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

Yunxin Zhang, Huan Guo, Ming Sun, Sifeng Liu and Jeffrey Forrest

Energy, 2023, vol. 264, issue C

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.

Keywords: Energy consumption structure; Competition and cooperation; Grey Lotka–Volterra model; Energy consumption forecasting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222030407
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:264:y:2023:i:c:s0360544222030407

DOI: 10.1016/j.energy.2022.126154

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().