Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and  Thermal Comfort

Schiavon, Stefano

doi:10.3390/en7041917

Cited by 25 publications

(12 citation statements)

References 10 publications

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“…For equation (4) R 2 adj is equal to 0.19 and for equation (5) is 0.22. The implications of these two models on energy use, HVAC sizing and thermal comfort evaluation is described in [26]. Indoor and outdoor climate variables explain only a small part of human clothing behavior, nonetheless, the predictive models allow more precise thermal comfort calculation, energy simulation, HVAC sizing and building operation than previous practice of keeping the clothing insulation values equal to 0.5 in the cooling season and 1 in the heating season [26].…”

Section: Multivariable Mixed Modelmentioning

confidence: 99%

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

Schiavon¹,

Lee

2013

Building and Environment

Self Cite

171

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The results show that the median clothing insulation is 0.59 clo (0.50 clo (n=3,384) in summer and 0.69 clo (n=2,949) in winter). The median winter clothing insulation value is significantly smaller than the value suggested in the international standards (1.0 clo). The California data (n= 2,950) shows that occupants dress equally in naturally and mechanically conditioned buildings and all the data has female and male dressing with quite similar clothing insulation levels. Clothing insulation is correlated with outdoor air (r = 0.45) and indoor operative (r=0.3) temperatures, and relative humidity (r=0.26) An index to predict the presence of a dress code is developed.Two multivariable linear mixed models were developed. In the first one clothing is a function of outdoor air temperature measured at 6 o'clock, and the second one adds the influence of indoor operative temperature. The models were able to predict 19 and 22% of the total variance, respectively. Climate variables explain only a small part of human clothing behavior; nonetheless, the predictive models allow more precise thermal comfort calculation, energy simulation, HVAC sizing and building operation than previous practice of keeping the clothing insulation values equal to 0.5 in the cooling season and 1 in the heating season. Highlights• We developed two models to dynamically predict clothing insulation levels, R 2 adj =0.19-0.22 • Winter clothing insulation values are significantly lower than 1.0 clo • In California occupants dress equally in naturally and mechanically conditioned • Clothing values are correlated with outdoor air and indoor operative temperatures • Climate variables explain only a small part of human clothing behavior KEYWORDS Clothing, behavior modeling, thermal comfort, dress code, occupant behavior, indoor climate INTRODUCTIONThe amount of thermal insulation worn by a person has a substantial impact on thermal comfort [1]. Clothing adjustment is a behaviour that directly affects the heat-balance. The thermal insulation provided by garments and clothing ensembles is expressed in a unit named clo, where 1 clo is equal to 0.155 m 2 K/W. For near-sedentary activities where the metabolic rate is approximately 1.2 met, the effect of changing clothing insulation on the optimum operative temperature is approximately 6°C per clo. For example, adding a thin, long-sleeve sweater to a clothing ensemble increases clothing insulation by approximately 0.25 clo. Adding this insulation would lower the optimum operative temperature by approximately 6°C/clo × 0.25 clo = 1.5°C [1]. Clothing adjustment is perhaps the most important of all the thermal comfort adjustments available to occupants in office buildings [2].

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Section: Multivariable Mixed Modelmentioning

confidence: 99%

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

Schiavon¹,

Lee

2013

Building and Environment

Self Cite

171

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“…In the case of the multi-node thermoregulation model, the sensible heat loss from the skin of each part of the body is calculated using Equation (5). The clo level can then be calculated using that equation.…”

Section: Multi-node Human Thermoregulation Modelmentioning

confidence: 99%

“…Newsham [4] reported that increasing the clo-level flexibility in an office can enhance the comfort of occupants and significantly reduce the building energy consumption. Lee and Schiavon [5] reported that fixing the clo level according to a predicted mean vote (PMV), a representative thermal comfort index, in a PMV-based HVAC control in summer and winter caused poor predictability of the room temperature and energy consumption, and they suggested the need for a dynamic clothing model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

Lee¹,

Choi

Kim

et al. 2020

Atmosphere

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For evaluating the thermal comfort of occupants, human factors such as clothing thermal insulation (clo level) and metabolic rate (Met) are one of the important parameters as well as environmental factors such as air temperature (Ta) and humidity. In general, a fixed clo level is commonly used for controlling heating, ventilation, and air conditioning using the thermal comfort index. However, a fixed clo level can lead to errors for estimating the thermal comfort of occupants, because clo levels of occupants can vary with time and by season. The present study assesses a method for predicting the clo level of occupants using a thermoregulation model and an infrared (IR) camera. The Tanabe model and the Fanger model were used as the thermoregulation models, and the predicted performance for high clo level (winter clothing) was compared. The skin and clothing temperatures of eight subjects using a non-contact IR camera were measured in a climate chamber. In addition, the measured values were used for the thermoregulation models to predict the clo levels. As a result, the Tanabe model showed a better performance than the Fanger model for predicting clo levels. In addition, all models tended to predict a clo level higher than the traditional method.

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“…From another point of view, in a different study done by Lee et al [2014], the impact of three newly developed dynamic clothing insulation models on the building simulation is quantitatively assessed using the detailed whole-building energy simulation program, EnergyPlus, version 6.0. The limitations on this particular study were that the new clothing insulation models on energy and comfort was performed only for one particular climate (Chicago, IL, USA) and one particular system type (conventional forced air system equipped with variable speed central AHU and VAV box with reheat coil in each zone).…”

Section: Related Workmentioning

confidence: 99%

X3D sensor-based thermal maps for residential and commercial buildings

Hamza-Lup

Borza

Dragut

et al. 2015

Proceedings of the 20th International Conference on 3D Web Technology

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There are many building energy simulation systems on the market today; however, most of them rely on theoretical thermal models to make decisions on the building structural design and modifications. Sustainable methods of construction have made tremendous progress in the recent decades. The example of the German Energy-Plus-House technology uses a combination of (almost) zero-carbon passive heating technologies. A webenabled X3D simulation system coupled with a cost-effective set of temperature/humidity sensors can provide valuable insights into building design, materials and construction that can lead to significant energy savings, an improved thermal comfort for residents and improved efficiency. We propose a cost effective hardware-software prototype system that can provide real data driven 3D thermal maps for residential buildings. The system can easily scale to provide 3D thermal maps for large commercial buildings.

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Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and Thermal Comfort

Cited by 25 publications

References 10 publications

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

X3D sensor-based thermal maps for residential and commercial buildings

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