Natural gas plays an important role in China's low-carbon energy development and transformation process due to its clean, low-carbon, stable, flexible, and economic characteristics. To accurately predict the quarterly production of natural gas in China, this paper proposes a novel seasonal grey prediction model with weighted fractional order accumulation operator. Firstly, based on the seasonal fluctuations of the raw data, the raw data is divided into four seasonal groups. Secondly, when an external disturbance affects the system, the classic average weakening buffer operator is used to weaken its effects. Then, a new weighted fractional order accumulation operator is created

by combining the new information accumulation generation operator and the fractional order accumulation generation operator. Finally, the new information accumulation parameters λ and the fractional-order cumulative generating operator parameter r, are optimized using the particle swarm optimization technique (PSO). The experimental results show that the new grey prediction model (DGGM(1,1,λ,r)and DGDGM(1,1,λ,r)) performs better than other models in predicting quarterly natural gas production of China. Finally, the two models are used to estimate China’s natural gas production in the next 3 years and put forward some relevant policy recommendations.

The grey model proposed in this paper optimizes the accumulation method of the traditional grey model, and flexibly adjusts the generation sequence through the two parameters introduced, so as to explore the internal law of the data information at a deeper level, and achieve the purpose of improving the model accuracy.