IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v243y2022ics0360544221032734.html
   My bibliography  Save this article

Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China

Author

Listed:
  • Wang, Meng
  • Wang, Wei
  • Wu, Lifeng

Abstract

Scientific prediction of regional energy is of practical significance for rational control of energy supply. In this paper, to further minimize the influence of subjective factors, the grey relational analysis and the FGM(1,1) model whose forecast results are affected by the fixed order are selected to analyze the samples. Then, a new grey multi-variable AGMC(1,N) model with better prediction performance is used to study 13 provinces (cities) in 7 different regions of China in detail. The results show that only the energy consumption in Central China will decrease in the short term. Energy consumption in the remaining 6 regions will continue to rise, and the energy consumption of East China, Northeast China, and Northwest China have a remarkable growth trend. It is important to provide reliable data to China's energy regulators and provide a reference for regional energy reform in the short term.

Suggested Citation

  • Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221032734
    DOI: 10.1016/j.energy.2021.123024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221032734
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.123024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2018. "Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption," Energy, Elsevier, vol. 165(PB), pages 223-234.
    2. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    3. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
    4. Hye-Yeong Lee & Kee Moon Jang & Youngchul Kim, 2020. "Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model," Energies, MDPI, vol. 13(17), pages 1-17, August.
    5. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    6. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    7. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    8. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    9. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    10. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    11. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    12. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 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, Elsevier, vol. 178(C), pages 487-507.
    13. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    14. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    15. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    16. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yong & Yang, Zhongsen & Wang, Li & Ma, Xin & Wu, Wenqing & Ye, Lingling & Zhou, Ying & Luo, Yongxian, 2022. "Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model," Energy, Elsevier, vol. 259(C).
    2. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    3. Shen, Yuewen & Wen, Lihong & Shen, Chaowen, 2024. "Based on hypernetworks and multifractals: Deep distribution feature fusion for multidimensional nonstationary time series prediction," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    4. Bei He & Xiaoyun Du & Junkang Li & Dan Chen, 2023. "A Effectiveness-and Efficiency-Based Improved Approach for Measuring Ecological Well-Being Performance in China," IJERPH, MDPI, vol. 20(3), pages 1-29, January.
    5. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    6. Xu He & Qin-Lei Jing, 2022. "The Impact of Environmental Tax Reform on Total Factor Productivity of Heavy-Polluting Firms Based on a Dual Perspective of Technological Innovation and Capital Allocation," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    7. Zhu, Huimin & Xiao, Xinping & Kang, Yuxiao & Kong, Dekai, 2022. "Lead-lag grey forecasting model in the new community group buying retailing," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    8. Sun, Ya-Fang & Yu, Shiwei & Zhang, Yue-Jun & Su, Bin, 2023. "How do imports change the energy consumption of China? An analysis of its role in intermediate inputs and final demands," Energy, Elsevier, vol. 270(C).
    9. Chengguang Liu & Jiaqi Zhang & Xixi Luo & Yulin Yang & Chao Hu, 2023. "Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    10. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    11. Geng Wu & Yi-Chung Hu & Yu-Jing Chiu & Shu-Ju Tsao, 2023. "A new multivariate grey prediction model for forecasting China’s regional energy consumption," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4173-4193, May.
    12. John A. Jinapor & Shafic Suleman & Richard Stephens Cromwell, 2023. "Energy Consumption and Environmental Quality in Africa: Does Energy Efficiency Make Any Difference?," Sustainability, MDPI, vol. 15(3), pages 1-26, January.
    13. Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    14. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    15. Wang, Yong & Chi, Pei & Nie, Rui & Ma, Xin & Wu, Wenqing & Guo, Binghong, 2022. "Self-adaptive discrete grey model based on a novel fractional order reverse accumulation sequence and its application in forecasting clean energy power generation in China," Energy, Elsevier, vol. 253(C).
    16. Jia Wu & Na Wu & Qiang Feng & Chenning Deng & Xiaomin Zhang & Zeqiang Fu & Zeqian Zhang & Haisheng Li, 2023. "Design and Optimization of a Coal Substitution Path Based on Cost–Benefit Analysis: Evidence from Coal Resource-Based Cities in China," Sustainability, MDPI, vol. 15(21), pages 1-14, October.
    17. Li, Hui & Wu, Zixuan & Yuan, Xing & Yang, Yixuan & He, Xiaoqiang & Duan, Huiming, 2022. "The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth," Energy, Elsevier, vol. 257(C).
    18. Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).
    19. Zhixiong Weng & Yuqi Song & Hao Ma & Zhong Ma & Tingting Liu, 2023. "Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10369-10391, September.
    20. Wang, Yong & Yang, Zhongsen & Ye, Lingling & Wang, Li & Zhou, Ying & Luo, Yongxian, 2023. "A novel self-adaptive fractional grey Euler model with dynamic accumulation order and its application in energy production prediction of China," Energy, Elsevier, vol. 265(C).
    21. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    22. Zhu, Yi & Lin, Yangyi & Tan, Yanyu & Liu, Bin & Wang, Hao, 2024. "The potential nexus between fintech and energy consumption: A new perspective on natural resource consumption," Resources Policy, Elsevier, vol. 89(C).
    23. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).
    24. Yang, Yang & Wang, Xiuqin, 2022. "A novel modified conformable fractional grey time-delay model for power generation prediction," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Wang, Yong & Yang, Zhongsen & Wang, Li & Ma, Xin & Wu, Wenqing & Ye, Lingling & Zhou, Ying & Luo, Yongxian, 2022. "Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model," Energy, Elsevier, vol. 259(C).
    3. Wang, Yong & Chi, Pei & Nie, Rui & Ma, Xin & Wu, Wenqing & Guo, Binghong, 2022. "Self-adaptive discrete grey model based on a novel fractional order reverse accumulation sequence and its application in forecasting clean energy power generation in China," Energy, Elsevier, vol. 253(C).
    4. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    5. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    6. Peng Zhang & Xin Ma & Kun She, 2019. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    7. Liu, Yitong & Xue, Dingyu & Yang, Yang, 2021. "Two types of conformable fractional grey interval models and their applications in regional electricity consumption prediction," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    8. Peng Zhang & Xin Ma & Kun She, 2019. "Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    9. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    10. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    11. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    12. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    13. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    14. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    15. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    16. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).
    17. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    18. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    20. Wang, Yong & He, Xinbo & Zhou, Ying & Luo, Yongxian & Tang, Yanbing & Narayanan, Govindasami, 2024. "A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction," Energy, Elsevier, vol. 302(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221032734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.