Occupancy and indoor environment quality sensing for smart buildings

This paper presents a technique to determine the occupancy and indoor environment quality (IEQ) in buildings by enhancing physical measurements from a distributed sensor network with a statistical estimation method. The research is motivated by the increasing demand for improving energy efficiency while maintaining healthy and comfortable environment in buildings. Features representing the occupancy level and the relative changes are extracted from a suite of sensors: passive infra-red (PIR) sensors, Carbon Dioxide (CO2) concentration sensors, and relative humidity (RH) sensors, which are networked and installed in a laboratory. An Autoregressive Hidden Markov Model (ARHMM) has been developed to model the occupancy pattern, based on the measurements, given its ability to establish correlations among the observed variables. The result is compared with that obtained from the classical Hidden Markov Model (HMM) and Support Vector Machines (SVM), which indicates that the ARHMM estimation method performed better than the other two methods, with an average estimation accuracy of 80.78%.

[1]  Joseph Bockhorst,et al.  Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting , 2010, NIPS.

[2]  Ki Yong Lee,et al.  Recognition of noisy speech by a nonstationary AR HMM with gain adaptation under unknown noise , 2001, IEEE Trans. Speech Audio Process..

[3]  K. Mui,et al.  Energy impact of indoor environmental policy for air-conditioned offices of Hong Kong , 2008 .

[4]  Rui Zhang,et al.  An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network , 2010 .

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  James D. Hamilton,et al.  Autoregressive conditional heteroskedasticity and changes in regime , 1994 .

[8]  Rui Zhang,et al.  Information-theoretic environmental features selection for occupancy detection in open offices , 2009 .

[9]  Shengwei Wang,et al.  Experimental Validation of CO2-Based Occupancy Detection for Demand-Controlled Ventilation , 1999 .

[10]  Martin J. McKeown,et al.  A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data , 2008, IEEE Transactions on Signal Processing.

[11]  William J. J. Roberts,et al.  Revisiting autoregressive hidden Markov modeling of speech signals , 2005, IEEE Signal Processing Letters.

[12]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[13]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..