Real-time multi-factor thermal comfort assessment

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

NOT PEER REVIEWED

3^rd 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

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Funding

No data provided

Supplemental files

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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.