Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.

从历史上看，用水量测量一直是手动进行的，每月的读数少于一次。领先的水服务提供商已经开始部署智能水表来收集高分辨率数据。开发了一个低成本的冲水计数器，并将其连接到用于大学校园内7座建筑物中119个超低冲水马桶的实时监控平台，以探索建筑物用户如何影响需水量。厕所的使用遵循典型的每周模式，其中平日使用比周末使用高92％±4。在学期期间，厕所需求量较高，并且与建筑物居住者的数量之间存在密切的正相关关系。与办公用途的建筑物相比，混合用途的建筑物在学期和节假日之间的厕所使用趋势往往更大。研究结果表明，冲洗传感器方法学是值得进一步考虑的可靠方法。来自研究数据集的补充数据将使从业人员能够将捕获的数据用于（i）预测模型，以为水资源计划提供依据；（ii）警报系统以自动执行维护计划；（iii）动态清洁时间表；（iv）监察建筑物的使用率；（v）设计智能雨水收集系统，以满足实时数据的需求；（vi）探索动态水价模型，以激励最佳的现场储水策略。（iv）监察建筑物的使用率；（v）设计智能雨水收集系统，以满足实时数据的需求；（vi）探索动态水价模型，以激励最佳的现场储水策略。（iv）监察建筑物的使用率；（v）设计智能雨水收集系统，以满足实时数据的需求；（vi）探索动态水价模型，以激励最佳的现场储水策略。