Unsupervised Residential Power Usage Monitoring Using a Wireless Sensor Network

作者：Tan Rui^*; Phillips Dennis E; Moazzami Mohammad Mahdi; Xing Guoliang; Chen Jinzhu

来源：ACM Transactions on Sensor Networks, 2017, 13(3): 20.

DOI：10.1145/3078240

• Nonintrusive load monitoring
• wireless sensor networks
• sensor fusion
• unsupervised learning

Appliance-level power usage monitoring may help conserve electricity in homes. Several existing systems achieve this goal by exploiting appliances' power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in a high installation cost. This article presents Supero the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, nonprofessional users can quickly deploy Supero with considerable flexibility.

• 出版日期2017-9
• 单位南阳理工学院