Noise-robust and sector-aware representation learning for natural gas demand forecasting

With natural gas becoming a key component of energy systems, precise demand forecasting is crucial for supporting efficient planning and resource management. However, existing methods face two key challenges: substantial noise in industrial datasets and heterogeneous consumption patterns across sectors. Data noise caused by sensor errors, irregular reporting, and logging inconsistencies obscures underlying consumption trends. Simultaneously, sector-specific variations in demand make it challenging to develop a unified forecasting model capable of capturing diverse consumption behaviors. To address these challenges, we propose a novel data forecasting framework that integrates contrastive learning with targeted noise filtering to enhance data representation and prediction robustness. The noise filtering module incorporates a denoising task that enables the model to learn to suppress noise and improve representation reliability. Meanwhile, the contrastive learning mechanism leverages sector-specific information to capture both shared patterns and sectoral usage behaviors. We further introduce a false negative removal strategy to refine sample selection, reducing representation bias and enhancing generalization. Our approach is validated on a large-scale dataset from the ENN Group, covering over 10,000 industrial, commercial, and welfare-related customers across multiple regions. Experimental results demonstrate that our model consistently outperforms a range of state-of-the-art forecasting baselines across both short- and long-term horizons, achieving notably better accuracy and robustness in real-world scenarios. This work demonstrates the potential of noise-robust and sector-aware representation learning for advancing natural gas demand forecasting in real-world applications.