Radial Basis Functions
Introduction to Machine Learning
Introduction to Machine Learning
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
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
Real-time occupancy detection using decision trees with multiple sensor types
Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design
Measuring building occupancy using existing network infrastructure
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
Estimation of building occupancy levels through environmental signals deconvolution
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Occupancy inferencing from non-intrusive data sources
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Heating, ventilation, and air conditioning (HVAC) is a major energy consumer in buildings, and implementing demand driven HVAC operations is a way to reduce HVAC related energy consumption. This relies on the availability of occupancy information, which determines peak/off-hour modes that impact cooling/heating loads of HVAC systems. This research proposes an occupancy estimation model that is built on a combination of non-intrusive sensors that can detect indoor temperature, humidity, CO2 concentration, light, sound and motion. Sensor data is processed in real time using a radial basis function (RBF) neural network to estimate the number of occupants. Field tests carried out in two shared lab spaces for 20 consecutive days report an overall detection rate of 87.62% for self-estimation and 64.83% for cross-estimation. The results indicate the ability of the proposed system to monitor the occupancy information of multi-occupancy spaces in real time, supporting demand driven HVAC operations.