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Salamone, F., Bellazzi, A., Belussi, L., Damato, G., Danza, L., Dell’Aquila, F., Ghellere, M., Megale, V., Meroni, I., & Vitaletti, W. (2020) · Sensors · doi:10.3390/s20061627 · Cited by 56 · see citing works

Identification of climate change impact and thermal comfort zones in semi-arid regions of AP, India using LST and NDBI techniques

Badapalli, P. K., Anusha, B. N., Babu, K. R., & Sree, P. P. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137175 · Cited by 54 · see citing works

Actively and reversibly controlling thermal conductivity in solid materials

Liu, C., Wu, C. J., Zhao, Y., Chen, Z., Ren, T., Chen, Y., & Zhang, G. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.01.001 · Cited by 54 · see citing works

Thermal comfort and physiological responses with standing and treadmill workstations in summer

Yang, L., Gao, S., Zhao, S., Zhang, H., Arens, E. A., & Zhai, Y. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107238 · Cited by 51 · see citing works

An architectural framework of elderly healthcare monitoring and tracking through wearable sensor technologies

Alsadoon, A., Al‐Naymat, G., & Jerew, O. D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18177-0 · Cited by 49 · see citing works

Preferred temperature with standing and treadmill workstations

Gao, S., Zhai, Y., Yang, L., Zhang, H., & Gao, Y. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.04.027 · Cited by 49 · see citing works

A qualitative and quantitative investigation of effects of indoor built environment for people with dementia in care and attention homes

Leung, M., Wang, C., & Chan, I. Y. S. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.04.019 · Cited by 47 · see citing works

Understanding facility management decision making for energy efficiency efforts for buildings at a higher education institution

Kim, A. A., Sunitiyoso, Y., & Medal, L. A. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.06.044 · Cited by 45 · see citing works

A modified approach to metabolic rate determination for thermal comfort prediction during high metabolic rate activities

Fletcher, M., Glew, D., Hardy, A., & Gorse, C. A. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107302 · Cited by 45 · see citing works

Indirect calorimetry of metabolic rate in college-age Japanese subjects during various office activities

Nomoto, A., Hisayama, R., Yoda, S., Akimoto, M., OGATA, M., Tsutsumi, H., & Tanabe, S. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107909 · Cited by 43 · see citing works

Application of smart wearable sensors in office buildings for modelling of occupants’ metabolic responses

Nižetić, S., Pivac, N., Zanki, V., & Papadopoulos, A. M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110399 · Cited by 40 · see citing works

Sweat Gland‐Like Fabric for Personal Thermal‐Wet Comfort Management

Zhang, Q., Wang, M., Chen, T., Chen, Z., Liu, D., Zhang, Z., Lei, Z., Wang, Y., Xiao, X., Zhu, B., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202409807 · Cited by 39 · see citing works

Occupant's thermal comfort expectations in naturally ventilated engineering workshop building: A case study at high metabolic rates

Kumar, S., Singh, M. K., Mathur, A., & Košir, M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.109970 · Cited by 38 · see citing works

Spatial interpolation-based analysis method targeting visualization of the indoor thermal environment

Yu, Z., Song, Y., Song, D., & Liu, Y. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107484 · Cited by 38 · see citing works

Thermal Comfort Model for HVAC Buildings Using Machine Learning

Fayyaz, M., Farhan, A. A., & Javed, A. R. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06156-8 · Cited by 38 · see citing works

Estimating the metabolic rate and associated physiological response for Indian subjects through climate chamber experiments

Anand, V. R., sendhil, D., & Rajasekar, E. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108466 · Cited by 38 · see citing works

Indoor air quality and passenger thermal comfort in Beijing metro transfer stations

Wu, L.-M., Xia, H., Wang, X., Dong, Q., Lin, C., Liu, X., & Liang, R. (2020) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2019.102217 · Cited by 35 · see citing works

Adaptive thermal comfort in the different buildings of Darjeeling Hills in eastern India – Effect of difference in elevation

Thapa, S., Bansal, A. K., Panda, G. K., & Indraganti, M. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.05.058 · Cited by 34 · see citing works

Metabolic rate in children and adolescents: Tabulate values for common activities and comparisons with standards and adult values

Gao, S., Yang, L., Li, Y., Liu, S., Zhang, H. E., Arens, E. A., & Zhai, Y. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110804 · Cited by 33 · see citing works

A review on different methodologies to study thermal comfort

Das, S., & Subudhi, S. (2021) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-021-03210-8 · Cited by 33 · see citing works

Evaluation of the eddy viscosity turbulence models for the simulation of convection–radiation coupled heat transfer in indoor environment

Li, X., & Tu, J. Y. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.11.043 · Cited by 32 · see citing works

Heating behavior using household air-conditioners during the COVID-19 lockdown in Wuhan: An exploratory and comparative study

Yan, L. H., Li, J., Liu, M., Hu, M., Xu, Z., & Xue, K. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107731 · Cited by 32 · see citing works

Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence

Fabbri, K., Gaspari, J., & Vandi, L. (2019) · Sustainability · doi:10.3390/su11236664 · Cited by 31 · see citing works

Modification and verification of the PMV model to improve thermal comfort prediction at low pressure

Zhou, B., Huang, Y., Nie, J., Ding, L., Sun, C., & Chen, B. (2023) · Journal of Thermal Biology · doi:10.1016/j.jtherbio.2023.103722 · Cited by 30 · see citing works

Analysis of the environmental thermal comfort conditions in public squares in the semiarid region of northeastern Brazil

Marçal, N. A., da Silva, R. M., Santos, C. A. G., & dos Santos, J. S. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.02.016 · Cited by 31 · see citing works

Thermal comfort in school children: Testing the validity of the Fanger method for a Mediterranean climate

Almagro-Lidón, M., Carramiñana, C. P., Galiano-Garrigós, A., & Emmitt, S. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111305 · Cited by 28 · see citing works

A review of investigation of the metabolic rate effects on human thermal comfort

Zhang, Y., Lin, Z., Zheng, Z., Zhang, S., & Fang, Z. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114300 · Cited by 27 · see citing works

A mixed-method systematic review of indoor environmental quality (IEQ) gaps and opportunities for future research

Nunayon, S. S., & Zhong, L. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106535 · Cited by 31 · see citing works

Evaluating thermal comfort and neutral temperature in residential apartments in hot and dry climate: A case study in Shiraz, Iran

Abdollahzadeh, S. M., Heidari, S., & Einifar, A. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107161 · Cited by 27 · see citing works

Occupant-centered indoor environmental quality management: Physiological response measuring methods

Kong, M., An, J., Jung, D., & Hong, T. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110661 · Cited by 27 · see citing works

Understanding thermal comfort expectations in older adults: The role of long-term thermal history

Hassani, A., Jancewicz, B., Wrotek, M., Chwałczyk, F., & Castell, N. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111900 · Cited by 26 · see citing works

Investigation of thermal comfort and preferred temperatures among rural elderly in Weihai, China: Considering metabolic rate effects

Zhang, J., Lu, J., Deng, W., Beccarelli, P., & Lun, I. Y.-F. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110940 · Cited by 26 · see citing works

Acceptable temperature steps for occupants moving between air-conditioned main space and naturally ventilated transitional space of building

Lu, P., & Li, J. H. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107150 · Cited by 26 · see citing works

Examination of human interaction on indoor environmental quality variables: A case study of libraries at the University of Alberta

Shum, C., Alipouri, Y., & Zhong, L. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108476 · Cited by 27 · see citing works

Passenger thermal comfort in the whole departure process of high-speed railway stations: A case study with thermal experience and metabolic rate changes in summer

Yuan, Y., Han, Y., Chen, H., Song, C., & Liu, G. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113105 · Cited by 25 · see citing works

Extended predicted mean vote of thermal adaptations reinforced around thermal neutrality

Zhang, S., & Lin, Z. (2021) · Indoor Air · doi:10.1111/ina.12792 · Cited by 24 · see citing works

Study on adaptive thermal comfort model and behavioral adaptation in naturally ventilated residential buildings, Jimma Town, Ethiopia

Yadeta, C., Indraganti, M., Tucho, G. T., & Alemayehu, E. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113483 · Cited by 24 · see citing works

Metabolic rate estimation method using image deep learning

Na, H., Choi, H., & Kim, T. (2020) · Building Simulation · doi:10.1007/s12273-020-0707-1 · Cited by 24 · see citing works

Extreme learning machine evolved by fuzzified hunger games search for energy and individual thermal comfort optimization

Wang, L., Khishe, M., Mohammadi, M., & Mahmoodzadeh, A. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105187 · Cited by 23 · see citing works

Experimental study on human comfort responses after simulated summer commutes with double transients of temperature and metabolic rate

Bai, Y. P., Zhang, Y., Wei, Z., & Wang, Y. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109253 · Cited by 22 · see citing works

Systematic review: Acute thermal effects of artificial light in the daytime

Wang, N., Wang, J., & Feng, Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112601 · Cited by 22 · see citing works

Data-driven thermal preference prediction model with embodied air-conditioning sensors and historical usage behaviors

Luo, M., Jiang, K., Wang, J., Feng, W., Ma, L., Shi, X., & Zhou, X. J. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109269 · Cited by 22 · see citing works

A systematic review of the influence of physiological factors on outdoor thermal comfort

Luo, M., Guo, F., Tang, H., Ming, R., Huang, L., & Zhao, H. (2025) · Human Settlements and Sustainability · doi:10.1016/j.hssust.2025.02.001 · Cited by 22 · see citing works

Thermal comfort in maternity wards: Summer vs. winter conditions

Rus, T., Cruciat, G., Nemeti, G. I., Mare, R., & Mureșan, D. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104356 · Cited by 20 · see citing works

Machine learning-based prediction of indoor thermal comfort in traditional Chinese dwellings: A case study of Hankou Lifen

Hui, X., Wang, B., & Hou, W. (2024) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2024.105048 · Cited by 20 · see citing works

Occupants’ subjective perceptions in three activity types to naturally ventilated & air conditioned room of mixed-function office building

Li, J. H., Lu, P., Zheng, H., & Wang, W. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.05.035 · Cited by 20 · see citing works

Analysis of thermal plumes forming over male human subjects

Bogdan, M., Ogłodziński, K., & Szyłak-Szydłowski, M. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103596 · Cited by 19 · see citing works

Indoor thermal comfort and ageing: A systematic review

Tang, Y., Yu, H., Mao, H., Zhang, K., & Wang, M. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111714 · Cited by 19 · see citing works

Field questionnaire survey on thermal comfort of medical personnel in operating rooms for hospitals in Nanjing

Wang, A., Wang, S., Fan, P., Xia, J., Ren, H., Tan, M.-L., & Zhou, B. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115423 · Cited by 18 · see citing works

Optimizing thermal comfort and energy efficiency in hospitals with PCM-Enhanced wall systems

Cui, H., Zhang, L., Yang, H., & Shi, Y. J. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114740 · Cited by 18 · see citing works

Nanotechnology for thermal comfort and energy efficiency in educational buildings with a simulation and measurement approach in BSh climate

Soleymani, M., Amrollahi, R., Taghdir, S., & Barzegar, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-72853-7 · Cited by 18 · see citing works

A review of thermal comfort of 4- to 14-year-old children via field experiments: Experimental designs and methods

Gao, S., Oh, W., Lin, C., & Feng, C. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114687 · Cited by 17 · see citing works

Development of data-driven thermal sensation prediction model using quality-controlled databases

Zhou, X. J., Xu, L., Zhang, J., Ma, L., Zhang, M., & Luo, M. (2022) · Building Simulation · doi:10.1007/s12273-022-0911-2 · Cited by 17 · see citing works

Effects of different metabolic rates on thermal comfort in a warmer thermal environment

Fang, Z., Zhang, P., Wu, P., Li, L., & Zheng, Z. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.108030 · Cited by 17 · see citing works

Novel Integrated and Optimal Control of Indoor Environmental Devices for Thermal Comfort Using Double Deep Q-Network

Kim, S., Yoon, Y.-R., Kim, J., & Moon, H. J. (2021) · Atmosphere · doi:10.3390/atmos12050629 · Cited by 16 · see citing works

Evaluation of Indoor Thermal Comfort Conditions of Residential Traditional and Modern Buildings in a Warm-Humid Climate

Okafor, M. U., Awuzie, B. O., Otasowie, K., Marcel-Okafor, U., & Aigbavboa, C. O. (2022) · Sustainability · doi:10.3390/su141912138 · Cited by 16 · see citing works

Accuracy Analysis of DNN-Based Pose-Categorization Model and Activity-Decision Algorithm

Park, B. R., Choi, E. J., Choi, Y. J., & Moon, J. W. (2020) · Energies · doi:10.3390/en13040839 · Cited by 16 · see citing works

Influencing factors of metabolic rates and their implications in thermal comfort research

Zhao, S., Yang, L., Gao, S., Yan, H., Zhang, H., Arens, E. A., Liu, S., & Zhai, Y. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115777 · Cited by 16 · see citing works

Study on winter thermal comfort of membrane structure gymnasium in severe cold region of China

Bai, Y., Zhao, L., Tang, R., & Kang, X. (2021) · Science and Technology for the Built Environment · doi:10.1080/23744731.2021.2018874 · Cited by 15 · see citing works

An investigation of college students on metabolic rate considering activity intensity, ambient temperature and clothing insulation

Liu, X., Liu, S., Zhang, H., Ye, T., Na, Y., & Zheng, W. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110865 · Cited by 14 · see citing works

Influence of Sun Shading Devices on Energy Efficiency, Thermal Comfort and Lighting Comfort in a Warm Semi-Arid Dry Mediterranean Climate

Carramiñana, C. P., Avilés, Á. B. G., Cabanes, N. C., & Galiano-Garrigós, A. (2024) · Buildings · doi:10.3390/buildings14020556 · Cited by 15 · see citing works

Measurement of steady state and dynamic metabolic rates under typical daily activities

Jia, X., Li, S., Wang, J., Zhu, Y., Du, Z., Zhang, X., & Cao, B. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111344 · Cited by 15 · see citing works

Effects of surface radiation on gaseous contaminants emission and dispersion in indoor environment – A numerical study

Li, X., Yan, Y., & Tu, J. Y. (2018) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2018.11.102 · Cited by 15 · see citing works

Developing occupant-centric smart home thermostats with energy-saving and comfort-improving goals

Luo, M., Zheng, Q., Zhao, Y., Zhao, F., & Zhou, X. J. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113579 · Cited by 14 · see citing works

Effects of indoor lighting conditions on subjective preferences of task lighting and room aesthetics in an Indian tertiary educational institution

Roy, S., Satvaya, P., & Bhattacharya, S. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.111119 · Cited by 14 · see citing works

Physiology or Psychology: What Drives Human Emissions of Carbon Dioxide and Ammonia?

Shen, Y., Bekö, G., Wargocki, P. L., Zhang, M., Merizak, M., Nenes, A., Williams, J., & Licina, D. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c07659 · Cited by 12 · see citing works

A review of investigation of the metabolic rate effects on human thermal comfort

Zhang, Y., Lin, Z., Zheng, Z., Zhang, S., & Fang, Z. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114300 · Cited by 27 · see citing works

Influencing factors of metabolic rates and their implications in thermal comfort research

Zhao, S., Yang, L., Gao, S., Yan, H., Zhang, H., Arens, E. A., Liu, S., & Zhai, Y. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115777 · Cited by 16 · see citing works

Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations

Lee, J., & Ham, Y. (2020) · Building Research & Information · doi:10.1080/09613218.2020.1840328 · Cited by 57 · see citing works

Accounting for Personalized Metabolic Rates on Building Energy Performance and Occupant Thermal Experience: Small Gains or Big Potential?

Younes, J., Sabit, F., & Khovalyg, D. (2026) · ASHRAE Winter Conference · doi:10.63044/w26kho006

Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate

Luo, M., Zhou, X. J., Zhu, Y., & Sundell, J. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.02.041 · Cited by 132 · see citing works