Incorporating security risks into natural gas forecasting: An interpretable, policy-oriented machine learning framework

Abstract

Most existing predictive models remain demand-centric and fail to systematically incorporate supply-side risks such as import dependence, price volatility, and market concentration. Thus, this study proposes a SHAP-driven Weighted Rule Attention Mechanism (SWRAM), and explicitly embeds energy security risk factors including import dependence, market concentration, and price volatility into natural gas consumption forecasting. The model integrates explainable machine learning with a rule-constrained attention mechanism to enable both transparent feature attribution and robust predictive performance, and, when compared with the benchmark models, it demonstrates lower forecasting errors and more stable generalization, thereby validating the effectiveness of embedding SHAP-based rule-constrained weights within the attention mechanism. Using monthly data for China from 2012 to 2024, the results show that supply capacity and infrastructure remain the dominant drivers of natural gas consumption, while risk-related factors have gained importance since 2020, reflecting the impact of global supply-chain instability. Interaction analysis reveals a strong nonlinear coupling between domestic production and import dependence, indicating that insufficient domestic output amplifies exposure to external risks. Price volatility exerts an increasingly significant effect during global energy shocks, especially between 2021 and 2023. These findings suggest that natural gas consumption is shaped jointly by short-term demand cycles and long-term structural dependencies. The SWRAM framework provides an interpretable and policy-oriented tool for improving forecasting reliability and supporting data-driven energy security governance.

Graphical abstract

Keywords

natural gas consumption / risk factors / attention mechanism / SHAP values / machine learning

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