A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China

Tianyao Duan, Huan Guo, Xiao Qi, Ming Sun and Jeffrey Forrest

Energy, 2024, vol. 304, issue C

Abstract: Diversified energy power generation is a critical component of China’s West-to-East Electricity Transmission (WEET) project and a key driver of China’s clean energy strategy. Aiming at the complex non-linear relationship of inter-regional energy system and fully exploring the information of system data, our paper proposes a novel information-enhanced Grey Lotka–Volterra model (IE-GLVM). The novel model consists of Grey Lotka–Volterra equations and universal network terms, which achieves a clever fusion of energy system competitive relationships and data information-driven modeling in the modeling methodology. In addition, the Joint Gradient Descent method is used to optimally search for all the parameters of the novel model, and we theoretically prove the stability of the algorithm. Based on this, the IE-GLVM model is used to analyze the competitive and cooperative relationships among the three provinces of Sichuan, Hubei, and Jiangsu in the middle line of the WEET project in China under multiple energy sources for power generation and to forecast the future power generation. Eventually, IE-GLVM was compared with three benchmark models, and it demonstrated superior performance in most cases. An analysis and summary of the power generation relationships of each regional energy source were conducted based on the quantitative results of the IE-GLVM model.

Keywords: West-to-East Electricity Transmission; Grey Lotka–Volterra model; Information enhanced; Network; Power forecasting (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:304:y:2024:i:c:s0360544224019509

DOI: 10.1016/j.energy.2024.132176

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