Binalarda Metabolik Hız ve Doluluk Oranının İç Isı Kazançları Üzerindeki Parametresel Etkileri

Year 2025, Volume: 16 Issue: 3, 725 - 737

Ahmet Yüksel

https://doi.org/10.24012/dumf.1717808

Abstract

Bu çalışmada, havalandırılmayan bir bina modelinde değişen metabolik hız (MR) ve doluluk seviyelerinin iç hava sıcaklığı, iç yüzey sıcaklıkları ve iç termal kazanımlar üzerindeki etkileri araştırılmıştır. Amaç, bu parametrelerin termal dinamikleri nasıl etkilediğini değerlendirmek ve hangisinin daha baskın bir rol oynadığını belirlemektir. Havalandırma, sakinlerin termal etkilerini izole etmek ve dış etkileri ortadan kaldırmak için kasıtlı olarak hariç tutulmuştur. Sakinler tarafından üretilen bağıl nem birikmiş ve tüm senaryolarda iç ortamda bağıl nemin doygunluğa ulaşmasına neden olmuştur. Bağıl nem, durumlar arasında hiçbir değişiklik göstermediğinden, analize dahil edilmemiştir. Sonuçlar, hem MR'nin hem de doluluğun iç mekan termal tepkilerini önemli ölçüde etkilediğini göstermiştir. Her iki parametrenin daha yüksek değerleri, bağıl etkileri değişse de sıcaklıkların artmasına neden olmuştur. MR, düşük doluluk koşullarında daha güçlü bir etkiye sahipken, doluluk arttıkça etkisi azalmıştır. Örneğin, 50 sakinin olduğu senaryolarda, 50 ve 200 W/kişi MR değerleri arasındaki iç hava sıcaklığı farkı 6,7°C'ye ulaşmıştır. Tersine, artan doluluk, özellikle 200 kişiyle, genişletilmiş ısı transfer yüzey alanı nedeniyle daha düzgün toplam termal kazanımlara yol açtı. Çalışma, iç mekan termal konforu ve enerji verimliliğini modellerken hem MR'nin hem de doluluğun dikkate alınması gerektiği sonucuna vardı. Ek olarak, sonuçlar, özellikle ısı transfer yüzey alanı genişlemesindeki rolü nedeniyle, doluluğun iç mekan sıcaklık dinamikleri üzerinde daha önemli bir etkiye sahip olduğunu gösterdi. Bu bulgular, doluluk yoğunluğunun iç mekan sıcaklık profillerini şekillendirmedeki kritik rolünün altını çizdi ve enerji açısından verimli ve termal olarak konforlu bina ortamları tasarlarken metabolik aktivite seviyelerini hesaba katma ihtiyacını vurguladı.

Keywords

Bina enerji simülasyonu , iç ısı kazanımları , metabolizma hızı , doluluk seviyesi , parametrik analiz

Ethical Statement

Hazırlanan makale için etik kuruldan izin alınmasına gerek yoktur.

Supporting Institution

Hazırlanan makalede herhangi bir kişi/kurumla çıkar çatışması bulunmamaktadır.

Parametric Impacts of Metabolic Rate and Occupancy on Internal Thermal Gains in Buildings

Abstract

This study investigated the effects of varying metabolic rate (MR) and occupancy levels on indoor air temperature, interior surface temperatures, and internal thermal gains within a non-ventilated building model. The goal was to evaluate how these parameters influence thermal dynamics and determine which plays a more dominant role. Ventilation was intentionally excluded to isolate the thermal effects of occupants and eliminate external influences. The relative humidity generated by occupants accumulated, causing relative humidity to reach saturation in all scenarios. Since relative humidity showed no variation between cases, it was not included in the analysis. The results demonstrated that both MR and occupancy significantly impacted indoor thermal responses. Higher values of either parameter led to increased temperatures, though their relative influence varied. MR had a stronger effect under low-occupancy conditions, while its impact diminished as occupancy increased. For instance, in scenarios with 50 occupants, the indoor air temperature difference between MR values of 50 and 200 W/person reached 6.7oC. Conversely, increasing occupancy led to more uniform total thermal gains due to expanded heat transfer surface area, especially with 200 occupants. The study concluded that both MR and occupancy need to be considered when modeling indoor thermal comfort and energy efficiency. Additionally, the results indicated that occupancy had a more significant influence on indoor temperature dynamics, particularly due to its role in heat transfer surface area expansion. These findings underscored the critical role of occupancy density in shaping indoor temperature profiles and highlighted the need to account for metabolic activity levels when designing energy-efficient and thermally comfortable building environments.

Keywords

Building energy simulation , internal thermal gains , metabolic rate , occupancy level , parametric analysis

Ethical Statement

There is no need to obtain permission from the ethics committee for the article prepared.

Supporting Institution

There is no conflict of interest with any person / institution in the article prepared.

References

• [1] M. Taylor, N. C. Brown, and D. Rim, “Optimizing thermal comfort and energy use for learning environments,” Energy Build., vol. 248, p. 111181, 2021, doi: 10.1016/j.enbuild.2021.111181.
• [2] D. Kajjoba, R. Wesonga, J. D. Lwanyaga, H. Kasedde, P. W. Olupot, and J. B. Kirabira, “Assessment of thermal comfort and its potential for energy efficiency in low-income tropical buildings: a review,” Sustain. Energy Res., vol. 12, no. 1, 2025, doi: 10.1186/s40807-025-00169-9.
• [3] S. Bhat, “Building Thermal Comforts with Various HVAC Systems and Optimum Conditions,” Int. J. Multidiscip. Res. Sci. Eng. Technol., vol. 7, no. 9, 2024, doi: 10.15680/IJMRSET.2024.0709032.
• [4] Z. Fang, P. Zhang, P. Wu, L. Li, and Z. Zheng, “Effects of different metabolic rates on thermal comfort in a warmer thermal environment,” J. Build. Eng., vol. 80, p. 108030, 2023, doi: 10.1016/j.jobe.2023.108030.
• [5] A. B. Avci, “Machine learning-based prediction of thermal comfort: exploring building types, climate, ventilation strategies, and seasonal variations,” Build. Res. Inf., vol. 3218, pp. 1–18, 2025, doi: 10.1080/09613218.2025.2462932.
• [6] J. Lee and Y. Ham, “Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations,” Build. Res. Inf., vol. 49, no. 5, pp. 512–524, 2021, doi: 10.1080/09613218.2020.1840328.
• [7] J. Liu, I. W. Foged, and T. B. Moeslund, “Clothing Insulation Rate and Metabolic Rate Estimation for Individual Thermal Comfort Assessment in Real Life,” Sensors, vol. 22, no. 2, pp. 1–20, 2022, doi: 10.3390/s22020619.
• [8] L. Zhuang and K. Zhong, “An adaptive comfort equation of human heat balance: Based on physiological and thermosensory adaptations,” Build. Environ., vol. 277, p. 112951, 2025, doi: 10.1016/j.buildenv.2025.112951.
• [9] L. Yang et al., “Comparative analysis of indoor thermal environment characteristics and occupants’ adaptability: Insights from ASHRAE RP-884 and the Chinese thermal comfort database,” Energy Build., vol. 309, p. 114033, 2024, doi: 10.1016/j.enbuild.2024.114033.
• [10] J. Liu, I. W. Foged, and T. B. Moeslund, “Automatic estimation of clothing insulation rate and metabolic rate for dynamic thermal comfort assessment,” Pattern Anal. Appl., vol. 25, no. 3, pp. 619–634, 2022, doi: 10.1007/s10044-021-00961-5.
• [11] C. Zhang, J. Li, and Y. Chen, “Improving the energy discharging performance of a latent heat storage (LHS) unit using fractal-tree-shaped fins,” Appl. Energy, vol. 259, p. 114102, 2020, doi: 10.1016/j.apenergy.2019.114102.
• [12] J. Y. Yun, E. J. Choi, M. H. Chung, K. W. Bae, and J. W. Moon, “Performance evaluation of an occupant metabolic rate estimation algorithm using activity classification and object detection models,” Build. Environ., vol. 252, p. 111299, 2024, doi: 10.1016/j.buildenv.2024.111299.
• [13] T. Bajc, A. Kerčov, M. Gojak, M. Todorović, N. Pivac, and S. Nižetić, “A novel method for calculation of the CO2 concentration impact on correlation between thermal comfort and human body exergy consumption,” Energy Build., vol. 294, no. 2, 2023, doi: 10.1016/j.enbuild.2023.113234.
• [14] Y. Zhang, Z. Lin, Z. Zheng, S. Zhang, and Z. Fang, “A review of investigation of the metabolic rate effects on human thermal comfort,” Energy Build., vol. 315, p. 114300, 2024, doi: 10.1016/j.enbuild.2024.114300.
• [15] A. Nomoto et al., “Indirect calorimetry of metabolic rate in college-age Japanese subjects during various office activities,” Build. Environ., vol. 199, p. 107909, 2021, doi: 10.1016/j.buildenv.2021.107909.
• [16] X. Jia, S. Li, Y. Zhu, Z. Du, and B. Cao, “Development of the Universal Standard Effective Temperature for evaluating thermal comfort across different metabolic rates,” Build. Environ., vol. 250, p. 111149, 2024, doi: 10.1016/j.buildenv.2023.111149.
• [17] “ANSI/ASHRAE Standard 55-2020 : Thermal Environmental Conditions for Human Occupancy,” ASHRAE Inc., 2020, doi: ISSN 1041-2336.
• [18] A. Čulić, S. Nižetić, P. Šolić, T. Perković, and V. Čongradac, “Smart monitoring technologies for personal thermal comfort: A review,” J. Clean. Prod., vol. 312, 2021, doi: 10.1016/j.jclepro.2021.127685.
• [19] E. J. Choi, J. Y. Yun, Y. J. Choi, M. C. Seo, and J. W. Moon, “Impact of thermal control by real-time PMV using estimated occupants personal factors of metabolic rate and clothing insulation,” Energy Build., vol. 307, p. 113976, 2024, doi: 10.1016/j.enbuild.2024.113976.
• [20] J. Y. Yun, E. J. Choi, M. H. Chung, T. W. Kim, and J. W. Moon, “Development and performance evaluation of an indoor thermal environment control algorithm incorporating MET estimation model with object detection,” Build. Environ., vol. 267, no. PA, p. 112217, 2025, doi: 10.1016/j.buildenv.2024.112217.
• [21] S. Jung, J. Jeoung, M. Kong, and T. Hong, “Occupant activities and clothes detection based on semi-supervised learning for occupant-centric thermal control,” Build. Environ., vol. 267, no. PB, p. 112178, 2025, doi: 10.1016/j.buildenv.2024.112178.
• [22] P. Zhang, D. Liao, H. Yu, and H. Qiu, “Innovative Metabolic Rate Sensing Approach for Probing Human Thermal Comfort,” ACS Biomater. Sci. Eng., vol. 9, no. 11, pp. 6504–6514, Nov. 2023, doi: 10.1021/acsbiomaterials.3c01029.
• [23] H. Na, H. Choi, and T. Kim, “Metabolic rate estimation method using image deep learning,” Build. Simul., vol. 13, no. 5, pp. 1077–1093, 2020, doi: 10.1007/s12273-020-0707-1.
• [24] F. Yıldırım and H. A. Pekel, “Wearable Artificial Intelligence Approach in Physical Activity: A Systematic Litearture Review,” vol. 14, no. 1, pp. 8–17, 2025.
• [25] H. Choi, B. Jeong, J. Lee, H. Na, K. Kang, and T. Kim, “Deep-vision-based metabolic rate and clothing insulation estimation for occupant-centric control,” Build. Environ., vol. 221, p. 109345, 2022, doi: 10.1016/j.buildenv.2022.109345.
• [26] J. Zhang, J. Lu, W. Deng, P. Beccarelli, and I. Y. F. Lun, “Investigation of thermal comfort and preferred temperatures among rural elderly in Weihai, China: Considering metabolic rate effects,” J. Build. Eng., vol. 97, p. 110940, 2024, doi: 10.1016/j.jobe.2024.110940.
• [27] Y. Bai, Y. Zhang, Z. Wei, and Y. Wang, “Experimental study on human comfort responses after simulated summer commutes with double transients of temperature and metabolic rate,” Build. Environ., vol. 221, no. 13, p. 109253, 2022, doi: 10.1016/j.buildenv.2022.109253.
• [28] Y. Zhai et al., “Preferred temperatures with and without air movement during moderate exercise,” Energy Build., vol. 207, p. 109565, 2020, doi: 10.1016/j.enbuild.2019.109565.
• [29] M. Borowski, K. Zwolińska, and M. Czerwiński, “An Experimental Study of Thermal Comfort and Indoor Air Quality—A Case Study of a Hotel Building,” Energies, vol. 15, no. 6, pp. 1–18, 2022, doi: 10.3390/en15062026.
• [30] S. Ma, W. Deng, J. Lu, T. Zhou, and B. Liu, “Investigation of thermal comfort and preferred temperatures for healthcare staff in hospitals in Ningbo, China,” J. Build. Eng., vol. 80, no. May, p. 108029, 2023, doi: 10.1016/j.jobe.2023.108029.
• [31] J. Lee, S. H. Cha, T. Hong, and C. Koo, “Empirical investigation of occupant-centric thermal comfort in hotel guestrooms,” Renew. Sustain. Energy Rev., vol. 189, no. PB, p. 114046, 2024, doi: 10.1016/j.rser.2023.114046.
• [32] P. Niemann and G. Schmitz, “Impacts of occupancy on energy demand and thermal comfort for a large-sized administration building,” Build. Environ., vol. 182, no. March, p. 107027, 2020, doi: 10.1016/j.buildenv.2020.107027.
• [33] P. Aparicio-Ruiz, E. Barbadilla-Martín, J. Guadix, and J. Muñuzuri, “A field study on adaptive thermal comfort in Spanish primary classrooms during summer season,” Build. Environ., vol. 203, 2021, doi: 10.1016/j.buildenv.2021.108089.
• [34] S. Kumar, M. K. Singh, A. Mathur, and M. Košir, “Occupant’s thermal comfort expectations in naturally ventilated engineering workshop building: A case study at high metabolic rates,” Energy Build., vol. 217, 2020, doi: 10.1016/j.enbuild.2020.109970.
• [35] L. Yang, S. Zhao, S. Gao, H. Zhang, E. Arens, and Y. Zhai, “Gender differences in metabolic rates and thermal comfort in sedentary young males and females at various temperatures,” Energy Build., vol. 251, p. 111360, 2021, doi: 10.1016/j.enbuild.2021.111360.
• [36] I. Reda, R. N. AbdelMessih, M. Steit, and E. M. Mina, “Experimental assessment of thermal comfort and indoor air quality in worship places: The influence of occupancy level and period,” Int. J. Therm. Sci., vol. 179, no. May, p. 107686, 2022, doi: 10.1016/j.ijthermalsci.2022.107686.
• [37] Y. Zhang, X. Zhou, Z. Zheng, M. O. Oladokun, and Z. Fang, “Experimental investigation into the effects of different metabolic rates of body movement on thermal comfort,” Build. Environ., vol. 168, no. 106489, 2020, doi: 10.1016/j.buildenv.2019.106489.
• [38] Britannica, “https://www.britannica.com/science/Koppen-climate-classification (accessed March 22, 2022),” 2022. https://www.britannica.com/science/Koppen-climate-classification (accessed Mar. 22, 2022).
• [39] “TS 825:2024 Turkish Standard: Thermal Insulation Requirements for Buildings,” 2024.
• [40] H. Baş and T. Kazanasmaz, “Hybrid-Model Simulations to Equilibrate Energy Demand and Daylight Autonomy as a Function of Window-to-Wall Ratio and Orientation For a Perimeter Office in Izmir,” MEGARON / Yıldız Tech. Univ. Fac. Archit. E-Journal, vol. 15, no. 4, pp. 537–552, 2020, doi: 10.14744/megaron.2020.42223.
• [41] S. Hammerling, “2023-2024: ASHRAE Research Report,” ASHRAE J., vol. 66, no. 10, 2024.
• [42] L. G. Ioannou et al., “Occupational heat strain in outdoor workers: A comprehensive review and meta-analysis,” Temperature, vol. 9, no. 1, pp. 67–102, 2022, doi: 10.1080/23328940.2022.2030634.
• [43] A. Alonso, J. Llanos, R. Escandón, and J. J. Sendra, “Effects of the Covid-19 pandemic on indoor air quality and thermal comfort of primary schools in winter in a mediterranean climate,” Sustainability, vol. 13, no. 5, pp. 1–17, 2021, doi: 10.3390/su13052699.
• [44] N. Netam, S. Sanyal, and S. Bhowmick, “A Mathematical Model Featuring Time Lag and Decrement Factor to Assess Indoor Thermal Conditions in Low-income-group House,” J. Therm. Eng., vol. 6, no. 2, pp. 114–127, 2020, doi: 10.18186/THERMAL.728054.
• [45] A. Calvaresi, M. Arnesano, F. Pietroni, and G. M. Revel, “Measuring metabolic rate to improve comfort management in buildings,” Environ. Eng. Manag. J., vol. 17, no. 10, pp. 2287–2296, 2018, doi: 10.30638/eemj.2018.227.
• [46] A. Yüksel, M. Arıcı, M. Krajčík, M. Civan, and H. Karabay, “Developing a Strategy to Reduce Energy Consumption and CO2 Emissions from Underfloor Heating Systems in Mosques: A Case Study of a Typical Neighbourhood Mosque,” Energy Build., p. 112984, 2023, doi: 10.1016/j.enbuild.2023.112984.