NDLI: PresenceSense: zero-training algorithm for individual presence detection based on power monitoring

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PresenceSense: zero-training algorithm for individual presence detection based on power monitoring

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PresenceSense: zero-training algorithm for individual presence detection based on power monitoring

Content Provider	ACM Digital Library
Author	Jin, Ming Konstantakopoulos, Ioannis C. Spanos, Costas J. Jia, Ruoxi Kang, Zhaoyi
Abstract	Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450331449
DOI	10.1145/2674061.2674073
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Non-intrusive method Power measurement Plug loads Semi-supervised learning Energy saving Occupancy detection
Content Type	Text
Resource Type	Article

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