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A platform for research: civil engineering, architecture and urbanism
Estimation of mean radiant temperature across diverse outdoor spaces: A comparative study of different modeling approaches
https://orcid.org/0000-0002-2320-1206
https://orcid.org/0000-0002-2498-0934
Highlights Five methods for estimating the mean radiant temperature () in compact cities are compared. Data at 670 locations in Hong Kong and 28 locations in Singapore are used for model development and evaluation. The Globe thermometer method and the SOLWEIG model underestimate . The DNN models generate high estimation accuracy in Hong Kong and Singapore.
Abstract The mean radiant temperature () is an important environmental parameter that affects the thermal comfort of human beings. However, both the measurement and estimation of have some inherent challenges. This study evaluated the performance of five methods in estimating , using data at 670 locations across 14 representative urban forms in Hong Kong. The evaluated methods include the customized globe thermometer method, recalibrated globe thermometer method, SOLWEIG simulation method, regression model, and neural network model. Values calculated from the integral radiation method were used as references for comparison. Results indicate that the customized and recalibrated globe thermometer methods and the SOLWEIG model consistently underestimate throughout most of the day, with substantial errors observed at low sun elevations and sunlit sites. The regression model provides a moderate fit to the data. The deep neural network (DNN) model yields the highest estimation accuracy, with an R2 of 0.878 and a root mean square error (RMSE) of 1.92 °C. To assess the generalizability of the DNN model, an additional dataset from Singapore is employed, including hourly meteorological data from 28 measurement stations over a two-year period. The DNN model demonstrates strong consistency between modeled and the reference across most of the sites, affirming its effectiveness in estimating in complex urban environments.
Estimation of mean radiant temperature across diverse outdoor spaces: A comparative study of different modeling approaches
https://orcid.org/0000-0002-2320-1206
https://orcid.org/0000-0002-2498-0934
Highlights Five methods for estimating the mean radiant temperature () in compact cities are compared. Data at 670 locations in Hong Kong and 28 locations in Singapore are used for model development and evaluation. The Globe thermometer method and the SOLWEIG model underestimate . The DNN models generate high estimation accuracy in Hong Kong and Singapore.
Abstract The mean radiant temperature () is an important environmental parameter that affects the thermal comfort of human beings. However, both the measurement and estimation of have some inherent challenges. This study evaluated the performance of five methods in estimating , using data at 670 locations across 14 representative urban forms in Hong Kong. The evaluated methods include the customized globe thermometer method, recalibrated globe thermometer method, SOLWEIG simulation method, regression model, and neural network model. Values calculated from the integral radiation method were used as references for comparison. Results indicate that the customized and recalibrated globe thermometer methods and the SOLWEIG model consistently underestimate throughout most of the day, with substantial errors observed at low sun elevations and sunlit sites. The regression model provides a moderate fit to the data. The deep neural network (DNN) model yields the highest estimation accuracy, with an R2 of 0.878 and a root mean square error (RMSE) of 1.92 °C. To assess the generalizability of the DNN model, an additional dataset from Singapore is employed, including hourly meteorological data from 28 measurement stations over a two-year period. The DNN model demonstrates strong consistency between modeled and the reference across most of the sites, affirming its effectiveness in estimating in complex urban environments.
Estimation of mean radiant temperature across diverse outdoor spaces: A comparative study of different modeling approaches
https://orcid.org/0000-0002-2320-1206
https://orcid.org/0000-0002-2498-0934
Highlights Five methods for estimating the mean radiant temperature () in compact cities are compared. Data at 670 locations in Hong Kong and 28 locations in Singapore are used for model development and evaluation. The Globe thermometer method and the SOLWEIG model underestimate . The DNN models generate high estimation accuracy in Hong Kong and Singapore.
Abstract The mean radiant temperature () is an important environmental parameter that affects the thermal comfort of human beings. However, both the measurement and estimation of have some inherent challenges. This study evaluated the performance of five methods in estimating , using data at 670 locations across 14 representative urban forms in Hong Kong. The evaluated methods include the customized globe thermometer method, recalibrated globe thermometer method, SOLWEIG simulation method, regression model, and neural network model. Values calculated from the integral radiation method were used as references for comparison. Results indicate that the customized and recalibrated globe thermometer methods and the SOLWEIG model consistently underestimate throughout most of the day, with substantial errors observed at low sun elevations and sunlit sites. The regression model provides a moderate fit to the data. The deep neural network (DNN) model yields the highest estimation accuracy, with an R2 of 0.878 and a root mean square error (RMSE) of 1.92 °C. To assess the generalizability of the DNN model, an additional dataset from Singapore is employed, including hourly meteorological data from 28 measurement stations over a two-year period. The DNN model demonstrates strong consistency between modeled and the reference across most of the sites, affirming its effectiveness in estimating in complex urban environments.
Estimation of mean radiant temperature across diverse outdoor spaces: A comparative study of different modeling approaches
Jia, Siqi (author) / Wang, Yuhong (author) / Hien Wong, Nyuk (author) / Liang Tan, Chun (author) / Chen, Shisheng (author) / Weng, Qihao (author) / Ming Mak, Cheuk (author)
Energy and Buildings ; 310
2024-03-06
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2017
| British Library Online Contents | 2017