model with structure compatibility",
Appl. Math. Model, 75, pp. 385{397 (2019).
12. Hashem-Nazari, M., Esfahanipour, A., and Ghomi,
F.S. A basic-form focused modeling and a modi ed
parameter estimation technique for grey prediction
models", Scientia Iranica, 25, pp. 2867{2880 (2018).
13. Ma, X. A brief introduction to the grey machine
learning", J. Grey System, 31(1), pp. 1{12 (2019).
14. Ma, X., Xie, M., Wu, W., Wu, X., and Zeng, B.
A novel fractional time delayed grey model with
grey wolf optimizer and its applications in forecasting
the natural gas and coal consumption in Chongqing
China", Energy, 178, pp. 487{507 (2019.)
15. Deng, J. Control problems of grey systems", Systems
& Control Letters, 1(5), pp. 288{294 (1982).
16. Wu, W., Ma, X., Zeng, B., Wang, Y., and Cai,
W. Application of the novel fractional grey model
FAGMO(1,1,k) to predict China's nuclear energy consumption",
Energy, 165, pp. 223{234 (2018).
17. Ma, X., Wu, W., Zeng, B., Wang, Y., and
Wu, X. The conformable fractional grey
system model", ISA T., 96, pp. 255{271
https://doi:10.1016/j.isatra.2019.07.009 (2019).
18. Zeng, B. and Chuan, L. Improved multi-variable
grey forecasting model with a dynamic backgroundvalue
coecient and its application", Computers &
Industrial Engineering, 118(4), pp. 278{290 (2018).
19. Wang, Z. and Li, Q. Modelling the nonlinear relationship
between CO2 emissions and economic growth
using a PSO algorithm-based grey Verhulst model", J.
Cleaner Prod., 207, pp. 214{224 (2019).
20. Wu, W., Ma, X., Zeng, B., Wang, Y., and Cai, W.
Forecasting short-term renewable energy consumption
of China using a novel fractional nonlinear grey
Bernoulli model", Renewable Energy, 140, pp. 70{87
(2019).
21. Ma, X. and Liu, Z. The GMC(1,n) model with
optimized parameters and its application", J. Grey
System, 29(4), pp. 122{138 (2017).
22. Mao, S., He, Q., Xiao, X., and Rao, C. Study of the
correlation between oil price and exchange rate under
the new state of the economy", Scientia Iranica, 26(4),
pp. 2472{2483 (2018). Doi: 1024200/SCI201820448
23. Ma, X., Xie, M., Wu, W., Zeng, B., Wang, Y., and
Wu, X. The novel fractional discrete multivariate
grey system model and its applications", Appl. Math.
Modell., 70, pp. 402{424 (2019).
24. Cui, J., Liu, S., Zeng, B., and Xie, N. A novel grey
forecasting model and its optimization", Appl. Math.
Modell., 37(6), pp. 4399{4406 (2013).
25. Chen, P. and Yu, H. Foundation settlement prediction
based on a novel NGM model", Mathematical Problems
in Engineering, 2014(1), pp. 1{8 (2014).
26. Xie, N., Liu, S., Yang, Y., and Yuan, C. On novel grey
forecasting model based on non-homogeneous index
sequence", Appl. Math. Modell., 37(7), pp. 5059{5068
(2013).
27. He, G., Wu, W., and Zhang, Y. Performance analysis
of machine repair system with single working
vacation", Communications in Statistics-Theory and
Methods, 48(22), pp. 5602{5620 (2019).
28. Wang, Y. and Yi, X. Flow modeling of well test analysis
for a multiple fractured horizontal well in triple
media carbonate reservoir", International Journal of
Nonlinear Sciences and Numerical Simulation, 19(5),
pp. 439{457 (2018).
W. Wu et al./Scientia Iranica, Transactions D: Computer Science & ... 28 (2021) 3379{3395 3395