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

Chong Liu · Wen-Ze Wu · Wanli Xie · Tao Zhang · Jun Zhang

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Grey System Theory Applications Forecasting Techniques and Applications Energy, Environment, Economic Growth

Abstract

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.

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References

1982Control problems of grey systems 2012Grey system model with the fractional order accumulation 1982Industrial and business forecasting methods 2008Discrete grey forecasting model and its optimization 2019The conformable fractional grey system model 2019Carbon trading volume and price forecasting in China using multiple machine learning models 2018A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors 2019Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model

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