NIWM:非侵入式水监测,以发现家庭的热能使用情况

IF 2.4 Q1 Computer Science
Samuel Schöb, Sebastian A. Günther, Karl Regensburger, Thorsten Staake
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引用次数: 1

摘要

在欧洲和美国,热水的使用占家庭平均能源消耗的13-18%,而照明和烹饪分别仅占4%和6%。由于水加热主要依赖于石油、天然气和电力,热水的使用已被确定为许多碳减排计划的重要目标。我们提出并描述了一个系统,该系统可与非侵入式电力负荷监测相媲美,从中央计量装置中分离出水。该系统可用于提供消费反馈,将信息输入能源管理系统,并有助于识别过度的水和能源使用。该系统依赖于事件检测技术和自适应随机森林分类器。我们在四个多月的时间里在两个家庭中测试和验证了这个系统。系统能够检测到85%的提取事件,然后我们将其分类(“洗碗机”,“淋浴”,“水龙头”,“厕所”和“洗衣机”)。随机森林的f值在71到91%之间。每个器具的曲线下面积都在0.9以上。我们得出结论,家电是可靠的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NIWM: non-intrusive water monitoring to uncover heat energy use in households
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.
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来源期刊
SICS Software-Intensive Cyber-Physical Systems
SICS Software-Intensive Cyber-Physical Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-
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