Advertisement

Building Simulation

, Volume 7, Issue 4, pp 321–334 | Cite as

Web application for thermal comfort visualization and calculation according to ASHRAE Standard 55

  • Stefano Schiavon
  • Tyler Hoyt
  • Alberto Piccioli
Research Article Building Thermal, Lighting, and Acoustics Modeling

Abstract

Thermal comfort is one of the fundamental aspects of indoor environmental quality and it is strongly related to occupant satisfaction and energy use in buildings. This paper describes a new web application for thermal comfort visualization and calculation according to ASHRAE Standard 55-2013. Compared to existing software, the web application is free, cross-platform, and provides a visual and highly interactive accurate representation of the comfort zone. Its main features are: dynamic visualization of the comfort zone on psychrometric, temperature-relative humidity, and adaptive charts; new implementation of the Elevated Air Speed model; local thermal discomfort assessment; compliance document automation for LEED thermal comfort credits; metabolic activity and clothing insulation tables and dynamic models; and compliance with the standard. The tool can be used by architects, engineers, building operators, educators, and students.

Keywords

thermal comfort predictive mean vote/predicted percentage of dissatisfied (PMV/PPD) adaptive comfort psychrometric chart visualization web application 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ANSI/ASHRAE (2013). ANSI/ASHRAE 55-2013: Thermal Environmental Conditions for Human Occupancy. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  2. Arens EA, Turner S, Zhang H, Paliaga G (2009). Moving air for comfort. ASHRAE Journal, 51(5): 18–29.Google Scholar
  3. ASHRAE (2009). ASHRAE 2009 Handbook of Fundamentals. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  4. Bostock M (2012). d3.js. Available: http://d3js.org/ Google Scholar
  5. CEN (2007). EN 15251-2007, Criteria for the Indoor Environment Including Thermal, Indoor Air Quality, Light and Noise. European Committee for Standardization.Google Scholar
  6. de Dear RJ, Brager GS (1998). Developing an adaptive model of thermal comfort and preference. ASHRAE Transaction, 104(1): 145–167.Google Scholar
  7. Fanger PO (1970). Thermal Comfort. Copenhagen: Danish Technical Press.Google Scholar
  8. Fobelets A, Gagge AP (1988). Rationalization of the effective temperature ET* as a measure of the enthalpy of the human environment. ASHRAE Transactions, 94(1): 12–31.Google Scholar
  9. Fountain ME, Huizenga C (1997). A thermal sensation prediction tool for use by the profession. ASHRAE Transactions, 103(2): 130–136.Google Scholar
  10. Frontczak M, Schiavon S, Goins J, Arens EA, Zhang H, Wargocki P (2012). Quantitative relationships between occupant satisfaction and satisfaction aspects of indoor environmental quality and building design. Indoor Air, 22: 119–131.CrossRefGoogle Scholar
  11. Gagge AP, Fobelets A, Berglund LG (1986). A standard predictive index of human response to the thermal environment. ASHRAE Transactions, 92(2): 709–731.Google Scholar
  12. Givoni B (1992). Comfort, climate analysis and building design guidelines. Energy and Buildings, 18: 11–23.CrossRefGoogle Scholar
  13. Givoni B (1998). Climate Considerations in Building and Urban Design. New York: Van Nostrand Reinhold.Google Scholar
  14. Houghton FC, Yaglou CP (1923). Determining equal comfort lines. Journal of American Society of Heating and Ventilating Engineers, 29: 165–176.Google Scholar
  15. Hoyt T, Lee KH, Zhang H, Arens EA, Webster T (2009). Energy savings from extended air temperature setpoints and reductions in room air mixing. In: Proceedings of International Conference on Environmental Ergonomics, Boston, USA.Google Scholar
  16. Huizenga C (2010). ASHRAE Thermal Comfort Tool, Version 2. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  17. ISO (1998). ISO 7726:98, Ergonomics of the Thermal Environment—Instruments for Measuring Physical Quantities. Geneva: International Organization for Standardization.Google Scholar
  18. ISO (2005). ISO 7730:2005, 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. Geneva: International Organization for Standardization.Google Scholar
  19. Lee KH, Schiavon S (2013). Influence of three dynamic predictive clothing insulation models on building energy use, HVAC sizing and thermal comfort. Center for the Built Environment, University of California Berkeley report. Available: http://escholarship.org/uc/item/3sx6n876.Google Scholar
  20. Lomas KJ, Fiala D, Cook MJ, Cropper PC (2004). Building bioclimatic charts for non-domestic buildings and passive downdraught evaporative cooling. Building and Environment, 39: 661–676.CrossRefGoogle Scholar
  21. Mendell MJ, Mirer AG (2009). Indoor thermal factors and symptoms in office workers: Findings from the US EPA BASE study. Indoor Air, 19: 291–302.CrossRefGoogle Scholar
  22. Milne M, Liggett R, Benson A, Bhattacharya Y (2009). Climate Consultant 4.0 develops design guidelines for each unique climate. Paper presented at American Solar Energy Society Meeting.Google Scholar
  23. Olgyay V (1963). Design with Climate: A Bioclimatic Approach to Architectural Regionalism. Princeton, USA: Princeton University Press.Google Scholar
  24. Schiavon S, Lee KH (2012). Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Building and Environment, 59, 250–260.CrossRefGoogle Scholar
  25. Schiavon S, Melikov AK (2008). Energy saving and improved comfort by increased air movement. Energy and Buildings, 40: 1954–1960.CrossRefGoogle Scholar
  26. Steinfeld K, Schiavon S, Moon D (2012). Open graphic evaluative frameworks: A climate analysis tool based on an open web-based weather data visualization platform. In: Proceedings of 30th International eCAADe Conference-Digital Physicality / Physical Digitality.Google Scholar
  27. The jQuery Foundation (2013a). jQuery.Google Scholar
  28. The jQuery Foundation (2013b). jQuery UI.Google Scholar
  29. U.S. Department of Energy (2011). Report on the First Quadrennial Technology Review.Google Scholar
  30. U.S. Green Building Council (2007). LEED Reference Guide for New Construction & Major Renovations (LEED-NC), Version 2.2, US Green Building Council.Google Scholar
  31. U.S. Green Building Council (2013). LEED—Leadership in Energy and Environmental Design.Google Scholar
  32. Zhang H, Arens EA, Kim D, Buchberger E, Bauman FS, Huizenga C (2010). Comfort, perceived air quality, and work performance in a low-power task-ambient conditioning system. Building and Environment, 45: 29–39.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefano Schiavon
    • 1
  • Tyler Hoyt
    • 1
  • Alberto Piccioli
    • 1
  1. 1.Center for the Built EnvironmentUniversity of California BerkeleyBerkeleyUSA

Personalised recommendations