Forecasting the natural gas demand in China using a self-adapting intelligent grey model. (1st October 2016) - Zeng, Bo; Li, Chuan
Abstract: Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. Highlights: A self-adapting intelligent grey prediction model (SIGM) is proposed in this paper. The SIGM has the advantage of working with exponential functions and linear functions. The SIGM solves the drawbacks of fixed structure and poor adaptability of grey models. The demand of natural gas in China is successfully forecasted using the SIGM model. The study findings can help Chinese government reasonably formulate energy policies.