ANALYZING COAL CONSUMPTION IN CHINA: FORECASTING WITH THE ECFGM(1, 1) MODEL AND A PERSPECTIVE ON THE FUTURE

Abstract

The study analyzes coal consumption using the ECFGM(1, 1) model by utilizing time series data provided by the Statistical Review of World Energy for the years 2016-2019. The optimal α value, determined using the Brute Force Algorithm, is utilized to establish the model’s parameters and formulate the solution function. Subsequently, the model’s predictive accuracy is assessed using data from the years 2020-2022, with the resulting Mean Absolute Percentage Error (MAPE) reflecting the model’s overall performance.

Keywords

• Coal consumption
• ECFGM(1, 1)
• Brute Force algorithm

References

1. [1] Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1, 288-294.
2. [2] David, K. W. Ng. (1994). Grey system and grey relational model. ACM SIGICE Bulletin, 20, 2-9.
3. [3] Morton, A. B., Mareels, I. M. Y. (2000). An efficient Brute Force solution to the network reconfiguration problem. IEEE Transactions On Power Delivery, 15, 996-1000.
4. [4] Khalil, R., Al Horani, M., Abdelrahman, Y., Mohammad, S. (2014). A new definition of fractional derivative. J. Comput. Appl. Math., 264, 65-70.
5. [5] Javed, S. A., Liu, S. (2018). Predicting the research output/growth of selected countries: application of even GM(1, 1) and NDGM models. Scientometrics, 115, 395-413.
6. [6] Duan, H., Lei, G. R., Shao, K. (2018). Forecasting crude oil consumption in China using a grey prediction model with an optimal fractional-order accumulating operator. Hindawi, 2018, 1076-2787.
7. [7] Wang, J., Du, P., Lu, H., Yang, W., Niu, T. (2018), An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting. Applied Soft Computing, 72, 321-337.
8. [8] Ozturk, Z., Bilgil, H. (2019). Mathematical estimation of expenditures in the health sector in Turkey with Grey Modeling. Journal of Institue of Science and Technology, 35.

9. [9] Wang, Z. X., Li, Q. (2019). Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model. Journal of Cleaner Production, 207, 214-224.
10. [10] Ikram, M., Mahmoudi, A., Shah, S. Z. A., Mohsin, M. (2019). Forecasting number of ISO 14001 certifications of selected countries: application of even GM(1, 1), DGM, and NDGM models, Environmental Science and Pollution Research. 26, 12505-12521.
11. [11] Ma, X., Xie, M., Wu, W., Zeng, B., Wang, Y., Wu, X. (2019). The novel fractional discrete multivariate grey system model and its applications. Applied Mathematical Modelling, 70, 402-424.
12. [12] Ma, X., Mei, X., Wu, W., Wu, X., Zeng, B. (2019). 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, 487-507.
13. [13] Wu, W., Ma, X., Wang, Y., Zhang, Y., Zeng, B. (2019). Research on a novel fractional GM(α,n) model and its application, Grey Systems: Theory and Application, 9.
14. [14] Bilgil, H. (2020). New grey forecasting model with its application and computer code. AIMS Mathematics, 6, 1497-1514.
15. [15] Zhou, W., Wu, X., Ding, S. (2020). J. Pan, Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China. Energy, 200, 117443.
16. [16] Ma, X., Wu, W., Zeng, B., Wang, Y., Wu, X. (2020). The conformable fractional grey system model. ISA Transactions, 96, 255-271.
17. [17] Wu, W., Ma, X., Zhang, Y., Li, W., Wang, Y. (2020). A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. Science of the total environment, 707, 1-24.
18. [18] Wu, W., Ma, X., Zeng, B., Lv, W., Wang, Y.,Li, W. (2020). A novel grey Bernoulli model for short-term natural gas consumption forecasting. Applied Mathematical Modelling, 84, 393-404.
19. [19] Liu, C., Lao, T., Wu, W. Z., Xie, W. (2021). Application of optimized fractional grey model-based variable background value to predict electricity consumption. Fractals, 29, 2150038.
20. [20] Yuxiao, K., Shuhua, M., Yonghong, Z. (2021), Variable order fractional grey model and its application. Applied Mathematical Modelling, 97, 619-635.
21. [21] Wang, Y., He, X., Zhang, L., Ma, X., Wu, W., Nie, R., Chi, P., Zhang, Y. (2022). A novel fractional time-delayed grey Bernoulli forecasting model and its application for the energy production and consumption prediction, Engineering Applications of Artificial Intelligence, 110, 104683.
22. [22] Kang, Y., Mao, S., Zhang, Y. (2022). Fractional time-varying grey traffic flow model based on viscoelastic fluid and its application. Transportation Research Part B: Methodological, 157, 149-174.
23. [23] Ozturk, Z., Bilgil, H., Erdinc, U. (2022). An optimized continuous fractional grey model for forecasting of the time dependent real world cases, Hacet. J. Math. Stat., 51, 308-326.
24. [24] Li, X., Zhou, S., Zhao, Y., Yang, B. (2023). Marine and land economy-energy-environment systems forecasting by novel structural-adaptive fractional time-delay nonlinear systematic grey model. Engineering Applications of Artificial Intelligence, 126, 106777.
25. [25] Wang, Y., Sun, L., Yang, R., He, W., Tang, Y., Zhang, Z., Wang, Y., Sapnken, F. E. (2023). A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction. Energy, 282, 128380.
26. [26] Wang, Y., Zhang, L., He, X., Ma, X., Wu, W., Nie, R., Chi, P., Zhang, Y. (2023). A novel exponential time delayed fractional grey model and its application in forecasting oil production and consumption of China. Cybernetics and Systems, 54, 168-196.
27. [27] Thike, A. M., Lupin, S., Khaing, M. T. (2023). Methods for improving the efficiency of Brute-Force algorithm by the example of solving an Unbounded Knapsack Problem. International Journal of Open Information Technologies, 11.
28. [28] Zeng, B., Chen, G., Meng, W., Wang, J. (2024). Prediction, analysis and suggestions of shale gas production in China based on a new grey model with four parameters. Alexandria Engineering Journal, 86, 258-276.
29. [29] Erdinc, U., Bilgil, H., Ozturk, Z. (2024). A novel fractional forecasting model for time dependent real world cases. Revstat-Statistical Journal, 22.
30. [30]https://www.energyinst.org/_data/assets/pdf_file/0004/1055542/El_Stat_Review_PDF_single_3.pdf