Occupancy-driven energy management for smart building automation
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Occupancy based demand response HVAC control strategy
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
TinyEARS: spying on house appliances with audio sensor nodes
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Proceedings of the 5th ACM international conference on Distributed event-based system
An approach for more efficient energy consumption based on real-time situational awareness
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations
Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design
Following the electrons: methods for power management in commercial buildings
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A flexible building management framework based on wireless sensor and actuator networks
Journal of Network and Computer Applications
Energy-aware meeting scheduling algorithms for smart buildings
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Managing plug-loads for demand response within buildings
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Enabling building energy auditing using adapted occupancy models
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
The case for efficient renewable energy management in smart homes
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
POEM: power-efficient occupancy-based energy management system
Proceedings of the 12th international conference on Information processing in sensor networks
Smart air-conditioning control by wireless sensors: an online optimization approach
Proceedings of the fourth international conference on Future energy systems
SPOT: a smart personalized office thermal control system
Proceedings of the fourth international conference on Future energy systems
An opportunistic activity-sensing approach to save energy in office buildings
Proceedings of the fourth international conference on Future energy systems
Estimation of building occupancy levels through environmental signals deconvolution
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Non-Intrusive Occupancy Monitoring using Smart Meters
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Randomized Model Predictive Control for HVAC Systems
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
A study towards applying thermal inertia for energy conservation in rooms
ACM Transactions on Sensor Networks (TOSN)
International Journal of Communication Networks and Distributed Systems
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Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may allow significant energy-savings by intelligent control of the L-HVAC systems. In this paper, we report on the deployment of a wireless camera sensor network for collecting data regarding occupancy in a large multi-function building. The system estimates occupancy with an accuracy of 80%. Using data collected from this system, we construct multivariate Gaussian and agent based models for predicting user mobility patterns in buildings. Using these models, we can predict room usage thereby enabling us to control the HVAC systems in an adaptive manner. Our simulations indicate a 14% reduction in HVAC energy usage by having an optimal control strategy based on occupancy estimates and usage patterns.