Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers' needs, ensuring society's energy security. This article introduces a novel multistep ahead forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables selecting a different model collection for successive time frames. Thus, three model collection selection procedures (with and without an error feedback loop) are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches. Our experiments show that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed. Also, simpler model collection selection procedures omitting forecasting error feedback leads to more robust forecasting models suitable for continual learning tasks.

翻译：考虑到季节性和趋势的天然气消耗预测对于规划其供应与消费、优化获取成本至关重要，特别是在工业实体中。然而，在面临供应威胁时，它也是保障该原材料满足个体消费者需求、确保社会能源安全的关键因素。本文提出了一种新颖的多步提前天然气消耗预测方法，该方法集成变点检测技术实现模型集合选择，并通过数据流处理具备持续学习能力。基于所提方法的预测模型在复杂的真实世界天然气消耗预测用例中进行了性能评估。我们采用Hoeffding树预测器作为预测模型，并使用Pruned Exact Linear Time（PELT）算法进行变点检测。变点检测的集成使得能够针对连续时间段选择不同的模型集合。因此，本文定义并评估了三种模型集合选择程序（含误差反馈回路与不含误差反馈回路），用于具有不同变点检测密度的预测场景。将这些模型与忽略变点的基准方法进行了比较。实验表明，无论采用何种模型集合选择程序，较少的变点都会导致较低的预测误差。此外，省略预测误差反馈的简单模型集合选择程序能够产生适用于持续学习任务的更稳健预测模型。