Occupants Behavior Research Bibliography

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A. Mahdavi, Patterns and implications of user control actions in buildings, Indoor and Built Environment, vol. 18, pp. 440-446, 2009.
A. Mahdavi and Pröglhof, C., Toward empirically-based models of people's presence and actions in buildings , Eleventh International IBPSA Conference. Building Simulation 2009, Glasgow, Scotland, pp. 537-544, 2009.
A. Mahdavi and Tahmasebi, F., The deployment-dependence of occupancy-related models in building performance simulation, Energy and Buildings, 2015.
A. Mahdavi, Mohammadi, A., Kabir, E., and Lambeva, L., Occupants' operation of lighting and shading systems in office buildings, Journal of Building Performance Simulation, vol. 1, pp. 57-65, 2008.
A. Mahdavi, Lambeva, L., Mohammdi, A., Kabir, E., and Pröglhof, C., Two case studies on user interactions with buildings' environmental systems , BAUPHYSIK, vol. 29, pp. 72-75, 2007.
A. Mahdavi, The human dimension of building performance simulation, in Proceedings of Building Simulation 2011, 12th Conference of International Building Performance Simulation Association, Sydney, 14-16 November., 2011.
A. Mahdavi and Tahmasebi, F., Predicting people’s presence in buildings: An empirically based model performance analysis., ENERG BUILDINGS, vol. 86, pp. 349-355., 2015.
T. Maier, Krzaczek, M., and Tejchman, J., Comparison of physical performances of the ventilation systems in low-energy residential houses, Energy and Buildings, vol. 41, pp. 337-353, 2009.
A. Mainwaring, Culler, D., Polastre, J., Szewczyk, R., and Anderson, J., Wireless sensor networks for habitat monitoring, Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. ACM, pp. 88-97, 2002.
T. Malmqvist and Glaumann, M., Environmental efficiency in residential buildings – A simplified communication approach, Building and Environment, vol. 44, pp. 937-947, 2009.
D. Maniccia, Rutledge, B., Rea, M. S., and Morrow, W., Occupant use of manual lighting controls in private offices, Journal of the Illuminating Engineering Society, vol. 28, pp. 42-56, 1999.
D. Maniccia, Tweed, A., Bierman, A., and Von Neida, B., The effects of changing occupancy sensor time-out setting on energy savings, lamp cycling and maintenance costs, Journal of the Illuminating Engineering Society, vol. 30, pp. 97-110, 2001.
C. Manna, Fay, D., Brown, K. N., and Wilson, N., Learning occupancy in single person offices with mixtures of multi-lag Markov Chains, Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on. pp. 151-158, 2013.
P. Mansourifard, Jazizadeh, F., Krishnamachari, B., and Becerik-Gerber, B., Online Learning for Personalized Room-Level Thermal Control: A Multi-Armed Bandit Framework, in 5th ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings, Rome, Italy, 2013.
M. L. Marceau and Zmeureanu, R., Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings, Energy Conversion and Management, vol. 41, pp. 1389-1403, 2000.
A. J. Marszal, Heiselberg, P., Bourrelle, J. S., Musall, E., Voss, K., Sartori, I., and Napolitano, A., Zero Energy Building - A review of definitions and calculation methodologies, Energy and Buildings, vol. 43, pp. 971-979, 2011.
O. T. Masoso and Grobler, L. J., The dark side of occupants’ behaviour on building energy use, Energy and Buildings, vol. 42, pp. 173-177, 2010.
K. J. McCartney and Fergus, N. J., Developing an adaptive control algorithm for Europe, Energy and Buildings, vol. 34, pp. 623-635, 2002.
D. Mcgee, Variable selection of correlated predictors in logistic regression: Investigating the diet-heart hypothesis , 2009.
G. Mcgill, An investigation of indoor air quality in UK energy efficient homes : A case study, 2013.
A. Meier, Aragon, C., Hurwitz, B., Mujumda, D., Peffer, T., and Perry, D., How people actually use thermostats, in In: ACEEE summer study on energy efficiency, Pacific Grove, CA, 2012.
A. Meinke, Hawighorst, M., Wagner, A., Trojan, J., and Schweiker, M., Comfort-related feedforward information: occupants' choice of cooling strategy and perceived comfort, Building Research and Information, pp. 1-17, 2016.
A. Melikov, Pitchurov, G., Naydenov, K., and Langkilde, G., Field study on occupant comfort and the office thermal environment in rooms with displacement ventilation, Indoor air, vol. 15, pp. 205-14, 2005.
A. K. Melikov and Hlavaty, R., Identification of occupants’ activities in practice, Proceedings of International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings IAQVEC, vol. 1. pp. 317-324, 2007.
C. Menassa, Kamat, V., Lee, S., Azar, E., Feng, C., and Anderson, K., A Conceptual Framework to Optimize Building Energy Consumption by Coupling Distributed Energy Simulation and Occupancy Model, Journal of Computing in Civil Engineering –Special Issue on Computational Approaches to Understand and Reduce Energy Consumption in the Built Environment, ASCE, vol. 28, no. 1, pp. 50-62, 2014.
R. S. Merali and Barfoot, T. D., Occupancy grid mapping with Markov Chain Monte Carlo Gibbs sampling, Robotics and Automation (ICRA), 2013 IEEE International Conference on. pp. 3183-3189, 2013.
J. W. Moon and Han, S. - H., Thermostat strategies impact on energy consumption in residential buildings, Energy and Buildings, vol. 43, pp. 338-346, 2011.
T. Moore, Carter, D. J., and Slater, A. I., Long-term patterns of use of occupant controlled office lighting, Lighting Research and Technology, vol. 35, pp. 43-57, 2003.
C. Morgan and de Dear, R., Weather, clothing and thermal adaptation to indoor climate, Climate research, vol. 24, pp. 267-284, 2003.
N. Motegi, Piette, M. A., Watson, D. S., Kiliccote, S., and Xu, P., Introduction to commercial building control strategies and techniques for demand response, Lawrence Berkeley National Laboratory LBNL-59975, 2007.
V. Motuziene and Vilutiene, T., Modelling the Effect of the Domestic Occupancy Profiles on Predicted Energy Demand of the Energy Efficient House, Procedia Engineering, vol. 57, pp. 798-807, 2013.
D. Mumovic, Davies, M., Ridley, I., Altamirano-Medina, H., and Oreszczyn, T., A methodology for post-occupancy evaluation of ventilation rates in schools, Building Services Engineering Research and Technology, vol. 30, pp. 143-152, 2009.
M. Mysen, Berntsen, S., Nafstad, P., and Schild, P. G., Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools, Energy and Buildings, vol. 37, pp. 1234-1240, 2005.