How to monitor people ‘smartly’ to help reducing energy consumption in buildings?

There is a complex link between building fabric, habitant expectation and behavior, energy consumption and actual internal conditions. Home owners exert total control of their homes and how people actually use buildings is not as how we think they do. Therefore, mapping occupants' behavior, and understanding how it relates to comfort and energy consumption is essential. To address this necessity, the paper reviews techniques and systems used to monitor occupants through time and location. Furthermore, it assesses the complexity, robustness, accuracy and performance of each method. To support this review, monitored data were collected using various methods to monitor people's activities in their home. These could be supported by fix building sensors, or/and wearable sensors. Results from these studies established that systems, using ultra wide band technology and radio-frequency identification hold the highest precision and accuracy, being a non-intrusive method in everyday domestic settings. In conclusion, gathering occupancy data may lead to better and more energy-efficient control system of indoor environment.

[1]  Yaser P. Fallah,et al.  Making indoor maps with portable accelerometer and magnetometer , 2010, 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service.

[2]  Gregory D. Abowd,et al.  Living laboratories: the future computing environments group at the Georgia Institute of Technology , 2000, CHI Extended Abstracts.

[3]  Mark Gillott,et al.  Domestic energy and occupancy: a novel post-occupancy evaluation study , 2010 .

[4]  Oliver J. Woodman,et al.  An introduction to inertial navigation , 2007 .

[5]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[6]  R. Andersen,et al.  Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions , 2009 .

[7]  Ubejd Shala,et al.  Indoor Positioning using Sensor-fusion in Android Devices , 2011 .

[8]  Neal Patwari,et al.  RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms , 2010, Proceedings of the IEEE.

[9]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[10]  Stephanie Gauthier,et al.  Review of Methods to Map People’s Daily Activity – Application for Smart Homes , 2013 .

[11]  Asker E. Jeukendrup,et al.  Sport Nutrition: An Introduction to Energy Production and Performance , 2004 .

[12]  Tae-Seong Kim,et al.  Mobile Motion Sensor-Based Human Activity Recognition and Energy Expenditure Estimation in Building Environments , 2013 .

[13]  Jonathan Baron,et al.  Behavioral Research Data Analysis with R , 2011 .

[14]  Jacek Stefanski.  Hyperbolic Position Location Estimation in the Multipath Propagation Environment , 2009, WMNC/PWC.

[15]  Ahmad Lotfi,et al.  Occupancy monitoring in intelligent environment through integrated wireless localizing aggents , 2009, 2009 IEEE Symposium on Intelligent Agents.

[16]  Moustafa Youssef,et al.  Analysis of a Device-Free Passive Tracking System in Typical Wireless Environments , 2009, 2009 3rd International Conference on New Technologies, Mobility and Security.

[17]  Rune Vinther Andersen,et al.  Window opening behaviour: simulations of occupant behaviour in residential buildings using models based on a field survey , 2012 .

[18]  Mark Gillott,et al.  The use of intelligent systems for monitoring energy use and occupancy in existing homes , 2011 .

[19]  Wolfram Burgard,et al.  Learning motion patterns of persons for mobile service robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[20]  Rong Zhu,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[22]  Alex Pentland,et al.  Auditory Context Awareness via Wearable Computing , 1998 .

[23]  Catalina Spataru,et al.  To work, the Green Deal needs to take seriously the Diversity of human Behavior , 2012 .

[24]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[25]  Brenda Vale,et al.  Domestic energy use, lifestyles and POE: past lessons for current problems , 2010 .

[26]  Rick Krohn,et al.  RFID: it's about more than asset tracking. , 2005, Journal of healthcare information management : JHIM.

[27]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  V. Kamat,et al.  Indoor User Localization for Rapid Information Access and Retrieval on Construction Sites , 2008 .

[29]  H. Elkamchouchi,et al.  Direction-of-arrival methods (DOA) and time difference of arrival (TDOA) position location technique , 2005, Proceedings of the Twenty-Second National Radio Science Conference, 2005. NRSC 2005..

[30]  Zhaoying Zhou,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. , 2004, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[31]  Girijesh Prasad,et al.  Internal Localisation Techniques using Wireless Networks: A Review , 2007 .

[32]  Shahram Izadi,et al.  SenseCam: A Retrospective Memory Aid , 2006, UbiComp.