Virtual occupancy sensors for real-time occupancy information in buildings

This study aims at developing a generic, feasible and low cost occupancy detection solution to provide reliable real-time occupancy information in buildings. Currently, various low cost or even free occupancy measurements are common in offices along with the popularization of information technologies. An information fusion method is proposed to integrate multiple occupancy measurements for reliable real-time occupancy information using the Bayesian belief network (BBN) algorithm. Based on this method, two types of virtual occupancy sensor are developed at room-level and working zone-level respectively. The room level virtual occupancy sensors are composed of physical occupancy sensors, chair sensor, keyboard and mouse amongst others. The working zone-level virtual occupancy sensors are developed based on real-time GPS location and Wi-Fi connection from smart device like smart phones and occupancy access information from building management systems. The developments of these two types of virtual occupancy sensors can be conducted automatically with functions of self-learning, self-performance assessment and fault detection. The performances of the developed virtual sensors are evaluated in two private office rooms. Results show that the developed virtual occupancy sensor are reliable and effective in providing real-time occupancy information. The paper also discusses application of the virtual occupancy sensors for demand driven HVAC operations.

[1]  Yun Kyu Yi,et al.  Simulating human behavior and its impact on energy uses , 2011 .

[2]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[3]  Ian Beausoleil-Morrison,et al.  A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices , 2013 .

[4]  Hélène Laurent,et al.  Towards a sensor for detecting human presence and characterizing activity , 2011 .

[5]  Jeong Tai Kim,et al.  A Field Survey of Occupancy and Air-Conditioner Use Patterns in Open Plan Offices , 2011 .

[6]  Nan Li,et al.  Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations , 2012 .

[7]  S. Lauritzen.  The EM algorithm for graphical association models with missing data , 1995 .

[8]  Shengwei Wang,et al.  An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network , 2013 .

[9]  C. Chao,et al.  Development of a dual-mode demand control ventilation strategy for indoor air quality control and energy saving , 2004 .

[10]  Carlos Duarte,et al.  Revealing occupancy patterns in an office building through the use of occupancy sensor data , 2013 .

[11]  Fu Xiao,et al.  Bayesian network based FDD strategy for variable air volume terminals , 2014 .

[12]  Stéphane Ploix,et al.  User Behavior Prediction in Energy Consumption in Housing Using Bayesian Networks , 2010, ICAISC.

[13]  Jing Shi,et al.  A comprehensive multi-factor analysis on RFID localization capability , 2011, Adv. Eng. Informatics.

[14]  Mu-Wook Pyeon,et al.  Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR , 2011 .

[15]  Andreas Wagner,et al.  Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building , 2015 .

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

[17]  Gregor P. Henze,et al.  Building occupancy detection through sensor belief networks , 2006 .

[18]  Ardeshir Mahdavi.  Patterns and Implications of User Control Actions in Buildings , 2009 .

[19]  Yongjun Sun,et al.  Development and In-situ validation of a multi-zone demand-controlled ventilation strategy using a limited number of sensors , 2012 .

[20]  Shengwei Wang,et al.  In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building , 2011 .

[21]  Gabriel Bekö,et al.  Modeling ventilation rates in bedrooms based on building characteristics and occupant behavior , 2011 .