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

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.Comment: BuildSys 201

Building a wireless mesh networked real-time electricity metering system in an MIT dormitory

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009."February 2009." Cataloged from PDF version of thesis.Includes bibliographical references (p. 25).A competitive, closed-loop information feedback system of wireless electricity meters was designed, tested, and implemented in seven MIT dormitory rooms. The meters utilized Allegro Hall Effect current sensors as well as ZigBee based mesh networking transceivers. A remote database stored energy use data for the community and implemented data and graph caching for an online web interface made available to the system users. The website displayed detailed statistics on energy consumption within the dormitory, rankings of the community members, and individualized pages with positive or negative normative feedback messages based upon a user's consumption level. Over the course of a three week test period, the average demand level of the seven rooms was 56.53 watts, which is equivalent to an annualized cost of $84.18 and emissions of 480 pounds of CO 2. The largest consumer used 28% of the total energy, while the bottom three consumers combined used only 26% of the overall energy. The seven rooms together demanded between 300-490 watts 95% of the time.by Austin L. Oehlerking.S.B

Real-time, appliance-level electricity use feedback system: How to engage users?

Engage is a rapidly deployable, retrofit energy monitoring system developed for direct support of a novel energy use behavior investigation and a large-scale deployment in campus apartments. We describe the end-to-end system and report results related to web dashboard engagement during a year-long experiment. The objective was to determine user engagement with real-time and easily accessible information about personal energy consumption. Leveraging low-cost components, this system was designed to measure separately appliance plug load, heating and cooling, and lighting electrical load in dense-occupancy building environments. We developed and used an open source technology for measurement of plug load and developed signal processing algorithms to significantly improve measurement accuracy. We also developed proxy sensors to measure heating and cooling and lighting. Our results indicate that 90% of the dashboard activity was undertaken by 50% of the participants and that website engagement was more likely in mid-day and more effective in combination with email reminders. Energy conservation was achieved when combining the dashboard with public information about energy consumption. © 2013 Elsevier B.V. All rights reserved