A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting. (1st February 2023)

• Record Type: Journal Article
• Authors: Zhang, Yunxin; Guo, Huan; Sun, Ming; Liu, Sifeng; Forrest, Jeffrey
• Is Part Of: Energy. Volume 264(2023)
• Publication Date: 2023-02-01
• Subjects: Energy consumption structure -- Competition and cooperation -- Grey Lotka–Volterra model -- Energy consumption forecasting; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126154
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: A new grey Lotka–Volterra model is proposed for energy consumption forecasting. The new model can quantitatively analyse the internal mechanism of energy structure. The parameter characteristics of the new model are analysed and proved. The new model is superior to other existing models in accuracy and interpretability.