Improved Energy Structure Prediction Model Based on Energy Demand Forecast

被引:0
|
作者
Li, Xueliang [1 ]
Ge, Yuyou [2 ]
Mao, Xuejiao [2 ]
Xue, Wanlei [1 ]
Xu, Nan [1 ]
机构
[1] State Grid Shandong Elect Power Co, Econ & Technol Res Inst, Jinan, Shandong, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China
关键词
Markov chain theory; energy structure prediction; grey system theory; energy demand forecast;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In recent years, with the outbreak of the energy crisis and the increasingly serious environmental problems, the energy consumption structure changes tremendously. Proportion of nonrenewable resources is declining year by year, while the renewable energy's proportion increases steadily. Therefore, it is necessary for a city to predict energy structure accurately, in order to make a reasonable development plan. Through increasing restrictions based on the energy demand forecast and the future energy plan, this paper introduces the Markov chain to set up an improved energy structure prediction model. Combining the data of the energy demand energy structure during 2003 to 2016 and the energy developing plan of a certain region, the proposed model is tested. The result verifies the feasibility of the model.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] An improved grey Verhulst model to forecast energy demand in the USA and Turkey
    Atalay, Sevcan Demir
    Adiyaman, Meltem
    Calis, Gulben
    [J]. PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-ENGINEERING SUSTAINABILITY, 2022, 175 (03) : 154 - 164
  • [2] Study on Beijing's energy utilization and forecast energy supply and demand based on grey model
    Li, Hongze
    Guo, Sen
    Fu, Liwen
    [J]. NATURAL RESOURCES AND SUSTAINABLE DEVELOPMENT, PTS 1-3, 2012, 361-363 : 1240 - 1243
  • [3] Forecast of total rural energy demand based on energy consumption intensity
    Wu, Guilian
    Zhang, Linyao
    Lin, Tingting
    Hao, Chen
    Li, Wenlong
    Wang, Shaofang
    Jun, Yan
    Liu, Conghao
    [J]. FRONTIERS IN ENERGY RESEARCH, 2023, 10
  • [4] Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting
    Jiang, Peng
    Hu, Yi-Chung
    Wang, Wenbao
    Jiang, Hang
    Wu, Geng
    [J]. MATHEMATICS, 2020, 8 (06)
  • [5] ENERGY MANAGEMENT AND ENERGY DEMAND FORECAST OF A METALLURGICAL PLANT
    FETT, FN
    REH, M
    STROHSCHEIN, H
    [J]. STAHL UND EISEN, 1993, 113 (05): : 109 - 119
  • [6] Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model
    Yin, Ziyi
    Jia, Benyou
    Wu, Shiqiang
    Dai, Jiangyu
    Tang, Deshan
    [J]. WATER, 2018, 10 (04)
  • [7] Application of combined model in energy demand prediction
    Meng, Fansheng
    Li, Meiying
    [J]. Li, M. (752265815@qq.com), 1600, Harbin Institute of Technology (45): : 106 - 111
  • [8] Prediction of primary energy demand in China based on AGAEDE optimal model
    Liu, Lu
    Huang, Junbing
    Yu, Shiwei
    [J]. CHINESE JOURNAL OF POPULATION RESOURCES AND ENVIRONMENT, 2016, 14 (01) : 16 - 29
  • [9] Prediction of primary energy demand in China based on AGAEDE optimal model
    Lu Liu
    Junbing Huang
    Shiwei Yu
    [J]. Chinese Journal of Population,Resources and Environment, 2016, (01) : 16 - 29
  • [10] Coal logistics demand forecast based on an integrated prediction model
    Pan, Yanfang
    Liu, Enyou
    [J]. ENERGY SCIENCE AND APPLIED TECHNOLOGY (ESAT 2016), 2016, : 533 - 536