PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring

TL;DR

PresenceSense is a zero-training, semi-supervised algorithm that detects individual presence in buildings using power data, outperforming traditional models without labeled data, and has applications in energy saving and security.

Contribution

The paper introduces PresenceSense, a novel semi-supervised learning algorithm that detects presence using power data without requiring labeled training data.

Findings

PresenceSense outperforms models trained on large labeled datasets.

It effectively detects individual presence using only unlabeled power data.

Potential applications include energy management and security in buildings.

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.