Abstract
Thermal comfort models are mathematical representations that simulate the thermal environment and predict human comfort based on various factors such as air temperature, air velocity, relative humidity, and radiation heat transfer. These models are used to design and evaluate heating, ventilation, and air conditioning systems, buildings, and outdoor spaces. The main issue when exploiting predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) and model for thermal comfort estimation is how to estimate clothing insulation and metabolic rate as accurately as possible. In this paper, a novel approach for calculating thermal comfort is presented that combines algorithms to enhance the precision of existing approaches. Experimental results showcase the suggested method is more accurate than other approaches.
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References
Sansaniwal, S.K., Mathur, J., Mathur, S.: Review of practices for human thermal comfort in buildings: present and future perspectives. Int. J. Ambient Energy 43(1), 2097–2123 (2022)
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), e615 (2005)
OpenAI: Thermal Comfort Models. OpenAI (2023). openai.com/thermal-comfort-models/
Lourenço Niza, I., Broday, E.E.: Development of thermal comfort models over the past years: a systematic literature review. Int. J. Ambient Energy 43(1), 8830–8846 (2022)
ANSI and ASHRAE, Thermal Environmental Conditions for Human Occupancy. Atlanta (2020)
Tartarini, F., Schiavon, S.: Pythermalcomfort: a Python package for thermal comfort research. SoftwareX 12, 100578. Elsevier BV (2020). Crossref, https://doi.org/10.1016/j.softx.2020.100578
Dimara, A., Timplalexis, C., Krinidis, S., Tzovaras, D.: A dynamic convergence algorithm for thermal comfort modelling. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds.) ICVS 2019. LNCS, vol. 11754, pp. 680–689. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34995-0_62
Harputlugil, T., de Wilde, P.: The interaction between humans and buildings for energy efficiency: a critical review. Energy Res. Soc. Sci. 71, 101828 (2021)
Dimara, A., Krinidis, S., Tzovaras, D.: Comfit: a novel indoor comfort inference tool, 165–170 (2021)
Krinidis, S., et al.: Multi-criteria HVAC control optimization. In: 2018 IEEE International Energy Conference (ENERGYCON). IEEE (2018)
Peeters, L., et al.: Thermal comfort in residential buildings: comfort values and scales for building energy simulation. Appl. Energy 86)(5), 772–780 (2009)
Dimara, A., et al.: NRG4-U: a novel home energy management system for a unique loadprofile. Energy Sources Part A: Recov. Utilization Environ. Effects 44(1), 353–378 (2022)
Oğulata, R.T.: The effect of thermal insulation of clothing on human thermal comfort. Fibres Textiles Eastern Europe 15(2), 67–72 (2007)
Havenith, G., Holmér, I., Parsons, K.: Personal factors in thermal comfort assessment: clothing properties and metabolic heat production. Energy Build. 34(6), 581–591 (2002)
Luo, M., et al.: Human metabolic rate and thermal comfort in buildings: the problem and challenge. Build. Environ. 131, 44–52 (2018)
Dimara, A., et al.: Optimal comfort conditions in residential houses. In: 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE (2020)
Arendt, K.: Influence of external walls’ thermal capacitance on indoor thermal comfort (2013)
Schiavon, S., Lee, K.H.: Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Build. Environ. 59, 250–260 (2013). https://doi.org/10.1016/j.buildenv.2012.08.024
ITI Smart Home CERTH.ITI. https://smarthome.iti.gr/
Tuya ZigBee Temperature and Humidity Sensor. https://www.expert4house.com/en/smart-home/sensors-and-detectors/tuya-zigbee-temperature-and-humidity-sensor
Kwon, J.Y., Choi, J.: Clothing insulation and temperature, layer and mass of clothing under comfortable environmental conditions. J. Physiol. Anthropol. 32(1) (2013). https://doi.org/10.1186/1880-6805-32-11
Acknowledgements
This work is partially supported by the PRECEPT project funded by the EU H2020 under Grant Agreement No. 958284.
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Tzitziou, G. et al. (2023). Realtime Multi-factor Dynamic Thermal Comfort Estimation for Indoor Environments. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_17
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