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Intelligent models to predict the indoor thermal sensation and thermal demand in steady state based on occupants’ skin temperature
Abstract The correct prediction of thermal sensation is an important factor in energy consumption and satisfaction of occupants. This study examined the effectiveness of six different intelligent approaches for predicting thermal sensation and demand using body temperature data of 615 experiments with an exposure time of 3 h in a controlled office place. At each hour, the temperature of 14 uncovered body points was measured and finally, 1845 temperature data points were extracted. The exposure time had a significant effect on the thermal sensation and insufficient impact on the body temperature. Among all measured temperature data points, four points including middle of forehead (MFH), left cheek (LC), Nose (No), and left hand (LH), were taken as models' inputs. The results indicated that the Gaussian Process Regression (GPR) method offers the best outcomes in prediction of thermal sensation with mean absolute error (MAE) of 0.571 and R2 of 0.84 for the test data points. The MAE and R2 obtained by this model were 0.95 and 0.69, respectively, suggesting that GPR is more accurate and reliable than well-known method PMV. Regarding thermal demand, it was found that the accuracies of the GPR and PMV models were 86% and 69%, respectively. Therefore, the GPR approach is capable of predicting outstanding results for thermal demand compared to the existing models on the basis of environmental factors such as PMV Overall, the present study suggested that intelligent methods based on occupants’ physiological factors estimate the thermal sensation and demand better than available standard methods.
Highlights A total of 1845 valid data from 205 subject were collected in a controlled office. A correlation between skin temperature and thermal sensation and demand was found. Exposure time has effect on thermal sensation and an insufficient impact on body Temp. Physiological sensors lead to better results than that of environmental factors. Smart models by Four-point skin temperatures predict better results than PMV.
Intelligent models to predict the indoor thermal sensation and thermal demand in steady state based on occupants’ skin temperature
Abstract The correct prediction of thermal sensation is an important factor in energy consumption and satisfaction of occupants. This study examined the effectiveness of six different intelligent approaches for predicting thermal sensation and demand using body temperature data of 615 experiments with an exposure time of 3 h in a controlled office place. At each hour, the temperature of 14 uncovered body points was measured and finally, 1845 temperature data points were extracted. The exposure time had a significant effect on the thermal sensation and insufficient impact on the body temperature. Among all measured temperature data points, four points including middle of forehead (MFH), left cheek (LC), Nose (No), and left hand (LH), were taken as models' inputs. The results indicated that the Gaussian Process Regression (GPR) method offers the best outcomes in prediction of thermal sensation with mean absolute error (MAE) of 0.571 and R2 of 0.84 for the test data points. The MAE and R2 obtained by this model were 0.95 and 0.69, respectively, suggesting that GPR is more accurate and reliable than well-known method PMV. Regarding thermal demand, it was found that the accuracies of the GPR and PMV models were 86% and 69%, respectively. Therefore, the GPR approach is capable of predicting outstanding results for thermal demand compared to the existing models on the basis of environmental factors such as PMV Overall, the present study suggested that intelligent methods based on occupants’ physiological factors estimate the thermal sensation and demand better than available standard methods.
Highlights A total of 1845 valid data from 205 subject were collected in a controlled office. A correlation between skin temperature and thermal sensation and demand was found. Exposure time has effect on thermal sensation and an insufficient impact on body Temp. Physiological sensors lead to better results than that of environmental factors. Smart models by Four-point skin temperatures predict better results than PMV.
Intelligent models to predict the indoor thermal sensation and thermal demand in steady state based on occupants’ skin temperature
Salehi, Behrouz (author) / Ghanbaran, Abdul Hamid (author) / Maerefat, Mehdi (author)
Building and Environment ; 169
2019-11-28
Article (Journal)
Electronic Resource
English
Prediction of whole-body thermal sensation in the non-steady state based on skin temperature
| British Library Online Contents | 2013
Prediction of whole-body thermal sensation in the non-steady state based on skin temperature
| Online Contents | 2013