Appliance Recognition using Hall Effect Sensors and K-Nearest Neighbors for Power Management Systems

被引：0

作者：

Miranda, Lester James V. ^{[1
]}

Gutierrez, Marian Joice S. ^{[1
]}

Dumlao, Samuel Matthew G. ^{[1
]}

Reyes, Rosula S. J. ^{[1
]}

机构：

[1] Ateneo Manila Univ, Dept Elect Comp & Commun Engn, Katipunan Ave, Quezon City 1108, Metro Manila, Philippines

来源：

PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON) | 2016年

关键词：

power management; computational intelligence; internet of things;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Power management systems employ appliance recognition such that the burden of manually configuring the system for each appliance is lifted from the user. This research then aims to develop an appliance recognition functionality through current readings gathered from a data acquisition (DAQ) device consisting of Hall Effect current sensors, and through a machine learning classification algorithm called k-nearest neighbors. Ten appliances were tested, comprising of 6,500 samples of test data in the four outlets tested. The average accuracy for the trials is 92.73%. In addition, the appliance recognition functionality was embedded to a cloud-based power management system following an Internet of Things (IoT) architecture. In the end, the developed system can gather data from plugged appliances, perform recognition, and carry out various power management functionalities such as monitoring and appliance-level smart-recommendations.

引用

页码：6 / 9

页数：4

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