Abstract
In the transformation of the energy system, natural gas energy is regarded as a buffer energy. How to make a reasonable energy distribution and effectively predict its production is very significant. In the work of this paper, a grid-optimized fractional-order non-homogeneous grey model is used to predict the natural gas energy production in the United States and obtain reliable results. This paper first introduces the prediction method and prediction mechanism. Then the model is optimized to make the prediction effect more prominent. The natural gas energy prediction results show that this method has high prediction accuracy compared with other methods, which means that the method proposed in this paper can be used as an effective tool for short-term forecasting of natural gas production in the United States and play an auxiliary role in energy forecasting.
Highlights
Energy has become a problem that every country must pay attention to
In terms of natural gas energy prediction, Li et al used particle swarm optimization to optimize the non-homogeneous discrete model and achieved good results[10], and Zheng et al verified the optimization of the fractional Bernoulli model with the moth flame optimization (MFO) algorithm based on the production and consumption of natural gas[11]
This paper uses the mechanism of one-step rolling prediction to model and predict the grey model
Summary
Energy has become a problem that every country must pay attention to. Global energy demand is expected to increase by 35% in 2035[1]. Natural gas has always been a very important energy source in the world. Studying the natural gas energy production of the world's industrial power, the United States, has certain significance for energy development. In the development of the grey model, Cui et al constructed a new model to improve the prediction accuracy of the data sequence that approximates the characteristics of non-homogeneous exponential law[5]. In terms of natural gas energy prediction, Li et al used particle swarm optimization to optimize the non-homogeneous discrete model and achieved good results[10], and Zheng et al verified the optimization of the fractional Bernoulli model with the moth flame optimization (MFO) algorithm based on the production and consumption of natural gas[11].
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