Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account.

Automated summary

This research proposes a hybrid mathematical model, called the Choquet integral-based model, to reliably predict natural gas consumption. The model combines the grey accumulation generating operator and the grey wolf optimizer to comprehensively consider model performance and handle both seasonal and long-term variability. The results demonstrate that the proposed model exhibits better robustness and prediction performance by considering the interaction between models.