Building Simulation

, Volume 5, Issue 3, pp 191–202 | Cite as

A novel personalized thermal comfort control, responding to user sensation feedbacks

  • Romain Nouvel
  • Franck Alessi
Research Article Building Systems and Components


This paper presents a novel heating, ventilation, and air conditioning (HVAC) control architecture for office buildings, which uses the predicted mean vote (PMV) index of each occupant as feedback and offers them the opportunity to act on their own comfort level by signalling a possible thermal uncomfortable sensation to a personal user interface. A co-simulation EnergyPlus/Simulink has been used to test this new personalized and adaptive thermal comfort control in an office building for different seasons, up to two employees per office. Simulation results show that such a comfort control algorithm leads to sizeable energy savings as well as comfort improvement for each occupant. Moreover, after processing the order given by the user interface, the control algorithm makes the simulated thermal sensation match the actual thermal sensation of the occupant with a high accuracy, leading to a better consideration of his thermal comfort.


thermal comfort control HVAC systems office buildings user interface co-simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Atthajariyakul S, Leephakpreeda T (2004). Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage. Energy and Buildings, 36: 720–733.CrossRefGoogle Scholar
  2. Bauman FS, Carter TG, Baughman AV, Arens EA (1998). Field study of the impact of a desktop task/ambient conditioning system in office buildings. ASHRAE Transactions, 104(1): 1153–1171.Google Scholar
  3. Bedford T (1936). The warmth factor in comfort at work: a physiological study of heating and ventilation. Industrial Health Report, 76, London.Google Scholar
  4. Brager GS, Paliaga G, de Dear R (2004). Operable windows, personal control and occupant comfort. ASHRAE Transactions, 110(2):17–35.Google Scholar
  5. Crawley DB, Hand JW, Kummert M, Griffth BT (2005). Contrasting the capabilities of building energy performance simulation programs. Joint Report of US Department Of Energy, University of Wisconsin- Madison, University of Strathcycle.Google Scholar
  6. de Dear RJ, Ring JW, Fanger PO (1993). Thermal sensations resulting from sudden ambient temperature changes. Indoor Air, 3: 181–192.CrossRefGoogle Scholar
  7. Fanger PO (1970). Thermal Comfort. Copenhagen: Danish Technical Press.Google Scholar
  8. Farzaneh Y, Tootoonchi A (2008). Controlling automobile thermal comfort using optimized fuzzy controller. Applied Thermal Engineering, 28: 1906–1917.CrossRefGoogle Scholar
  9. Federspiel C (1992). User-adaptive and minimum-power thermal comfort control. PhD Thesis, Massachusetts Institute of Technology, USA.Google Scholar
  10. Feldmeier MC (2009). Personalized building comfort control. PhD Thesis, Massachusetts Institute of Technology, USA.Google Scholar
  11. Fountain M, Arens E, de Dear R, Bauman F, Miura K (1994). Locally controlled air movement preferred in warm isothermal environments. ASHRAE Transactions, 100(2): 937–952.Google Scholar
  12. Gagge AP, Stolwijk JA, Nishi Y (1971). An effective temperature scale based on a simple model of human physiological regulatory response. ASHRAE Transactions, 77(1): 247–262.Google Scholar
  13. Humphreys MA, Nicol JF (2002). The validity of ISO-PMV for predicting comfort votes in every-day thermal environments. Energy and Buildings, 34: 667–684.CrossRefGoogle Scholar
  14. ISO (2005). 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. International Standard 7730.Google Scholar
  15. McCartney KJ, Humphreys MA (2002). Thermal comfort and productivity. In: Proceedings of the 9th International Conference on Indoor Air Quality and Climate, Monterey, CA.Google Scholar
  16. Melikov AK, Cermak R, Mayer M (2002). Personalized ventilation: evaluation of different air terminal devices. Energy and Buildings, 34: 829–836.CrossRefGoogle Scholar
  17. Stephen EA, Shnathi M, Rajalakshmy P, Parthido MM (2010). Application of fuzzy logic in control of thermal comfort. International Journal of Computational and Applied Mathematics, 5: 289–300.Google Scholar
  18. Stevens JC, Marks LE, Gagge AP (1969). The quantitative assessment of thermal discomfort. Environmental Research, 2: 149–165.CrossRefGoogle Scholar
  19. Stolwijk JAJ (1980). Mathematical model of thermoregulation. Annuals of the New York Academy of Sciences, 335: 98–106.CrossRefGoogle Scholar
  20. Struler E, Hoeflinger J, Kalé LV, Bhandarkar M (2000). A new approach to software integration frameworks for multi-physics simulation codes. In: Proceedings of the IFIP TC2/WG2.5 Working Conference on the Architecture of Scientific Software (pp. 87–104).Google Scholar
  21. Trcka M, Wetter M, Hensen JLM (2009). An implementation of co- simulation for performance prediction of innovative integrated HVAC systems in buildings. In: Proceedings of the 11th international IBPSA conference (pp. 724–731), Glasgow, UK.Google Scholar
  22. Vastamäki R, Sinkonnen I, Leinonen C (2005). A behavioural model of temperature controller usage and energy saving. Personal and Ubiquitous Computing, 9: 250–259.CrossRefGoogle Scholar
  23. Wanatabe S, Melikov AK, Knudsen G (2010). Design of an individually controlled system for an optimal thermal microenvironment. Building and Environment, 45: 549–558.CrossRefGoogle Scholar
  24. Wetter M, Haves P (2008). A modular building controls virtual bed for the integration of heterogeneous systems. Third National Conference of IBPSA-USA.Google Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Laboratoire d’Energétique du batimentCEA, INESLe Bourget du LacFrance

Personalised recommendations