A systematic approach to occupancy modeling in ambient sensor-rich buildings

SIMULATION

Volumn 90, Issue 8, 2014, Pages 960-977

  Yang, Zheng ^a   Becerik Gerber, Burcin ^a   Li, Nan ^b   Orosz, Michael ^c  

^a University of Southern California ^*  (United States)

^b TSINGHUA UNIVERSITY   (China)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

ambient sensing; Building energy conservation; building energy simulation; intelligent buildings; occupancy modeling

Indexed keywords

AIR CONDITIONING; ENERGY UTILIZATION; INTELLIGENT BUILDINGS; MEAN SQUARE ERROR; OFFICE BUILDINGS;

AMBIENT SENSING; BUILDING ENERGY CONSERVATION; BUILDING ENERGY SIMULATIONS; COMMERCIAL BUILDING; INEXPENSIVE ENERGY; MACHINE LEARNING TECHNIQUES; OVERALL ACCURACIES; ROOT MEAN SQUARE ERRORS;

ENERGY CONSERVATION;

EID: 84905979634     PISSN: 00375497     EISSN: 17413133     Source Type: Journal    
DOI: 10.1177/0037549713489918     Document Type: Article

References (25)

1. US Department of Energy, Building energy data book, US Department of Energy, 2011.
2. Kelly M. Energy efficiency, resilience to future climates and long-term sustainability: The role of the built environment, Philos Trans R Soc London, Ser A 2010; 368: 1083-1089.
3. Agarwal Y. Balaji B. Gupta R. et al. Occupancy-driven energy management for smart building automation. In: BuildSys’10 Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, Zurich, 2010, Switzerland pp. 1-6. Association for Computing Machinery.
4. Sun Z. Wang S. Ma Z. In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building. Build.Environ. 2011; 46: 124-133.
5. Leephakpreeda T. Thitipatanapong R. Grittiyachot T. et al. Occupancy-based control of indoor air ventilation: a theoretical and experimental study. Science Asia 2001; 27: 279-284.
6. Nielsen T.R. Drivsholm C. Energy efficient demand controlled ventilation in single family houses. Energy Build. 2010; 42: 1995-8.
7. Wang H. Jia Q. Song C. et al. Estimation of occupancy level in indoor environment based on heterogeneous information fusion. In: 49th IEEE Conference on Decision and Control (CDC). Atlanta, Georgia; December 15–17, 2010.
8. Erickson V.L. Lin Y. Kamthe A. et al. Energy efficient building environment control strategies using real-time occupancy measurements. In: BuildSys ’09 Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. Berkeley, California; November 4–6, 2009.
9. Benezeth Y. Laurent H. Emile B. et al. Towards a sensor for detecting human presence and characterizing activity. Energy Build. 2011; 43: 305-314.
10. Meyn A. Surana A. Lin Y. et al. A sensor-utility-network method for estimation of occupancy in buildings. In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference [CDC/CCC 2009 ]. Shanghai, China; December 15–18, 2009.
11. Henze G.P. Dodier R.H. Tiller D.K. et al. Building occupancy detection through sensor belief networks. Energy Build. 2006; 38: 1033-1043.
12. Hailemariam E. Goldstein R. Attar R. et al. Real-time occupancy detection using decision trees with multiple sensor types. In: SimAUD ’11 Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design. San Diego, California; April 7–10, 2013.
13. Dong B. Andrews B. Khee P.L. et al. An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build. 2010; 42: 1038-1046.
14. Melfi R. Rosenblum B. Nordman B. et al. Measuring building occupancy using existing network infrastructure. Presented at: 2011 International Green Computing Conference and Workshops (IGCC). Orlando, Florida; July 25–28, 2011.
15. Hutchins J. Ihler A. Smyth P., Modeling count data from multiple sensors: a building occupancy model. Presented at: 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007 [CAMPSAP 2007 ]. Piscataway, New Jersey; December 12–14, 2007.
16. Friedman N. Geiger D. Goldszmidt M. et al. Bayesian network classifiers. Mach. Learning 1997; 29: 131-163.
17. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings from the International Joint Conference on Artificial Intelligence. August 20–25, 1995.
18. Ben-Hur A. A user’s guide to support vector machines. In: Data mining techniques for the life sciences. New York, New York; Humana Press; 2010: 223-239.
19. Haykin S. Neural networks: a comprehensive foundation, 2nd ed., Prentice Hall, 1998.
20. Friedman N. Goldszmidt M. Lee T.J. Bayesian network classification with continuous attributes: getting the best of both discretization and parametric fitting. In: Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco, California; July 24–27, 1998.
21. Bouckaert R.R. Bayesian network classifiers in Weka for version 3-5-7. In: Artificial Intelligence Tools 11.3. 2008; 369-387.
22. Frank E. Pruning decision trees and lists [PhD dissertation]. Hamilton, New Zealand: University of Waikato, 2000.
23. Jolliffe I.T. Principal component analysis. New York: Springer-Verlag, 1986.
24. Johnson Controls Inc., Building energy efficiency and sustainability. Milwaukee, Wisconsin; 2010.
25. ABB Inc., Smart home and intelligent building control. Zurich, Switzerland; 2010.