Modeling personalized occupancy profiles for representing long term patterns by using ambient context

Building and Environment

Volumn 78, Issue , 2014, Pages 23-35

  Yang, Zheng ^a   Becerik Gerber, Burcin ^a  

^a University of Southern California   (United States)

Author keywords

Ambient context; Energy simulation; HVAC load; Long term presence; Personalized occupancy profile; Presence patterns

Indexed keywords

ARCHITECTURAL DESIGN; CLIMATE CONTROL; ENERGY UTILIZATION; HVAC; PROCESS ENGINEERING; RANDOM PROCESSES; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SURVEYS; TIME SERIES; WIRELESS SENSOR NETWORKS;

AMBIENT CONTEXT; ENERGY SIMULATION; HVAC LOAD; LONG TERM PRESENCE; PERSONALIZED OCCUPANCY PROFILE; PRESENCE PATTERNS;

PATTERN RECOGNITION;

AIR CONDITIONING; ARCHITECTURAL DESIGN; BUILDING; DATA ACQUISITION; HEATING; PATTERN RECOGNITION; SENSOR; STOCHASTICITY; VENTILATION;

EID: 84899689966     PISSN: 03601323     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.buildenv.2014.04.003     Document Type: Article

References (85)

1. Energy Information Administration Annual energy outlook 2012 with projections to 2035 2012, United States.
2. DOE Building energy data book 2011, Available at:, [accessed 07.10.13]. http://buildingsdatabook.eren.doe.gov/.
3. Trčka M., Hensen J.L. Overview of HVAC system simulation. Autom Constr 2010, 19(2):93-99.
4. Tabak V., de Vries B. Methods for the prediction of intermediate activities by office occupants. Build Environ 2010, 45(6):1366-1372.
5. Zhang Rui, Poh Lam Khee, Chiou Yun-Shang, Dong Bing Information-theoretic environment features selection for occupancy detection in open office spaces. Build Simul Springer 2012, 5(2):179-188.
6. Handbook A. HVAC systems and equipment 1996, American Society of Heating, Refrigerating, and Air Conditioning Engineers, Atlanta, GA.
7. Wang S., Ma Z. Supervisory and optimal control of building HVAC systems: a review. HVAC&R Res 2008, 14(1):3-32.
8. Yu Tina Modeling occupancy behavior for energy efficiency and occupants comfort management in intelligent buildings. Machine learning and applications (ICMLA). 2010 Ninth international conference on: IEEE 2010.
9. Rea M.S. Window blind occlusion: a pilot study. Build Environ 1984, 19(2):133-137.
10. Fritsch R., Kohler A., Nygård-Ferguson M., Scartezzini J. Astochastic model of user behaviour regarding ventilation. Build Environ 1990, 25(2):173-181.
11. Carbon Trust Building controls- realising savings through the use of controls 2012, Technical Report.
12. ABB Inc. The European standard EN 15232: a key contribution to worldwide energy efficiency 2010, Technical Report.
13. Crawley D.B., Lawrie L.K., Pedersen C.O., Winkelmann F.C. EnergyPlus: energy simulation program. ASHRAE J 2000, 42(4):49-56.
14. Birdsall B., Buhl W., Ellington K., Erdem A., Winkelmann F. Overview of the DOE-2 building energy analysis program 1990, Report LBL-19735m rev.w, Lawrence Berkeley Laboratory, Berkeley, CA.
15. ESRU ESP-r: a building and plant energy simulation environment, user guide version 9 series 1999.
16. University of Wisconsin-Madison TRNSYS, A transient system simulation program 1979, Solar Energy Laboratory, Klein SA, Solar Energy Laboratory, University of Wisconsin-Madison.
17. Yan Da, Xia Jianjun, Tang Waiyin, Song Fangting, Zhang Xiaoliang, Jiang Yi DeST-an integrated building simulation toolkit part I: Fundamentals. Build Simul Springer 2008, 1(2):95-110.
18. Azar E., Menassa C.C. Acomprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy Build 2012, 55:841-853.
19. Yang Z., Li N., Becerik-Gerber B., Orosz M. Asystematic approach to occupancy modeling in ambient sensor rich office environment. Simul Trans Soc Model Simul Int March 2013, Special Issue: Simulation for Architecture and Urban Design Accepted. 10.1177/0037549713489918.
20. Pan Y., Huang Z., Wu G. Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy Build 2007, 39(6):651-657.
21. Mohammadi Abdolazim, Kabir Elham, Mahdavi Ardeshir, Pröglhöf Claus Modeling user control of lighting and shading eevices in office buildings: an empirical case study. Proc of 10th Int Building Performance Simulation Assoc Conf and Exhib, building simulation 2007, Tsinghua. China.
22. Abushakra B., Sreshthaputra A., Haberl J., Claridge D. Compilation of diversity factors and schedules for energy and cooling load calculations 2001, American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE Research Project 1093-RP, Final Report.
23. Davis J.A., Nutter D.W. Occupancy diversity factors for common university building types. Energy Build 2010, 42(9):1543-1551.
24. ASHRAE AS Standard 90.1-2004, energy standard for buildings except low rise residential buildings 2004, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
25. U.S.Energy Information Administration Commercial buildings energy consumption survey - overview of commercial buildings characteristics 2003, Available at:, [accessed Dec, 2013]. http://www.eia.doe.gov/emeu/cbecs/cbecs2003/introduction.html.
26. Duarte C., Van Den Wymelenberg K., Rieger C. Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy Build 2013, 67:587-595.
27. Brandemuehl M.J., Braun J.E. The impact of demand-controlled and economizer ventilation strategies on energy use in buildings. Conference: ASHRAE annual meeting, Seattle 1999.
28. Erickson Varick L., Lin Yiqing, Kamthe Ankur, Brahme Rohini, Surana Amit, Cerpa Alberto E., et al. Energy efficient building environment control strategies using real-time occupancy measurements. Proceedings of the first ACM workshop on embedded sensing systems for energy-efficiency in buildings; November 04-06 2009, ACM.
29. 2 source generation rates from measurements in small commercial buildings. Build Environ 2007, 42(2):623-639.
30. Wang D., Federspiel C.C., Rubinstein F. Modeling occupancy in single person offices. Energy Build 2005, 37(2):121-126.
31. Mahdavi A. Patterns and implications of user control actions in buildings. Indoor Built Environ 2009, 18(5):440-446.
32. Mahdavi Ardeshir, Pröglhöf Claus User behaviour and energy performance in buildings. Int. energy economics workshop TUV, IEWT 2009.
33. Page J., Robinson D., Morel N., Scartezzini J. Ageneralised stochastic model for the simulation of occupant presence. Energy Build 2008, 40(2):83-98.
34. Mahdavi Ardeshir, Pröglhöf Claus Observation-based models of user control actions in building. Proceedings of PLEA-passive and low energy architecture 2008 conference; October 22-24 2008, PLEA.
35. Chang Wen-Kuei, Hong Tianzhen Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. Build Simul Springer 2013, 6(1):23-32.
36. Chao C., Hu J. Development of a dual-mode demand control ventilation strategy for indoor air quality control and energy saving. Build Environ 2004, 39(4):385-397.
37. Wang Chuang, Yan Da, Jiang Yi Anovel approach for building occupancy simulation. Build Simul Springer 2011, 4(2):149-167.
38. Newsham G., Mahdavi A., Beausoleil-Morrison I. Lightswitch: a stochastic model for predicting office lighting energy consumption. Proceeding of right light three, 3rd European conference on efficient lighting, Mewcastle, UK 1995, 59-66.
39. Reinhart C.F. Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Sol Energy 2004, 77(1):15-28.
40. Javad Akhlaghinia M., Lotfi Ahmad, Langensiepen Caroline, Sherkat Nasser Afuzzy predictor model for the occupancy prediction of an intelligent inhabited environment. Fuzzy systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE international conference on: IEEE 2008.
41. Yamaguchi Yohei, Shimoda Yoshiyuki, Mizuno Minoru Development of district energy system simulation model based on detailed energy demand model. Proceeding of eighth international IBPSA conference 2003.
42. Haldi F., Robinson D. Interactions with window openings by office occupants. Build Environ 2009, 44(12):2378-2395.
43. Gunay H.B., O'Brien W., Beausoleil-Morrison I. Acritical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices. Build Environ 2013, 70:31-47.
44. Dong B., Andrews B., Lam K.P., Höynck M., Zhang R., Chiou Y., et al. An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build 2010, 42(7):1038-1046.
45. Dong Bing, Andrews Burton Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings. The 11th international building performance simulation association conference; July 27-30 2009, IBPSA.
46. Stoppel C.M., Leite F. Integrating probabilistic methods for describing occupantpresence with building energy simulation models. Energy Build 2014, 68:99-107.
47. Karp Brad, Kung Hsiang-Tsung GPSR: greedy perimeter stateless routing for wireless networks. Proceedings of the 6th annual international conference on mobile computing and networking 2000, ACM.
48. Cook D.J., Youngblood M., Heierman E.O., Gopalratnam K., Rao S., Litvin A., et al. MavHome: an agent-based smart home. Pervasive computing and communications, 2003. (PerCom 2003). Proceedings of the first IEEE international Conference on: IEEE 2003.
49. 2-based adaptive demand-controlled ventilation strategy in a multi-zone office building. Build Environ 2011, 46(1):124-133.
50. Leephakpreeda T., Thitipatanapong R., Grittiyachot T., Yungchareon V. Occupancy-based control of indoor air ventilation: a theoretical and experimental study. Sci Asia 2001, 27(4):279-284.
51. Nielsen T.R., Drivsholm C. Energy efficient demand controlled ventilation in single family houses. Energy Build 2010, 42(11):1995-1998.
52. Wang Heng-Tao, Jia Qing-Shan, Song Chen, Yuan Ruixi, Guan Xiaohong Estimation of occupancy level in indoor environment based on heterogeneous information fusion. Decision and Control (CDC), 2010 49th IEEE conference on: IEEE 2010.
53. Benezeth Y., Laurent H., Emile B., Rosenberger C. Towards a sensor for detecting human presence and characterizing activity. Energy Build 2011, 43(2):305-314.
54. Agarwal Yuvraj, Balaji Bharathan, Gupta Rajesh, Lyles Jacob, Wei Michael, Weng Thomas Occupancy-driven energy management for smart building automation. Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building; November 03-05 2010, ACM.
55. Meyn Sean, Surana Amit, Lin Yiqing, Maris Oggianu Stella, Narayanan Satish, Frewen Thomas A. Asensor-utility-network method for estimation of occupancy in buildings. Decision and control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE conference on: IEEE 2009.
56. Henze G.P., Felsmann C., Knabe G. Evaluation of optimal control for active and passive building thermal storage. Int J Therm Sci 2004, 43(2):173-183.
57. Hailemariam Ebenezer, Goldstein Rhys, Attar Ramtin, Khan Azam Real-time occupancy detection using decision trees with multiple sensor types. Proceedings of the 2011 symposium on simulation for architecture and urban design 2011, Society for Computer Simulation International.
58. Melfi Ryan, Rosenblum Ben, Nordman Bruce, Christensen Ken Measuring building occupancy using existing network infrastructure. Green Computing Conference and workshops (IGCC), 2011 international: IEEE 2011.
59. Hutchins Jon, Ihler Alexander, Smyth Padhraic Modeling count data from multiple sensors: a building occupancy model. Computational advances in multi-sensor adaptive processing, 2007. CAMPSAP 2007. 2nd IEEE international workshop on: IEEE 2007.
60. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 2009, 11(1):10-18.
61. Frank Eibe Pruning decision trees and lists 2000, [Thesis], University of Waikato.
62. Yang Z., Li N., Becerik-Gerber B., Orosz M. Amulti-sensor based occupancy estimation model for supporting demand driven HVAC operations. Proceeding of SimAUD 12 symposium on simulation for architecture and urban design 2012.
63. Forney G. Generalized minimum distance decoding. Inform Theory IEEE Trans 1966, 12(2):125-131.
64. Eigen M., Biebricher C.K. Sequence space and quasispecies distribution. RNA Genet 1988, 3:211-245.
65. Gorcin Ali, Celebi Hasari, Qaraqe Khalid A., Arslan Hüseyin An autoregressive approach for spectrum occupancy modeling and prediction based on synchronous measurements. Personal Indoor and Mobile Radio Communications (PIMRC), 2011 IEEE 22nd international symposium on: IEEE 2011.
66. Hamilton J.D. Time series analysis 1994, Cambridge Univ Press.
67. Harvey A.C., Harvey A. Time series models 1993, Harvester Wheatsheaf.
68. Brockwell P.J., Davis R.A. Time series: theory and methods 2009, Springer.
69. Chatfield C. The analysis of time series: an introduction 2003, CRC Press.
70. Croarkin C., Tobias P., Zey C. Engineering statistics handbook 2001, National Institute of Standards and Technology (US).
71. Hannan E. Multiple time series. Wiley series in probability and mathematical statistics 1970, John Wiley and Sons, New York.
72. Whitle P. Hypothesis testing in time series analysis 1951, Almqvist & Wiksells.
73. Box G.E., Jenkins G.M., Reinsel G.C. Time series analysis: forecasting and control 2013, Wiley. com.
74. Polikar Robi Pattern recognition. Wiley encyclopedia of biomedical engieering 2006, John Wiley & Sons Inc., New York.
75. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, John Wiley & Sons, New York.
76. Yang Zheng, Li Nan, Becerik-Gerber Burcin, Orosz Michael Anon-intrusive occupancy monitoring system for demand driven HVAC operations 2012, Construction Research Congress.
77. Pinsky M., Karlin S. An introduction to stochastic modeling 2010, Academic press.
78. Meyn S.S.P., Tweedie R.L. Markov chains and stochastic stability 2009, Cambridge University Press.
79. Efron B. Better bootstrap confidence intervals. JAm Stat Assoc 1987, 82(397):171-185. JSTOR 2289144. 10.2307/2289144.
80. Aitken A.C. Statistical mathematics 1957, Oliver and Boyd Edinburgh.
81. Guglielmetti Rob, Macumber Dan, Long Nicholas OpenStudio: an open source integrated analysis platform. Proceedings of the 12th conference of international building performance simulation association 2011.
82. NREL OpenStuio: commercial buildings research and software development 2013, Available at:, [accessed 30.09.13]. http://openstudio.nrel.gov/.
83. Efficiency Valuation Organization (EVO) International Performance Measurement and Verification Protocol (IPMVP). Energy project financing: resources and strategies for success Lilburn, GA 2009, Vol. 1. The Fairmont Press, Inc.
84. Federal Energy Management Program (FEMP) M&V guidelines: measurement and verification for federal energy projects 2008, version 3.0. Prepared by Nexant, Inc.
85. Department of Energy Energy efficiency & renewable energy 2013, Available at:, [accessed Nov, 2013]. http://www.eere.energy.gov/.