Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
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
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
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
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
COPOLAN: non-invasive occupancy profiling for preliminary assessment of HVAC fixed timing strategies
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
the KTH open testbed for smart HVAC control
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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We address the problem of estimating the occupancy levels in rooms using the information available in standard HVAC systems. Instead of employing dedicated devices, we exploit the significant statistical correlations between the occupancy levels and the CO2 concentration, room temperature, and ventilation actuation signals in order to identify a dynamic model. The building occupancy estimation problem is formulated as a regularized deconvolution problem, where the estimated occupancy is the input that, when injected into the identified model, best explains the currently measured CO2 levels. Since occupancy levels are piecewise constant, the zero norm of occupancy is plugged into the cost function to penalize non-piecewise constant inputs. The problem then is seen as a particular case of fused-lasso estimator by relaxing the zero norm into the ℓ1 norm. We propose both online and offline estimators; the latter is shown to perform favorably compared to other data-based building occupancy estimators. Results on a real testbed show that the MSE of the proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or Support Vector Machine (SVM) estimation strategies.