Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
SCOPES: Smart Cameras Object Position Estimation System
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
Energy efficient building environment control strategies using real-time occupancy measurements
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
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
Occupancy based demand response HVAC control strategy
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
A framework for extensible languages
Proceedings of the 12th international conference on Generative programming: concepts & experiences
EnergyTrack: Sensor-Driven Energy Use Analysis System
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
ThermoSense: Occupancy Thermal Based Sensing for HVAC Control
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
ZonePAC: Zonal Power Estimation and Control via HVAC Metering and Occupant Feedback
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
TOSS: Thermal Occupancy Sensing System
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Buildings account for 40% of US primary energy consumption and 72% of electricity. Of this total, 50% of the energy consumed in buildings is used for Heating Ventilation and Air-Conditioning (HVAC) systems. Current HVAC systems only condition based on static schedules; rooms are conditioned regardless of occupancy. By conditioning rooms only when necessary, greater efficiency can be achieved. This paper describes POEM, a complete closed-loop system for optimally controlling HVAC systems in buildings based on actual occupancy levels. POEM is comprised of multiple parts. A wireless network of cameras called OPTNet is developed that functions as an optical turnstile to measure area/zone occupancies. Another wireless sensor network of passive infrared (PIR) sensors called BONet functions alongside OPTNet. This sensed occupancy data from both systems are then fused with an occupancy prediction model using a particle filter in order to determine the most accurate current occupancy in each zone in the building. Finally, the information from occupancy prediction models and current occupancy is combined in order to find the optimal conditioning strategy required to reach target temperatures and minimize ventilation requirements. Based on live tests of the system, we estimate ~30.0% energy saving can be achieved while still maintaining thermal comfort.