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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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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