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：天然气消耗预测需兼顾季节性与趋势性，对于规划其供应与消费、优化获取成本（尤其对工业实体而言）至关重要。在供应面临威胁的时期，该预测亦是保障这一原材料满足个体消费者需求、确保社会能源安全的关键要素。本文提出一种新颖的天然气消耗多步超前预测方法，该方法集成变点检测机制以筛选模型集合，并采用数据流处理技术实现持续学习能力。基于所提方法的预测模型性能在一个复杂的真实世界天然气消耗预测案例中得到评估。我们采用Hoeffding树作为预测模型，并运用Pruned Exact Linear Time (PELT)算法进行变点检测。变点检测集成机制能够为连续时间段选择不同的模型集合。为此，我们定义并评估了三种模型集合筛选流程（含/不含误差反馈回路），以应对不同变点检测密度的预测场景。这些模型与忽略变点的基线方法进行了对比。实验表明：无论采用何种模型集合筛选流程，较少的变点数量均会带来更低的预测误差。同时，省略预测误差反馈的简化型模型集合筛选流程可产生更鲁棒的预测模型，适用于持续学习任务。