Water usage data collected from smart meters at the end user can improve the accuracy and applicability of water distribution network models. Collecting and storing large amounts of data across hundreds or more smart meters is costly, which makes it important to consider what constitutes a sufficient sampling interval. This paper explores the effect of varying sampling intervals in smart meter data on model performance in regard to flow, pressure and water age simulations. Furthermore, the effect of using linear interpolation, a demand pattern or a network-inflow-weighted approach to fill gaps when data are sampled coarsely, is investigated. The study was based on real data from 525 smart meters in a district metered area in Denmark. The results show that smart meter data can improve modelling results, and if the sampling intervals are coarser than 2 h, then a weighted gap-filling approach markedly outperforms linear interpolation and models with coarse bi-annual demand data.

最终用户从智能水表收集的用水数据可以提高配水网络模型的准确性和适用性。在数百个或更多的智能电表中收集和存储大量数据的成本很高，这使得考虑什么构成足够的采样间隔非常重要。本文探讨了有关流量，压力和水龄模拟的智能水表数据中不同采样间隔对模型性能的影响。此外，研究了在数据粗略采样时使用线性插值，需求模式或网络流入加权方法填补空白的效果。该研究基于丹麦某地区计量区内525个智能电表的真实数据。结果表明，智能电表数据可以改善建模结果，