Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms

Forecasting petroleum consumption is a complicated and challenging task because many parameters affect the oil consumption. Whereas a highly accurate prediction model can help one utilize data resources reasonably, an inaccurate model will lead to a waste of resources. Thus, choosing an optimization model with the best forecasting accuracy is not only a challenging task but also a remarkable problem for oil consumption forecasting. However, a single model cannot always satisfy time series forecasting and the variations in oil consumption. In this paper, a total of 26 combination models using traditional combination method were developed to increase the prediction accuracy and avoid the problem of individual risk prediction methods "over-fitting", which would reduce the accuracy. Our conclusion is that the proposed combination models provide desirable forecasting results compared to the traditional combination model, and the combination method of TCM-NNCT is the most feasible and effective one. This paper also discussed the factors related to the statistical models and the results can be used by policy makers to plan strategies. Numerical results demonstrated that the proposed combined model is not only robust but able to approximate the actual consumption satisfactorily, which is an effective tool in analysis for the energy market.