Occupants Behavior Research Bibliography

Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
K. Pathak, Birk, A., Poppinga, J., and Schwertfeger, S., 3D forward sensor modeling and application to occupancy grid based sensor fusion, Proceedings of the 2007 IEEE/RSJ International
Conference on Intelligent Robots and Systems
. IEEE, San Diego, CA, pp. 2059-2064, 2007.
D. Parker, Mills, E., Rainer, L., Bourassa, N., and Homan, G., Accuracy of the Home Energy Saver Energy Calculation Methodology. , in ACEEE Summer Study on Energy Efficiency in Buildings, 2012.
D. M. Rowe, Activity rates and thermal comfort of office occupants in Sydney, Journal of Thermal Biology , vol. 26, pp. 415-418, 2001.
F. Haldi and Robinson, D., Adaptive actions on shading devices in response to local visual stimuli, Journal of Building Performance Simulation, vol. 3, pp. 135-153, 2010.
B. H. Gunay, O'Brien, W., Beausoleil-Morrison, I., and Huchuk, B., On adaptive occupant-learning window blind and lighting controls, Building Research & Information, vol. 7, no. 6, pp. 739-756, 2014.
J. F. Nicol and Humphreys, M. A., Adaptive thermal comfort and sustainable thermal standards for buildings, Energy and Buildings, vol. 34, pp. 563-572, 2002.
M. Indraganti, Adaptive use of natural ventilation for thermal comfort in Indian apartments, Building and Environment, vol. 45, pp. 1490-1507, 2010.
J. A. Jakubiec and Reinhart, C. F., The 'adaptive zone' - A concept for assessing discomfort glare throughout daylit spaces, Lighting Research & Technology, vol. 44, pp. 149-170, 2012.
D. Bourgeois, Reinhart, C., and Macdonald, I., Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control, Energy and Buildings, vol. 38, pp. 814-823, 2006.
T. Hong, Taylor-Lange, S. C., D’Oca, S., Yan, D., and Corgnati, S. P., Advances in Research and Applications of Energy-Related Occupant Behavior in Buildings, Energy and Buildings, vol. 116, pp. 694-702, 2016.
C. H. Putra, Andrews, C. J., and Senick, J. A., An agent-based model of building occupant behavior during load shedding, Building Simulation, 2017.
E. Azar and Menassa, C. C., Agent-based modeling of occupants and their impact on energy use in commercial buildings., Journal of Computing in Civil Engineering, vol. 26, pp. 506-518, 2011.
Y. Chen, Hong, T., and Luo, X., An agent-based stochastic Occupancy Simulator, Building Simulation, pp. 1-13, 2017.
H. Zhang, Arens, E., Fard, S. A., Huizenga, C., Paliaga, G., Brager, G., and Zagreus, L., Air movement preferences observed in office buildings, International journal of biometeorology, vol. 51, pp. 349-60, 2007.
A. P. Patton, Calderon, L., Xiong, Y., Wang, Z., Senick, J., Allacci, M. A. Sorensen, Plotnik, D., Wener, R., Andrews, C. J., Krogmann, U., and Mainelis, G., Airborne Particulate Matter in Two Multi-Family Green Buildings: Concentrations and Effect of Ventilation and Occupant Behavior, International Journal of Environmental Research and Public Health, vol. 13, no. 1, p. 144, 2016.
X. Ren, Yan, D., and Wang, C., Air-conditioning usage conditional probability model for residential buildings, Building and Environment, vol. 81, pp. 172-182, 2014.
M. A. López-Rodríguez, Santiago, I., Trillo-Montero, D., Torriti, J., and Moreno-Munoz, A., Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption, Energy Policy, vol. 62, pp. 742-751, 2013.
A. J. Ruiz, Blunck, H., Prentow, T. S., Stisen, A., and Kjærgaard, M. B., Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning., in PerCom, 2014.
S. Wei, Xu, C., Pan, S., Su, J., Wang, Y., Luo, X., Hassan, T., Firth, S., Fouchal, F., Jones, R., and de Wilde, P., Analysis of factors influencing the modelling of occupant window opening behaviour in an office building in Beijing, China, in Building Simulation Conference, Hyderabad, India, 2015.
B. Von Neida, Manicria, D., and Tweed, A., An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems, Journal of the Illuminating Engineering Society, vol. 30, pp. 111-125, 2001.
F. Rubinstein, Colak, N., Jennings, J., and Neils, D., Analyzing occupancy profiles from a lighting controls field study, Lawrence Berkeley National Laboratory, 2003.
E. S. Lee and DiBartolomeo, D. L., Application issues for large-area electrochromic windows in commercial buildings, Solar Energy Materials and Solar Cells, vol. 71, pp. 465-491, 2002.
M. Kordjamshidi, Application of fuzzy technique to integrate multiple occupancy scenarios into house rating schemes (HRS), Energy and Buildings, vol. 67, pp. 463-470, 2013.
B. Dong, Cao, C., and Lee, S. E., Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings, vol. 37, pp. 545-553, 2005.
K. J. Lomas, Architectural design of an advanced naturally ventilated building form., Energy and Buildings, vol. 39, pp. 166-181, 2007.
A. Leaman and Bordass, B., Are users more tolerant of 'green' buildings?, Building Research and Information, vol. 35, pp. 662-673, 2007.
J. A. Veitch and Gifford, R., Assessing beliefs about lighting effects on health, performance, mood, and social behavior, Environment and Behavior, vol. 28, pp. 446-470, 1996.
B. Bordass, Cohen, R., Standeven, M., and Leaman, A., Assessing building performance in use 2: technical performance of the Probe buildings, Building Research and Information, vol. 29, pp. 103-113, 2001.
A. Leaman and Bordass, B., Assessing building performance in use 4: the Probe occupant surveys and their implications, Building Research & Information, vol. 29, pp. 129-143, 2001.
D. Daum and Morel, N., Assessing the total energy impact of manual and optimized blind control in combination with different lighting schedules in a building simulation environment, Journal of Building Performance Simulation, vol. 3, pp. 1-16, 2010.
J. Weisenberg, Cuddihy, P., and Rajiv, V., Augmenting motion sensing to improve detection of periods of unusual inactivity, Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments. ACM, p. 2, 2008.
S. Y. Koo, Yeo, M. S., and Kim, K. W., Automated blind control to maximize the benefits of daylight in buildings, Building and Environment, vol. 45, pp. 1508-1520, 2010.
A. Roetzel, Tsangrassoulis, A., Dietrich, U., and Busching, S., Balancing buildings and occupants - a holistic approach to thermal comfort and greenhouse gas emissions in mixed mode offices, in Adapting to Change: New Thinking on Comfort, Windsor, UK, 2010.
T. Sherwood, Perelman, E., and Calder, B., Basic block distribution analysis to find periodic behavior and simulation points in applications, Parallel Architectures and Compilation Techniques, 2001. Proceedings. 2001 International Conference on. pp. 3-14, 2001.
D. Lindeloef and Morel, N., Bayesian estimation of visual discomfort, Building Research and Information, vol. 36, pp. 83-96, 2008.
C. Coué, Pradalier, C., Laugier, C., Fraichard, T., and Bessière, P., Bayesian occupancy filtering for multitarget tracking: an automotive application, The International Journal of Robotics Research, vol. 25, pp. 19-30, 2006.
M. Taylor, Grant, T., Knoefel, F., and Goubran, R., Bed occupancy measurements using under mattress pressure sensors for long term monitoring of community-dwelling older adults, Medical Measurements and Applications Proceedings (MeMeA), 2013 IEEE International Symposium on. pp. 130-134, 2013.
E. Arens, Behavior and Buildings, Newsletter of the Center for the Built Environment at the University of California, Berkeley. Center for the Built Environment, Berkeley, CA, 2010.
F. Haldi and Robinson, D., On the behaviour and adaptation of office occupants, Building and Environment, vol. 43, pp. 2163-2177, 2008.
N. Baker and Standeven, M., A behavioural approach to thermal comfort assessment, International Journal of Solar Energy, vol. 19, pp. 21-35, 1997.
S. Hackel and Schuetter, S., Best practices for commissioning automatic daylighting controls, Ashrae Journal, vol. 55, p. 46-+, 2013.
R. Gulbinas, Jain, R., and Taylor, J., BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy, Applied Energy, vol. 136, pp. 1076–1084, 2014.
B. Dong and Lam, K. P., Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network, Journal of Building Performance Simulation, vol. 4, pp. 359-369, 2011.
S. Ahuja and Peles, S., Building energy models: Quantifying uncertainties due to stochastic processes., 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). pp. 4814-4820, 2013.
R. H. Dodier, Henze, G. P., Tiller, D. K., and Guo, X., Building occupancy detection through sensor belief networks, Energy and Buildings, vol. 38, pp. 1033-1043, 2006.
M. Langerwisch and Wagner, B., Building variable resolution occupancy maps assuming unknown but bounded sensor errors, Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. pp. 4687-4693, 2013.
K. B. Janda, Buildings don't use energy: people do, Architectural Science Review, vol. 54, 2011.
G. R. Newsham and Arsenault, C., A camera as a sensor for lighting and shading control, Lighting Research & Technology, vol. 41, pp. 143-163, 2009.
R. P. Leslie, Capturing the daylight dividend in buildings: why and how?, Building and Environment, vol. 38, pp. 381-385, 2003.