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Engineering
Building and Construction

pythermalcomfort: A Python package for thermal comfort research

Profile image of Stefano SchiavonStefano Schiavon

2020, SoftwareX

https://doi.org/10.1016/J.SOFTX.2020.100578
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5 pages

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Abstract

We developed pythermalcomfort, a Python package that allows users to calculate the most common thermal comfort indices in compliance with the main thermal comfort standards. For example, pythermalcomfort can be used to calculate: whole body thermal comfort indices (e.g., Predicted Mean Vote, adaptive models, Standard Equivalent Temperature), local discomfort, clothing insulation, and psychrometric properties of air. All pythermalcomfort functions have been validated against the reference tables provided in the corresponding thermal comfort standards. We have developed documentation, examples and tutorial videos to guide users on how to use our package. pythermalcomfort allows researchers and professionals to accurately perform complex thermal comfort calculations without the need of rewriting the programming code. With Python being among the most widely utilized programming languages and pythermalcomfort being the only Python library which includes a comprehensive list of thermal comfort functions, we believe that pythermalcomfort will have a significant impact on both the research and industrial communities.

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References (26)

  1. de Dear RJ, Akimoto T, Arens EA, Brager G, Candido C, Cheong KWD, et al. Progress in thermal comfort research over the last twenty years. Indoor Air 2013;23(6):442-61. http://dx.doi.org/10.1111/ina.12046.
  2. Zhang F, de Dear R, Hancock P. Effects of moderate thermal environ- ments on cognitive performance: A multidisciplinary review. Appl Energy 2019;236(November 2018):760-77. http://dx.doi.org/10.1016/j.apenergy. 2018.12.005.
  3. Wyon D, Wargocki P. Room temperature effects on office work. In: Creating the productive workplace. Taylor and Francis; 2006, Ch. 11.
  4. Fanger PO. Thermal comfort: analysis and applications in environmental engineering. New York: McGraw-Hill; 1970.
  5. Nicol J, Humphreys M. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build 2002;34(6):563-72. http://dx.doi.org/ 10.1016/S0378-7788(02)00006-3.
  6. de Dear R, Brager G, Cooper D. Developing an adaptive model of thermal comfort and preference -Final Report on RP-884. Australia: Macquarie University; 1997.
  7. Gagge, A. P., Fobelets, A. P. and Berglund LG. A standard predic- tive index of human response to thermal environment. ASHRAE Trans 1986;(92(2B)):709-31.
  8. ANSI, ASHRAE. Standard 55 -Thermal environmental conditions for human occupancy. 2017.
  9. CEN. EN 16798-1 -Energy performance of buildings -Ventilation for buildings. Part 1: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. 2019.
  10. ISO. ISO 7730 -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.
  11. Kim J, Tartarini F, Parkinson T, Cooper P, Dear RD. Energy & Buildings Thermal comfort in a mixed-mode building : Are occupants more adaptive ?. Energy Build 2019;203:109436. http://dx.doi.org/10.1016/j.enbuild.2019. 109436.
  12. van Rossum G. Python tutorial. Tech. Rep. CS-R9526, Amsterdam: Centrum voor Wiskunde en Informatica (CWI); 1995.
  13. IEEE Spectrum. The top programming languages 2019. 2019, https://spectrum.ieee.org/computing/software/the-top-programming- languages-2019, accessed: 2020-03-04.
  14. Python Software Fundation. Pypi. 2020, https://pypi.org, accessed: 2020-03-19.
  15. Chuan-Che (Jeff) Huang. Comfort. 2018, https://pypi.org/project/comfort, accessed: 2020-03-19.
  16. Ladybug Tools. Ladybug. 2020, https://www.ladybug.tools/ladybug.html, accessed: 2020-03-19.
  17. Ladybug Tools. Ladybug-comfort. 2019, https://pypi.org/project/ladybug- comfort, accessed: 2020-03-19.
  18. Schweiker M. Comf: An R Package for Thermal Comfort Studies. R J 2016;8(2):341. http://dx.doi.org/10.32614/RJ-2016-050.
  19. Arens E, Hoyt T, Zhou X, Huang L, Zhang H, Schiavon S. Modeling the comfort effects of short-wave solar radiation indoors. Build Environ 2015;88:3-9. http://dx.doi.org/10.1016/j.buildenv.2014.09.004.
  20. Fiala D, Havenith G, Brode P, Kampmann B, Jendritzky G. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int J Biometeorol 2012;56:429-41. http://dx.doi.org/10.1007/s00484-011-0454- 1.
  21. Schiavon S, Lee KH. Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Build Environ 2013;59:250-60. http://dx.doi.org/10.1016/j.buildenv.2012.08.024.
  22. Liu S, Wang Z, Schiavon S, He Y, Luo M, Zhang H, et al. Predicted percentage dissatisfied with vertical temperature gradient. Energy Build 2020;220:110085. http://dx.doi.org/10.1016/j.enbuild.2020.110085.
  23. Liu S, Schiavon S, Kabanshi A, Nazaroff WW. Predicted percentage dissat- isfied with ankle draft. Indoor Air 2017;27(4):852-62. http://dx.doi.org/10. 1111/ina.12364.
  24. International Energy Agency. Buildings a source of enormous untapped efficiency potential. 2020, https://www.iea.org/topics/buildings, accessed: 2020-03-19.
  25. Graham LT, Parkinson T, Schiavon S. Where do we go now? Lessons learned from 20 years of CBE's Occupant Survey. CBE Report; 2020, URL https://escholarship.org/uc/item/8k20v82j.
  26. Tartarini F, Schiavon S, Cheung T, Hoyt T. CBE Thermal Comfort Tool: online tool for thermal comfort calculations and visualizations. SoftwareX 2020;12:100563. http://dx.doi.org/10.1016/j.softx.2020.100563.

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  • Computer Science
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  • Predicted Mean Vote
  • python programming language
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