Open Access
MATEC Web Conf.
Volume 66, 2016
The 4th International Building Control Conference 2016 (IBCC 2016)
Article Number 00086
Number of page(s) 6
Published online 13 July 2016
  1. V. De Giuli, O. Da Pos, and M. De Carli, Indoor environmental quality and pupil perception in Italian primary schools. Building and Environment, 2012. 56: p. 335–345. [Google Scholar]
  2. B.W. Olesen, Revision of EN 15251: indoor environmental criteria. REHVA European HVAC Journal, 2012. 49(4): p. 6–12. [Google Scholar]
  3. G. Clausen and D.P. Wyon, The combined effects of many different indoor environmental factors on acceptability and office work performance. HVAC&R Research, 2008. 14(1): p. 103–113. [CrossRef] [Google Scholar]
  4. A. Lai, et al., An evaluation model for indoor environmental quality (IEQ) acceptance in residential buildings. Energy and Buildings, 2009. 41(9): p. 930–936. [CrossRef] [Google Scholar]
  5. A. Standard, 55: Thermal Environmental Conditions for Human Occupancy American Society of Heating. Refrigeration and Air Conditioning Engineers, Atlanta, USA, 1992. [Google Scholar]
  6. C. Iso, Ergonomics of the thermal environment-Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. 2005, Brussels. European committee for standardization. [Google Scholar]
  7. I. Asadi, I. Hussein, and K. Palanisamy. Analysis on Thermal Comfort of Air-Conditioned Buildings in Malaysia: Case Study of Universiti Tenaga Nasional. in Applied Mechanics and Materials. 2014. Trans Tech Publ. [Google Scholar]
  8. G. Havenith, Heat balance when wearing protective clothing. Annals of Occupational Hygiene, 1999. 43(5): p. 289–296. [CrossRef] [Google Scholar]
  9. B. Cao, et al., Development of a multivariate regression model for overall satisfaction in public buildings based on field studies in Beijing and Shanghai. Building and Environment, 2012. 47: p. 394–399. [Google Scholar]
  10. L. Wong, K. Mui, and P. Hui, A multivariate-logistic model for acceptance of indoor environmental quality (IEQ) in offices. Building and Environment, 2008. 43(1): p. 1–6. [CrossRef] [Google Scholar]
  11. I. Asadi and I. Hussein, Indoor Air Quality (IAQ) Acceptance in Universiti Tenaga National. [Google Scholar]
  12. S. Hygge and I. Knez, Effects of noise, heat and indoor lighting on cognitive performance and self-reported affect. Journal of Environmental Psychology, 2001. 21(3): p. 291–299. [CrossRef] [Google Scholar]
  13. R. Visser, Verlichting en interieur. 1992: Dekker/vd Bos & Partners. [Google Scholar]
  14. P. Hoes, et al., User behavior in whole building simulation. Energy and buildings, 2009. 41(3): p. 295–302. [Google Scholar]
  15. G.S. Brager and R.J. de Dear, Thermal adaptation in the built environment: a literature review. Energy and buildings, 1998. 27(1): p. 83–96. [CrossRef] [Google Scholar]
  16. D. Bourgeois, C. Reinhart, and I. Macdonald, 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, 2006. 38(7): p. 814–823. [CrossRef] [Google Scholar]
  17. H.B. Rijal, et al., Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 2007. 39(7): p. 823–836. [CrossRef] [Google Scholar]
  18. Y.S. Lee, Y.K. Yi, and A. Malkawi. Simulating human behaviour and its impact on energy uses. in 12th Conference of International Building Performance Simulation Association, Sydney Austrialia. 2011. [Google Scholar]
  19. A. Leaman and B. Bordass, Are users more tolerant of ‘green’buildings? Building Research & Information, 2007. 35(6): p. 662–673. [Google Scholar]
  20. C. Simonson, M. Salonvaara, and T. Ojanen, The effect of structures on indoor humidity–possibility to improve comfort and perceived air quality. Indoor Air, 2002. 12(4): p. 243–251. [CrossRef] [Google Scholar]
  21. J. Hensen and R. Lamberts, Introduction to building performance simulation. Building performance simulation for design and operation, 2011: p. 1–14. [Google Scholar]
  22. R. Loonen, M. Loomans, and J.L. Hensen, Towards predicting the satisfaction with indoor environmental quality in building performance simulation. 2015. [Google Scholar]
  23. Z. Zhang and Q. Chen, Prediction of particle deposition onto indoor surfaces by CFD with a modified Lagrangian method. Atmospheric Environment, 2009. 43(2): p. 319–328. [CrossRef] [Google Scholar]
  24. M. Vorländer, Computer simulations in room acoustics: Concepts and uncertaintiesa. The Journal of the Acoustical Society of America, 2013. 133(3): p. 1203–1213. [CrossRef] [Google Scholar]
  25. S. Citherlet, J. Clarke, and J. Hand, Integration in building physics simulation. Energy and Buildings, 2001. 33(5): p. 451–461. [CrossRef] [Google Scholar]
  26. A. Kashif, et al., Simulating the dynamics of occupant behaviour for power management in residential buildings. Energy and Buildings, 2013. 56: p. 85–93. [CrossRef] [Google Scholar]
  27. J. Anderson and C. Lebiere, The atomic components of thought Lawrence Erlbaum. Mathway, NJ, 1998. [Google Scholar]
  28. J. Langevin, Human Behavior & Low Energy Architecture: Linking Environmental Adaptation, Personal Comfort, & Energy Use in the Built Environment. 2014. [Google Scholar]
  29. M.A. Just, P.A. Carpenter, and S. Varma, Computational modeling of high-level cognition and brain function. Human Brain Mapping, 1999. 8: p. 128–136. [CrossRef] [Google Scholar]
  30. W. Zachary, J.M. Ryder, and J.H. Hicinbothom, Cognitive task analysis and modeling of decision making in complex environments. Making decisions under stress: Implications for individual and team training, 1998: p. 315–344. [CrossRef] [Google Scholar]
  31. S.K. Card, A. Newell, and T.P. Moran, The psychology of human-computer interaction. 1983. [Google Scholar]
  32. R.G. Eggleston, M.J. Young, and K.L. McCreight. Distributed cognition: A new type of human performance model. in Proceedings of the aiaa fall symposium series technical reports. North Falmouth, Massachusetts: American Institute of Aeronautics and Astronautics.(Cité page 41.). 2000. [Google Scholar]
  33. D.E. Kieras and D.E. Meyer. Predicting human performance in dual-task tracking and decision making with computational models using the EPIC architecture. in Proceedings of the First International Symposium on Command and Control Research and Technology, National Defense University, June. Washington, DC: National Defense University. 1995. [Google Scholar]
  34. M.A. Freed, Simulating human performance in complex, dynamic environments. 1998, Northwestern University. [Google Scholar]
  35. M. Sierhuis, W.J. Clancey, and R.J. Van Hoof, Brahms: a multi-agent modelling environment for simulating work processes and practices International Journal of Simulation and Process Modelling, 2007. 3(3): p. 134–152. [CrossRef] [Google Scholar]
  36. R.J.,WherryJr, The human operator simulator—HOS, in Monitoring behavior and supervisory control. 1976, Springer. p. 283–293. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.