Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
Machine Learning
SCOPES: Smart Cameras Object Position Estimation System
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
The BehaviorScope framework for enabling ambient assisted living
Personal and Ubiquitous Computing
The smart thermostat: using occupancy sensors to save energy in homes
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
POEM: power-efficient occupancy-based energy management system
Proceedings of the 12th international conference on Information processing in sensor networks
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In order to achieve sustainability, steps must be taken to reduce energy consumption. In particular, heating, cooling, and ventilation systems, which account for 42% of the energy consumed by US buildings in 2010 [8], must be made more efficient. In this paper, we demonstrate ThermoSense, a new system for estimating occupancy. Using this system we are able to condition rooms based on usage. Rather than fully conditioning empty or partially filled spaces, we can control ventilation based on near real-time estimates of occupancy and temperature using conditioning schedules learned from occupant usage patterns. ThermoSense uses a novel multisensor node that utilizes a low-cost, low-power thermal sensor array along with a passive infrared sensor. By using a novel processing pipeline and sensor fusion, we show that our system is able measure occupancy with a RMSE of only ≈0.35 persons. By conditioning spaces based on occupancy, we show that we can save 25% energy annually while maintaining room temperature effectiveness.