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Real-time multi-factor thermal comfort assessment

PUBLISHED April 15, 2023 (DOI: https://doi.org/10.54985/peeref.2304p8708798)

NOT PEER REVIEWED
3rd Place Peeref Competition

Authors

Georgia Tzitziou1 , Christos Tzouvaras2 , Asimina Dimara1 , Alexios Papaioannou1 , Stelios Krinidis1 , Konstantinos Arvanitis2 , Christos-Nikolaos Anagnostopoulos3 , Dimosthenis Ioannidis1 , Dimitrios Tzovaras1
  1. Centre for Research & Technology, Hellas - CERTH, Thessaloniki, Greece
  2. Watt + Volt, Thessaloniki, Greece
  3. University of the Aegean, Mytilene, Greece

Conference / event

IFIP International Conference on Artificial Inteligence Applications & Innovations, June 2023 (Leon, Spain)

Poster summary

The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are commonly used metrics for thermal comfort estimation, nevertheless estimating clothing insulation and the metabolic rate remains a challenge. The main objective is to mitigate the error in estimating personal factors by taking into account as many factors as possible, such as indoor and outdoor conditions. To address this issue, an algorithm that combines existing approaches to enhance precision is proposed. Experimental results demonstrate that the suggested method is more accurate than other approaches. The proposed approach has significant implications for designing and evaluating heating, ventilation, and air conditioning systems, buildings, and indoor spaces.

Keywords

Thermal comfort, Indoor environmental conditions, Personal factors, PMV, PPD

Research areas

Environmental Engineering, Energy Engineering

References

  1. Sansaniwal, Sunil Kumar, Jyotirmay Mathur, and Sanjay Mathur. "Review of practices for human thermal comfort in buildings: present and future perspectives." International Journal of Ambient Energy 43.1 (2022): 2097-2123.
  2. ISO, ISO7730. "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." Management 3.605 (2005): e615.
  3. OpenAI. "Thermal Comfort Models." OpenAI, openai.com/thermal-comfort-models/. 2023.
  4. Lourenço Niza, Iasmin, and Evandro Eduardo Broday. "Development of Thermal Comfort Models over the past years: a systematic literature Review." International Journal of Ambient Energy 43.1 (2022): 8830-8846.
  5. ANSI and ASHRAE, Thermal Environmental Conditions for Human Occupancy. Atlanta, 2020.
  6. Tartarini, Federico, and Stefano Schiavon. “Pythermalcomfort: A Python Package for Thermal Comfort Research.” SoftwareX, vol. 12, Elsevier BV, July 2020, p. 100578. Crossref, doi:10.1016/j.softx.2020.100578.
  7. Dimara, Asimina, et al. "A dynamic convergence algorithm for thermal comfort modelling." Computer Vision Systems: 12th International Conference, ICVS 2019, Thessaloniki, Greece, September 23–25, 2019, Proceedings 12. Springer International Publishing, 2019.
  8. Harputlugil, Timuçin, and Pieter de Wilde. "The interaction between humans and buildings for energy efficiency: A critical review." Energy Research & Social Science 71 (2021): 101828.
  9. Dimara, Asimina, et al. "NRG4-U: a novel home energy management system for a unique loadprofile." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44.1 (2022): 353-378.
  10. Dimara, Asimina, et al. "Optimal comfort conditions in residential houses." 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2020.

Funding

No data provided

Supplemental files

  1. Video poster   View

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.
Creative Commons license
Copyright © 2023 Tzitziou et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Tzitziou, G., Tzouvaras, C., Dimara, A., Papaioannou, A., Krinidis, S., Arvanitis, K., Anagnostopoulos, C., Ioannidis, D., Tzovaras, D. Real-time multi-factor thermal comfort assessment [not peer reviewed]. Peeref 2023 (poster).
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