Forecasting natural gas consumption of China by using a novel fractional grey model with time power term

• Main Authors: Chong Liu, Wen-Ze Wu, Wanli Xie, Tao Zhang, Jun Zhang
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: Natural gas consumption; DFGM(1,1,tα); Quantum Genetic Algorithm (QGA)
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484721000834

Natural gas, an important low-carbon and clean energy, is increasingly replacing high-pollution sources such as coal and gasoline. Accurate natural gas consumption forecasts are important to policy makers in making plans, saving costs, and improving efficiency. This study developed a discrete fractional grey model with a time power term (denoted as DFGM(1,1,tα)) with reference to the discretization technique. The new model is simplified, generalized, and overcomes existing model drawbacks. Moreover, the quantum genetic algorithm (QGA) is used to determine the new coefficients, namely, the fractional order and time-power coefficient. Based on observations from 2001 to 2018, the novel model predicted the natural gas consumption in China from 2019 to 2025 better than other benchmarks. Specifically, natural gas consumption was predicted to maintain a steady upward trend, reaching 315.30 billion cubic metres (hereinafter referred to as Bcm) in 2020 and 439.14 Bcm in 2025. Reasonable suggestions are put forward for associated sectors based on the forecasts.