As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.

随着天然气市场朝着短期计划发展，准确，可靠的短期天然气需求预测对于稳定能源供应，天然气控制时间表和日常运输运营至关重要。基础。我们基于最新的统计模型和人工神经网络，提出了一种混合预测模型，功能自回归和卷积神经网络模型。我们对德国高压天然气管网中92个分布节点的每小时天然气流量进行了短期预测，结果表明，该模型为不同类型的节点提供了良好且稳定的精度。它的性能优于所有其他模型，相对精度提高了两倍（工厂节点）和四倍（市政节点）。