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
相关论文
共 50 条
  • [31] Using grey models for forecasting China's growth trends in renewable energy consumption
    Tsai, Sang-Bing
    CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2016, 18 (02) : 563 - 571
  • [32] Using grey models for forecasting China’s growth trends in renewable energy consumption
    Sang-Bing Tsai
    Clean Technologies and Environmental Policy, 2016, 18 : 563 - 571
  • [33] Forecasting Renewable Energy Consumption Using a Novel Fractional Grey Reverse Accumulation Model
    Zhang, Yipeng
    Wang, Huiping
    SYSTEMS, 2025, 13 (01):
  • [34] A novel compositional data model for predicting the energy consumption structures of Europe, Japan, and China
    Xinping Xiao
    Xue Li
    Environment, Development and Sustainability, 2023, 25 : 11673 - 11698
  • [35] A novel compositional data model for predicting the energy consumption structures of Europe, Japan, and China
    Xiao, Xinping
    Li, Xue
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2023, 25 (10) : 11673 - 11698
  • [36] A novel discrete grey Riccati model and its application
    Zeng, Liang
    GREY SYSTEMS-THEORY AND APPLICATION, 2021, 11 (02) : 309 - 326
  • [37] Optimized multivariate grey forecasting model for predicting electricity consumption: A China study
    Zhao, Zhen-Yu
    Ma, Xu
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 43 (05) : 5859 - 5875
  • [38] Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption
    Wu, Wenqing
    Ma, Xin
    Zeng, Bo
    Wang, Yong
    Cai, Wei
    ENERGY, 2018, 165 : 223 - 234
  • [39] Forecasting the total energy consumption in China using a new-structure grey system model
    Zeng, Bo
    Luo, Chengming
    GREY SYSTEMS-THEORY AND APPLICATION, 2017, 7 (02) : 194 - 217
  • [40] Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model
    Wang, Huiping
    Zhang, Zhun
    SYSTEMS, 2023, 11 (06):