Problem
Research questions and friction points this paper is trying to address.
water demand forecasting
leak detection
public datasets
data quality
cumulative index
The Tethys Dataset: Seven Years of Hourly Smart Water Metering and a Pipeline for Making It Usable
🤖 AI Summary
为解决水需求预测和泄漏检测数据稀缺问题，本文发布Tethys数据集，提供91个月的小时级用水数据，并公开其质量评估与处理流程。
📝 Abstract
Methods for water demand forecasting and leak detection are based on public datasets, and for water those are scarce, short, or released only after an undocumented cleaning process, hiding defects of the deployment they came from. We present Tethys: 91 months of hourly water consumption data from 24 buildings of a municipal water network, published with a quantitative account of its quality, rather than in place of one. Raw availability is 59.4%, while loss is not random: 4 fleet-wide outages totalling 595 days interrupt the entire estate at once. We show that the aggregation producing the released files silently introduced 205,200 impossible decreases in a cumulative index, and that correcting it is a one-line change. Because the meters are cumulative, the readings bracketing a short gap fix the volume that passed through it, so 75.9% of hours rest on a measurement, while 24.1% are reported as unknown. We release the dataset, its per-hour provenance, and the pipeline that produces it.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tethys Dataset
Water Consumption Data
Data Quality Assessment
Error Correction
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