IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v264y2023ics0360544222030407.html
   My bibliography  Save this article

A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting

Author

Listed:
  • Zhang, Yunxin
  • Guo, Huan
  • Sun, Ming
  • Liu, Sifeng
  • Forrest, Jeffrey

Abstract

Energy is the foundation for the stable operation and long-term growth of the national economy. Quantifying the degree of competition and cooperation among different types of energy consumption and predicting its future development trend will help to analyse the changes in energy consumption structure, to better formulate and make decisions on energy policies. In view of the inherent complexity of the energy consumption system structure, this paper proposes a novel grey Lotka–Volterra model (GLVM) for energy consumption forecasting, to evaluate the impact of long-term competition and cooperation on the national energy consumption system and its development trend. In theory, the solution method and parameter identification of the GLVM are given, and the parameter characteristics of GLVM under multiplicative transformation are analysed. Based on this, the GLVM is used to analyse the consumption structure of different types of energy in China, the United States and Germany, and quantitatively analyses the internal relationship of the energy consumption structure of the three representative countries. Compared with other models, the results show that this model is superior to other existing models in accuracy and interpretability. The model proposed in this paper is of great value to researchers and decision makers.

Suggested Citation

  • Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222030407
    DOI: 10.1016/j.energy.2022.126154
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222030407
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126154?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. Payne, James E., 2009. "On the dynamics of energy consumption and output in the US," Applied Energy, Elsevier, vol. 86(4), pages 575-577, April.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    4. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    5. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
    6. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    7. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    8. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    9. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    10. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    11. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    12. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.
    13. Yoo, Seung-Hoon & Lim, Hea-Jin & Kwak, Seung-Jun, 2009. "Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias," Applied Energy, Elsevier, vol. 86(4), pages 460-465, April.
    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. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    16. 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).
    17. 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.
    18. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    19. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    20. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    21. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    22. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    23. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Scenario analysis of nonresidential natural gas consumption in Italy," Applied Energy, Elsevier, vol. 113(C), pages 392-403.
    24. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    25. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    26. Tian, Jinfang & Yu, Longguang & Xue, Rui & Zhuang, Shan & Shan, Yuli, 2022. "Global low-carbon energy transition in the post-COVID-19 era," Applied Energy, Elsevier, vol. 307(C).
    27. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    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. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    2. Yuvaraj Natarajan & Sri Preethaa K. R. & Gitanjali Wadhwa & Young Choi & Zengshun Chen & Dong-Eun Lee & Yirong Mi, 2024. "Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction," Sustainability, MDPI, vol. 16(5), pages 1-23, February.

    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. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    2. 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).
    3. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    4. 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).
    5. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    6. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    7. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    8. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    9. 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).
    10. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    11. 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.
    12. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    13. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    14. 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.
    15. 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).
    16. Fan, Jingjing & Wang, Jianliang & Liu, Mingming & Sun, Wangmin & Lan, Zhixuan, 2022. "Scenario simulations of China's natural gas consumption under the dual-carbon target," Energy, Elsevier, vol. 252(C).
    17. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    18. Afzal, Sadegh & Ziapour, Behrooz M. & Shokri, Afshar & Shakibi, Hamid & Sobhani, Behnam, 2023. "Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms," Energy, Elsevier, vol. 282(C).
    19. Yeqi An & Yulin Zhou & Rongrong Li, 2019. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods," Energies, MDPI, vol. 12(13), pages 1-24, July.
    20. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.

    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:264:y:2023:i:c:s0360544222030407. 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.