Predicting China's energy consumption using a novel grey Riccati model

被引:59
|
作者
Wu, Wenqing [1 ,2 ]
Ma, Xin [1 ,3 ]
Wang, Yong [3 ,4 ]
Cai, Wei [5 ]
Zeng, Bo [6 ]
机构
[1] Southwest Univ Sci & Technol, Sch Sci, Mianyang 621010, Sichuan, Peoples R China
[2] Sichuan Normal Univ, Visual Comp & Virtual Real Key Lab Sichuan Prov, Chengdu 610068, Peoples R China
[3] Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Peoples R China
[4] Southwest Petr Univ, Sch Sci, Chengdu 610500, Peoples R China
[5] Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China
[6] Chongqing Technol & Business Univ, Coll Business Planning, Chongqing 400067, Peoples R China
基金
中国国家自然科学基金;
关键词
Grey Riccati model; Energy consumption; Simulated annealing algorithm; Genetic algorithm; Optimized parameter; FORECASTING-MODEL; BASS MODEL; OPTIMIZATION; ALGORITHMS; GROWTH;
D O I
10.1016/j.asoc.2020.106555
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper studies the China's oil consumption and the China's nuclear energy consumption by a grey Riccati model. The newly developed model is analysed by the trapezoidal formula of definite integrals, the theory of ordinary differential equations and the grey technique. And some special cases including the GM(1,1) model, the grey Verhulst model and the grey Bass model are all discussed. Meanwhile, the hybrid of the simulated annealing algorithm and the genetic algorithm is utilized to search optimal background values. Further, the performance of the new model is verified through some experiments. Finally, the model is applied to study China's energy consumption with original sequences from 2001 to 2018 claimed by British Petroleum Statistical Review of World Energy 2019, and the results show that the new model can obtain competitive results and better than other comparative models. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:11
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