In Europe and the US, hot water use accounts for 13–18% of the average home’s energy consumption, compared to just 4 and 6% for lighting and cooking, respectively. As water heating mostly relies on oil, gas, and electricity, hot water use has been identified as an important target of many carbon reduction programs. We propose and describe a system that—comparable to non-intrusive load monitoring for electricity—disaggregates water extractions from a central metering device. The system can be used to provide consumption feedback, feed information into energy management systems, and can help to identify excessive water and energy use. The system relies on event-detection techniques and adapted Random Forest classifiers. We have tested and validated the system in two households over four months. The system was able to detect 85% of the extraction events which we then classify (“Dishwasher”, “Shower”, “Tap”, “Toilet”, and “Washing machine”). Random Forest achieves an F-measure between 71 and 91%. The area under the curve is above 0.9 for each appliance. We conclude that appliances are predicted reliably.

中文翻译：

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NIWM：非侵入式水监测以发现家庭的热能使用

在欧洲和美国，热水使用量占家庭平均能耗的13％至18％，而照明和烹饪分别仅占4％和6％。由于水加热主要依靠石油，天然气和电力，因此热水使用已被确定为许多减碳计划的重要目标。我们提出并描述了一种系统，该系统与电力的非侵入式负载监测相比，可以分解从中央计量设备提取的水。该系统可用于提供消耗量反馈，将信息馈入能源管理系统，并有助于识别过多的水和能源使用情况。该系统依靠事件检测技术和适应的随机森林分类器。我们在四个月的时间内对两个家庭进行了测试和验证。该系统能够检测出85％的提取事件，然后将其分类（“洗碗机”，“淋浴”，“水龙头”，“厕所”和“洗衣机”）。随机森林的F测度介于71％和91％之间。每个设备的曲线下面积均大于0.9。我们得出结论，可以可靠地预测设备。