A design model for building occupancy detection using sensor fusion

Building occupancy sensing is useful for control of building services such as lighting and ventilation, enabling energy savings, whilst maintaining a comfortable environment. However, a precise and reliable measurement of occupancy still remains difficult. Existing technologies are plagued with a number of issues ranging from unreliable data, maintaining privacy, sensor drift, change of use, and short-term financial pressures, including low quality parts and insufficient commissioning. A major performance barrier is currently the fitness to purpose, or otherwise of sensing technologies used. Sensor fusion techniques offer a way to make up for this, aiming to more reliably determine occupancy using a range of different indoor climatic variables. Over the last decade, artificial intelligence (AI) techniques have found some application for building controls, and can also be applied to occupancy estimation. We describe a novel methodology for building occupancy detection using a sensor fusion model based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. The system monitors indoor climatic variables, indoor events and energy data obtained from a non-domestic building to infer occupancy patterns.

[1]  Ilkka Korhonen,et al.  Health monitoring in the home of the future. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

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

[3]  Gregory M. P. O'Hare,et al.  Evaluation of energy-efficiency in lighting systems using sensor networks , 2009, BuildSys '09.

[4]  C. Guestrin,et al.  Distributed localization of networked cameras , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[5]  H.S. Matthews,et al.  Scoping the potential of monitoring and control technologies to reduce energy use in homes , 2007, Proceedings of the 2007 IEEE International Symposium on Electronics and the Environment.

[6]  Neil Brown,et al.  Practical application of a sensor overlay system for building monitoring and commissioning. , 2012 .

[7]  Malcolm J. Cook,et al.  Evaluation of sensors for post-occupancy building monitoring. , 2010 .

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Moncef Krarti,et al.  An Overview of Artificial Intelligence-Based Methods for Building Energy Systems , 2003 .

[10]  Anastasios I. Dounis,et al.  Artificial intelligence for energy conservation in buildings , 2010 .

[11]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[12]  James Fogarty,et al.  Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition , 2006, UIST.

[13]  Neil Brown.  Image processing for overnight lighting quantification in buildings. , 2010 .

[14]  P. Smyth,et al.  Modeling Count Data from Multiple Sensors: A Building Occupancy Model , 2007, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.

[15]  Mat Santamouris.  Energy Performance of Residential Buildings: A Practical Guide for Energy Rating and Efficiency , 2004 .

[16]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[17]  Gregor P. Henze,et al.  The performance of occupancy-based lighting control systems: A review , 2010 .

[18]  Naoyuki Kubota,et al.  GP-preprocessed fuzzy inference for the energy load prediction , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[19]  Ruxu Du,et al.  Thermal comfort control based on neural network for HVAC application , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[20]  Neil Brown,et al.  Novel instrumentation for monitoring after-hours electricity consumption of electrical equipment, and some potential savings from a switch-off campaign. , 2012 .