Abstract
Artificial neural network (ANN) was utilized to predict the thermal insulation values of children’s school wear in Kuwait. The input thermal insulation data of the different children’s school wear used in Kuwait classrooms were obtained from study using thermal manikins. The lowest mean squared error (MSE) value for the validation data was 1.5 × 10−5 using one hidden layer of six neurons and one output layer. The R2 values for the training, validation, and testing data were almost equal to 1. The values from ANN prediction were compared with McCullough’s equation and the standard tables’ methods. Results suggested that the ANN is able to give more accurate prediction of the clothing thermal insulation values than the regression equation and the standard tables methods. The effect of the different input variables on the thermal insulation value was examined using Garson algorithm and sensitivity analysis and it was found that the cloths weight, the body surface area nude (BSA0), and body surface area covered by one layer of clothing (BSAC1) have the highest effect on the thermal insulation value with about 29%, 27%, and 23%, respectively.
Highlights
IntroductionConducting thermal comfort field experiments is a moneyand time-consuming process due to the need of sophisticated equipment to measure the environmental factors (ambient air temperature, humidity, mean radiant temperature, and air speed) and personal factors (activity level and clothing insulation)
Conducting thermal comfort field experiments is a moneyand time-consuming process due to the need of sophisticated equipment to measure the environmental factors and personal factors
The three methods were compared together and the results of this study suggested that the clothing insulation values found from the measured and adapted data were similar to the adult’s data in standards tables for the same summer and winter seasons ensembles
Summary
Conducting thermal comfort field experiments is a moneyand time-consuming process due to the need of sophisticated equipment to measure the environmental factors (ambient air temperature, humidity, mean radiant temperature, and air speed) and personal factors (activity level and clothing insulation). Assessing the thermal insulation values of these clothing ensembles can be done using adult-size thermal manikins which is not always available and costly This problem became more complicated for children’s clothing due to the absences of the children-size thermal manikin. To cover this lack and as an alternative method the applicability of the neural network techniques was investigated to predict the thermal insulation of the children’s clothing. This investigation can support the use of the neural network techniques in the thermal comfort, HVAC, and indoor air quality fields especially in built environment area
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