Investigating current trends in clothing insulation using a global thermal comfort database

Rupp, Ricardo Forgiarini; Kazanci, Ongun Berk; Toftum, Jørn

doi:10.1016/j.enbuild.2021.111431

Cited by 19 publications

(4 citation statements)

References 16 publications

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“…All participants wore standard clothes (underclothes, long-sleeved shirt, trousers, socks and sneakers), with a clothing thermal resistance of 1.0 clo. According to the MDPC thermal manikin test in the literature [37] , the clothing thermal resistance of MDPC group is about 3.0 clo [38] . The experimental procedure and the experimental site are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Research on the influence of indoor thermal environment and activity levels on thermal comfort in protective clothing

Yao

Cao

et al. 2023

Energy and Buildings

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Section: Methodsmentioning

confidence: 99%

Research on the influence of indoor thermal environment and activity levels on thermal comfort in protective clothing

Yao

Cao

et al. 2023

Energy and Buildings

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“…Yang et al, 2018b;Kong et al, 2019;Kwag et al, 2019;Deng and Chen, 2021;Zuo, Luo and Liu, 2021) and air velocity (Kalmár, 2018;Van Craenendonck et al, 2019;Sansaniwal et al, 2020). At the same time, the personall parameters are personal activity and clothing insulation levels (Chen, Moshfegh and Cehlin, 2013;Wang, Tian and Ding, 2013;Kwang Ho Lee and Schiavo, 2014;Song, Wang and Wei, 2016;Gautam et al, 2019;Wang, Kang and Zhou, 2019;Kim, Shin and Cho, 2021;Rupp, Kazanci and Toftum, 2021). It is suggested that PMV predictions can improve by considering chair and clothing insulation (Rupp, Kazanci and Toftum, 2021) and the effects of adding the age parameter to the thermal comfort investigations (Djamila, 2017).…”

Section: Thermal Comfort Parametersmentioning

confidence: 99%

“…One major issue in the PMV-PPD model is building ventilation, as this model overestimates occupant discomfort in naturally ventilated buildings . Some minor issues include other insulation factors, such as chair insulation, as suggested by (Rupp, Kazanci and Toftum, 2021), who have argued that this impacts the indoor environment classification according to the European standard EN 16798-1. It also has significant limitations in incorporating and adapting to the individual users' preferences that can be resolved using Bayesian Comfort Model (BCM) (Auffenberg, 2017).…”

Section: Thermal Comfort Parametersmentioning

confidence: 99%

Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

et al. 2023

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The building sector is one of the most resource-exhausting areas in global energy consumption. Maintaining good thermal comfort for occupants is the leading energy demand in buildings. The primary purpose of the current study is to identify the development of research areas on occupant comfort, pinpoint the gaps in knowledge and recommend directions for future studies. A scientometric analysis and a comprehensive systematic literature review are conducted using 792 sources. It is evident from the exponential increase in published papers that scholars are highly interested in this research topic. However, discrepancies remain between the two fundamental models of evaluating thermal comfort. There is a pressing need to balance thermal comfort while increasing energy efficiency. The foundation of achieving this balance can only be done by correctly evaluating the surrounding environment of occupants and understanding all the factors influencing human thermal comfort conditions. There is also a high potential in employing industry 4.0 technologies to assist in designing more innovative solutions for thermal comfort. Furthermore, there is a need for local thermal standards targeting specific regions. The lack of interoperability between BIM 3D modelling and energy simulation tools remains an obstacle.

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“…There are predictive models of clothing insulation that consider outdoor temperature, season, climate, indoor air temperature, indoor operative temperature, relative humidity [2][3][4]. Rupp et al [5] evaluated the clothing insulation collected in the ASHRAE database II [6] to predict garment insulation from the indoor air temperature, the season, and building ventilation type. Moreover, Wang et al [7] proposed a predictive model of clothing insulation for naturally ventilated buildings using the same ASHRAE database II.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats

et al. 2022

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Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting.

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Investigating current trends in clothing insulation using a global thermal comfort database

Cited by 19 publications

References 16 publications

Research on the influence of indoor thermal environment and activity levels on thermal comfort in protective clothing

Research on the influence of indoor thermal environment and activity levels on thermal comfort in protective clothing

Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats

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